Dr Haiyan (Helen) LU is an Associate Professor in the School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney (UTS).
Dr Lu received her Bachelor and Master Degrees of Engineering from the Harbin University of Technology, China in 1985 and 1988 respectively.
She was a lecturer in the Yanshan University China before she moved to Sydney Australia in 1994. She completed her Ph.D. degree in engineering at UTS in Australia in 2002.
She is a core member of the Decision Systems & e-Service Intelligence Lab in the Australian Artificial Intelligence Institute, UTS.
- A senior member of IEEE
- A member of ACM
- A member of IEEE Computational Intelligence Society
- Vice Chair of IEEE NSW Section Women in Engineering Affinity Group
- Secretary and Treasurer of IEEE NSW Computational Intelligence Society Chapter
Can supervise: YES
- Computational intelligence: evolutionary computing, genetic algorithm and swarm intelligence
- Learning algorithms: active learning, deep learning and transfer learning
- Knowledge representation: ontology, semantics
- Decision theories
- Forecasting theores
- Magnetic materials and design of electromagnetic devices
- Intelligent multi-agent systems and multi-robot control systems
- Smart SaaS cloud
- Smart power grid
- Smart learning systems for classification problems
- Recommendation systems
- Prediction and forecasting of time series (power demand, electricity price, wind speed etc)
- Modelling and numerical simulation of electromagnetic devices
- Database Principles / Fundamentals
- Computer Graphics
- Research Projects
- Technology Research Preparation
- Artificial Intelligence
© 2019, Springer-Verlag London Ltd., part of Springer Nature. The emerging complex circumstances caused by economy, technology, and government policy and the requirement of low-carbon development of power grid lead to many challenges in the power system coordination and operation. However, the real-time scheduling of electricity generation needs accurate modeling of electricity demand forecasting for a range of lead times. In order to better capture the nonlinear and non-stationary characteristics and the seasonal cycles of future electricity demand data, a new concept of the integrated model is developed and successfully applied to research the forecast of electricity demand in this paper. The proposed model combines adaptive Fourier decomposition method, a new signal preprocessing technology, for extracting useful element from the original electricity demand series through filtering the noise factors. Considering the seasonal term existing in the decomposed series, it should be eliminated through the seasonal adjustment method, in which the seasonal indexes are calculated and should multiply the forecasts back to restore the final forecast. Besides, a newly proposed moth-flame optimization algorithm is used to ensure the suitable parameters of the least square support vector machine which can generate the forecasts. Finally, the case studies of Australia demonstrated the efficacy and feasibility of the proposed integrated model. Simultaneously, it can provide a better concept of modeling for electricity demand prediction over different forecasting horizons.
Niu, T, Wang, J, Lu, H, Yang, W & Du, P 2020, 'Developing a deep learning framework with two-stage feature selection for multivariate financial time series forecasting', EXPERT SYSTEMS WITH APPLICATIONS, vol. 148.View/Download from: Publisher's site
Wang, J, Li, H, Lu, H, Yang, H & Wang, C 2020, 'Integrating offline logistics and online system to recycle e-bicycle battery in China', Journal of Cleaner Production, vol. 247.View/Download from: Publisher's site
© 2019 Elsevier Ltd E-bicycles are powered by batteries including lithium-ion, lead–acid, and others. The reuse of waste batteries shows promise for grid-scale storage. The New National Standard for e-bicycles is to be introduced in China, that might result in the country becoming the largest source of battery waste in the world. If the waste batteries are not recycled appropriately, it will cause significant heavy metal pollution, which will in turn, pose a serious threat to the ecological environment and human health. This paper discusses the current status of recycling of e-bicycle batteries in China and reviews the current recycling approaches. We developed a waste e-bicycle battery recycling system based on “Internet+” to solve the dilemma of recycling end-of-life batteries; this system has three subsystems: offline reverse logistics recovery system, online network recycling system, and traceability management system. In particular, the participation of consumers and government, reward-penalty mechanism, “Internet +” development, and other strategies are considered to improve recycling systems throughout life cycle of the products. The proposed recycling system can increase the waste battery recycling rate by 2.59% under the reward-penalty mechanism, and reduce carbon dioxide emissions by 58%, which is conducive to promoting sustainable development.
Wang, J, Niu, T, Lu, HY, Yang, W & Du, P 2020, 'A Novel Framework of Reservoir Computing for Deterministic and Probabilistic Wind Power Forecasting', IEEE Transactions on Sustainable Energy, vol. 11, no. 1, pp. 337-349.View/Download from: Publisher's site
Song, Y, Lu, J, Lu, H & Zhang, G 2020, 'Fuzzy Clustering-Based Adaptive Regression for Drifting Data Streams', IEEE TRANSACTIONS ON FUZZY SYSTEMS, vol. 28, no. 3, pp. 544-557.View/Download from: Publisher's site
Jiang, P, Wang, B, Li, H & Lu, H 2019, 'Modeling for chaotic time series based on linear and nonlinear framework: Application to wind speed forecasting', Energy, vol. 173, pp. 468-482.View/Download from: Publisher's site
© 2019 Elsevier Ltd Wind-speed forecasting plays a crucial part in improving the operational efficiency of wind power generation. However, accurate forecasts are difficult owing to the uncertainty of the wind speed. Although numerous investigations of wind-speed forecasting have been performed, many of the previous studies used wind-speed data directly to make forecasts, which were rarely based on the structural characteristics of the data. Therefore, in this study, a hybrid linear-nonlinear modeling method based on the chaos theory was successfully employed to capture the linear and nonlinear factors hidden in chaotic time series. Before the forecast, the noise in the data was removed using a decomposition algorithm. Then, through the phase-space reconstruction, the one-dimensional time series were extended to the multi-dimensional space to determine the utilization form of the data. Finally, Holt's exponential smoothing based on the firefly optimization algorithm and support vector regression were combined to predict the wind speed. The experimental results show that the proposed model is not only better than the comparison models but also has great application potential in the wind power generation system.
Li, H, Wang, J, Li, R & Lu, H 2019, 'Novel analysis–forecast system based on multi-objective optimization for air quality index', Journal of Cleaner Production, vol. 208, pp. 1365-1383.View/Download from: Publisher's site
© 2018 Elsevier Ltd The air quality index (AQI) is an important indicator of air quality. Owing to the randomness and non-stationarity inherent in AQI, it is still a challenging task to establish a reasonable analysis–forecast system for AQI. Previous studies primarily focused on enhancing either forecasting accuracy or stability and failed to improve both aspects simultaneously, leading to unsatisfactory results. In this study, a novel analysis–forecast system is proposed that consists of complexity analysis, data preprocessing, and optimize–forecast modules and addresses the problems of air quality monitoring. The proposed system performs a complexity analysis of the original series based on sample entropy and data preprocessing using a novel feature selection model that integrates a decomposition technique and an optimization algorithm for removing noise and selecting the optimal input structure, and then forecasts hourly AQI series by utilizing a modified least squares support vector machine optimized by a multi-objective multi-verse optimization algorithm. Experiments based on datasets from eight major cities in China demonstrated that the proposed system can simultaneously obtain high accuracy and strong stability and is thus efficient and reliable for air quality monitoring.
Wang, J, Zhang, N & Lu, H 2019, 'A novel system based on neural networks with linear combination framework for wind speed forecasting', Energy Conversion and Management, vol. 181, pp. 425-442.View/Download from: Publisher's site
© 2018 Elsevier Ltd The absence of accurate and stable prediction of wind speed remains a major obstacle to the rational planning, scheduling, and maintenance of wind power generation. Currently, an extensive body of methods that aim to enhance the accuracy of wind speed prediction have been proposed. However, the majority of previous studies have tended to emphasize the structural improvement of individual forecasting models without considering the validity of data preprocessing. This can result in poor forecasting accuracy due to their failure to fully capture the effective information of the wind speed data. A new approach is proposed in this paper that successfully combines a data preprocessing technique with a linear combination method. Further, a new neural network framework is employed to determine the required combination weights to ensure improved prediction performance, thereby overcoming the drawback of the low accuracy of individual prediction models. Six wind speed datasets from Penglai are regarded as expository cases to analyze the forecasting validity and stability of the developed model. It can be concluded from the experiments that the combined forecasting system outperforms the individual models and the traditional linear combination models with higher accuracy and stronger stability.
Yang, W, Wang, J, Lu, H, Niu, T & Du, P 2019, 'Hybrid wind energy forecasting and analysis system based on divide and conquer scheme: A case study in China', Journal of Cleaner Production, vol. 222, pp. 942-959.View/Download from: Publisher's site
© 2019 Elsevier Ltd Wind energy, acknowledged as a promising form of renewable energy and the fastest-growing clean method for electricity generation, has attracted considerable attention from many scientists and researchers in recent decades. However, wind energy forecasting is still a challenging task owing to its inherent features of non-linearity and randomness. Therefore, this study develops a hybrid wind energy forecasting and analysis system including a deterministic forecasting module and an uncertainty analysis module to mitigate the challenges in existing studies. In particular, these challenges are as follows: (1) It is difficult to guarantee that the data characteristics underlying the time series are effectively extracted; (2) in the modeling of each subseries, i.e., when the original data is decomposed into some time series, forecasting accuracy and stability are not simultaneously considered, and thus, they are not properly modeled; and (3) the best function to perform a deterministic forecasting and uncertainty analysis based on the forecaster of each subseries is unknown. The developed hybrid system consists of three steps: First, data preprocessing is conducted to capture and mine the main feature of the wind energy time series and weaken the noises’ negative effects; second, multi-objective optimization is proposed to achieve the forecasting of each subseries with improvements in accuracy and stability; finally, search for the best function, which obtains the deterministic forecasting and uncertainty analysis results using an optimized extreme learning machine based on different modeling objectives, is conducted. Experimental simulations are performed using data from three sites in a real wind farm, which indicate that the developed system has a better performance in engineering applications than that of other methods. Furthermore, this system could not only be used as an effective tool for wind energy deterministic forecasting and uncertainty ...
Zhang, K, Qu, Z, Dong, Y, Lu, H, Leng, W, Wang, J & Zhang, W 2019, 'Research on a combined model based on linear and nonlinear features - A case study of wind speed forecasting', Renewable Energy, vol. 130, pp. 814-830.View/Download from: Publisher's site
© 2018 Elsevier Ltd As one of the most promising sustainable energy sources, wind energy is being paid more attention by the researchers. Because of the volatility and instability of wind speed series, wind power integration faces a severe challenge; thus, an accurate wind energy forecasting plays a key role in smart grid planning and management. However, many traditional forecasting models do not consider the necessity and importance of data preprocessing and neglect the limitation of using a single forecasting model, which leads to poor forecasting accuracy. To solve these problems, a novel combined model based on two linear and four nonlinear forecasting algorithms is proposed to adapt both the linear and nonlinear characteristics of the wind energy time series. In addition, a modified Artificial Fish Swarm Algorithm and Ant Colony Optimization (AFSA-ACO) algorithm is proposed and employed to determine the optimal weight coefficients of the combined models. To verify the forecasting performance of the developed combined model, several experiments were implemented by using 10-min interval wind speed data in Shandong, China. Then, one-step (10-min), three-step (30-min) and five-step (50-min) predictions were conducted. The experimental results indicate that the developed combined model is remarkably superior to all benchmark models for the high precision and stability of wind-speed predictions.
Zhang, Y, Wang, J & Lu, H 2019, 'Research and application of a novel combined model based on multiobjective optimization for multistep-ahead electric load forecasting', Energies, vol. 12, no. 10.View/Download from: Publisher's site
© 2019 by the authors. Accurate forecasting of electric loads has a great impact on actual power generation, power distribution, and tariff pricing. Therefore, in recent years, scholars all over the world have been proposing more forecasting models aimed at improving forecasting performance; however, many of them are conventional forecasting models which do not take the limitations of individual predicting models or data preprocessing into account, leading to poor forecasting accuracy. In this study, to overcome these drawbacks, a novel model combining a data preprocessing technique, forecasting algorithms and an advanced optimization algorithm is developed. Thirty-minute electrical load data from power stations in New South Wales and Queensland, Australia, are used as the testing data to estimate our proposed model's effectiveness. From experimental results, our proposed combined model shows absolute superiority in both forecasting accuracy and forecasting stability compared with other conventional forecasting models.
Islam, MR, Lu, H, Hossain, J & Li, L 2019, 'Mitigating unbalance using distributed network reconfiguration techniques in distributed power generation grids with services for electric vehicles: A review', Journal of Cleaner Production, vol. 239.View/Download from: Publisher's site
© 2019 Elsevier Ltd With rapid movement to combat climate change by reducing greenhouse gases, there is an increasing trend to use more electric vehicles (EVs) and renewable energy sources (RES). With more EVs integration into electricity grid, this raises many challenges for the distribution service operators (DSOs) to integrate such RES-based, distributed generation (DG) and EV-like distributed loads into distribution grids. Effective management of distribution network imbalance is one of the challenges. The distribution network reconfiguration (DNR) techniques are promising to address the issue of imbalance along with other techniques such as the optimal distributed generation placement and allocation (OPDGA) method. This paper presents a systematic and thorough review of DNR techniques for mitigating unbalance of distribution networks, based on papers published in peer-reviewed journals in the last three decades. It puts more focus on how the DNR techniques have been used to manage network imbalance due to distributed loads and DG units. To the best of our knowledge, this is the first attempt to review the research works in the field using DNR techniques to mitigate unbalanced distribution networks. Therefore, this paper will serve as a prime source of the guidance for mitigating network imbalance using the DNR techniques to the new researchers in this field.
Sarker, PC, Islam, MR, Guo, Y, Zhu, J & Lu, HY 2019, 'State-of-The-Art Technologies for Development of High Frequency Transformers with Advanced Magnetic Materials', IEEE Transactions on Applied Superconductivity, vol. 29, no. 2.View/Download from: Publisher's site
© 2002-2011 IEEE. With the development of advanced soft magnetic materials of high-saturation flux density and low specific core loss and semiconductor power devices, the high-frequency transformer (HFT) has received significant attention in recent years for its widespread emerging applications. The optimal design of high-power-density HFTs for high-performance energy conversion systems is, however, a multiphasic problem that needs special considerations on various aspects such as core material selection, minimization of parasitic components, and thermal management. This paper presents a comprehensive review on advancement of soft magnetic materials for high-power-density magnetic devices and advanced technologies for characterizations and optimal design of HFTs. The future research and development trends are also discussed.
Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies.
© 2018 Elsevier Ltd Wind speed forecasting is important for high-efficiency utilization of wind energy. Correspondingly, numerous researchers have always focused on the development of reliable forecasting models of wind speed, which is often noisy, unstable and irregular. Current approaches could adapt to various wind speed data. However, many of these usually ignore the importance of the selection of the modeling sample, which often results in poor forecasting performance. In this study, a hybrid forecasting system is proposed that contains three modules: data preprocessing, data clustering, and forecasting modules. In this system, the decomposing technique is applied to reduce the influence of noise within the raw data series to obtain a more stable sequence that is conducive to extract traits from the original data. To extract the characteristic of similarity within wind speed data, a kernel-based fuzzy c-means clustering algorithm is used in data clustering module. In the forecasting module, a sample with a highly similar fluctuation pattern is selected as training dataset, and which could reduce the training requirement of model to improve the forecasting accuracy. The experimental results indicate that the developed system outperforms the discussed traditional forecasting models with respect to forecasting accuracy.
Li, H, Wang, J, Lu, H & Guo, Z 2018, 'Research and application of a combined model based on variable weight for short term wind speed forecasting', RENEWABLE ENERGY, vol. 116, pp. 669-684.View/Download from: Publisher's site
Niu, T, Wang, J, Lu, H & Du, P 2018, 'Uncertainty modeling for chaotic time series based on optimal multi-input multi-output architecture: Application to offshore wind speed', Energy Conversion and Management, vol. 156, pp. 597-617.View/Download from: Publisher's site
© 2017 Elsevier Ltd Wind energy is attracting more attention with the growing demand for energy. However, the efficient development and utilization of wind energy are restricted due to the intermittency and randomness of wind speed. Although abundant investigations concerning wind speed forecasting have been conducted by numerous researchers, most of the studies merely attach importance to point forecasts, which cannot quantitatively characterize the uncertainties as developing intervals. In this study, a novel interval prediction architecture has been designed, aiming at constructing effective prediction intervals for a wind speed series, composed of a preprocessing module, a feature selection module, an optimization module, a forecast module and an evaluation module. The feature selection module, in cooperation with the preprocessing module, is developed to determine the optimal model input. Furthermore, the forecast module optimized by the optimization module is considered a predictor for giving prediction intervals. The experimental results shed light on the architecture that not only outperforms the benchmark models considered, but also has great potential for application to wind power systems.
Song, J, Wang, J & Lu, H 2018, 'A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting', Applied Energy, vol. 215, pp. 643-658.View/Download from: Publisher's site
© 2018 Elsevier Ltd Short-term wind speed forecasting has a significant influence on enhancing the operation efficiency and increasing the economic benefits of wind power generation systems. A substantial number of wind speed forecasting models, which are aimed at improving the forecasting performance, have been proposed. However, some conventional forecasting models do not consider the necessity and importance of data preprocessing. Moreover, they neglect the limitations of individual forecasting models, leading to poor forecasting accuracy. In this study, a novel model combining a data preprocessing technique, forecasting algorithms, an advanced optimization algorithm, and no negative constraint theory is developed. This combined model successfully overcomes some limitations of the individual forecasting models and effectively improves the forecasting accuracy. To estimate the effectiveness of the proposed combined model, 10-min wind speed data from the wind farm in Peng Lai, China are used as case studies. The experiment results demonstrate that the developed combined model is definitely superior compared to all other conventional models. Furthermore, it can be used as an effective technique for smart grid planning.
Wang, J, Du, P, Lu, H, Yang, W & Niu, T 2018, 'An improved grey model optimized by multi-objective ant lion optimization algorithm for annual electricity consumption forecasting', Applied Soft Computing Journal, vol. 72, pp. 321-337.View/Download from: Publisher's site
© 2018 Elsevier B.V. Accurate and stable annual electricity consumption forecasting play vital role in modern social and economic development through providing effective planning and guaranteeing a reliable supply of sustainable electricity. However, establishing a robust method to improve prediction accuracy and stability simultaneously of electricity consumption forecasting has been proven to be a highly challenging task. Most previous researches only pay more attention to enhance prediction accuracy, which usually ignore the significant of forecasting stability, despite its importance to the effectiveness of forecasting models. Considering the characteristics of annual power consumption data as well as one criterion i.e. accuracy or stability is insufficient, in this study a novel hybrid forecasting model based on an improved grey forecasting mode optimized by multi-objective ant lion optimization algorithm is successfully developed, which can not only be utilized to dynamic choose the best input training sets, but also obtain satisfactory forecasting results with high accuracy and strong ability. Case studies of annual power consumption datasets from several regions in China are utilized as illustrative examples to estimate the effectiveness and efficiency of the proposed hybrid forecasting model. Finally, experimental results indicated that the proposed forecasting model is superior to the comparison models.
Wang, J, Li, H & Lu, H 2018, 'Application of a novel early warning system based on fuzzy time series in urban air quality forecasting in China', Applied Soft Computing Journal, vol. 71, pp. 783-799.View/Download from: Publisher's site
© 2018 Elsevier B.V. With atmospheric environmental pollution becoming increasingly serious, developing an early warning system for air quality forecasting is vital to monitoring and controlling air quality. However, considering the large fluctuations in the concentration of pollutants, most previous studies have focused on enhancing accuracy, while few have addressed the stability and uncertainty analysis, which may lead to insufficient results. Therefore, a novel early warning system based on fuzzy time series was successfully developed that includes three modules: deterministic prediction module, uncertainty analysis module, and assessment module. In this system, a hybrid model combining the fuzzy time series forecasting technique and data reprocessing approaches was constructed to forecast the major air pollutants. Moreover, an uncertainty analysis was generated to further analyze and explore the uncertainties involved in future air quality forecasting. Finally, an assessment module proved the effectiveness of the developed model. The experimental results reveal that the proposed model outperforms the comparison models and baselines, and both the accuracy and the stability of the developed system are remarkable. Therefore, fuzzy logic is a better option in air quality forecasting and the developed system will be a useful tool for analyzing and monitoring air pollution.
Wang, J, Niu, T, Lu, H, Guo, Z, Yang, W & Du, P 2018, 'An analysis-forecast system for uncertainty modeling of wind speed: A case study of large-scale wind farms', Applied Energy, vol. 211, pp. 492-512.View/Download from: Publisher's site
© 2017 Elsevier Ltd The uncertainty analysis and modeling of wind speed, which has an essential influence on wind power systems, is consistently considered a challenging task. However, most investigations thus far were focused mainly on point forecasts, which in reality cannot facilitate quantitative characterization of the endogenous uncertainty involved. An analysis-forecast system that includes an analysis module and a forecast module and can provide appropriate scenarios for the dispatching and scheduling of a power system is devised in this study; this system superior to those presented in previous studies. In order to qualitatively and quantitatively investigate the uncertainty of wind speed, recurrence analysis techniques are effectively developed for application in the analysis module. Furthermore, in order to quantify the uncertainty accurately, a novel architecture aimed at uncertainty mining is devised for the forecast module, where a non-parametric model optimized by an improved multi-objective water cycle algorithm is considered a predictor for producing intervals for each mode component after feature selection. The results of extensive in-depth experiments show that the devised system is not only superior to the considered benchmark models, but also has good potential practical applications in wind power systems.
Heng, J, Wang, J, Xiao, L & Lu, H 2017, 'Research and application of a combined model based on frequent pattern growth algorithm and multi-objective optimization for solar radiation forecasting', Applied Energy, vol. 208, pp. 845-866.View/Download from: Publisher's site
© 2017 Elsevier Ltd Solar radiation forecasting plays a significant role in precisely designing solar energy systems and in the efficient management of solar energy plants. Most research only focuses on accuracy improvements; however, for an effective forecasting model, considering only accuracy or stability is inadequate. To solve this problem, a combined model based on nondominated sorting-based multiobjective bat algorithm (NSMOBA) is developed for the optimization of weight coefficients of each model to achieve high accuracy and stability results simultaneously. In addition, a statistical method and data mining-based approach are used to determine the input variables for constructing the combined model. Monthly average solar radiation and meteorological variables from six datasets in the U.S. collected for case studies were used to assess the comprehensive performance (both in accuracy and stability) of the proposed combined model. The simulation in four experiments demonstrated the following: (a) the proposed combined model is suita ble for providing accurate and stable solar radiation forecasting; (b) the combined model exhibits a more competitive forecasting performance than the individual models by using the advantage of each model; (c) the NSMOBA is an efficient algorithm for providing accurate forecasting results and improving the stability where the single bat algorithm is insufficient.
Wang, J, Zhang, X, Guo, Z & Lu, H 2017, 'Developing an early-warning system for air quality prediction and assessment of cities in China', Expert Systems with Applications, vol. 84, pp. 102-116.View/Download from: Publisher's site
© 2017 Elsevier Ltd Air quality has received continuous attention from both environmental managers and citizens. Accordingly, early-warning systems for air pollution are very useful tools to avoid negative health effects and develop effective prevention programs. However, developing robust early-warning systems is very challenging, as well as necessary. This paper develops a reliable and effective early-warning system that consists of air quality prediction and assessment modules. In the prediction module, a hybrid forecasting method is developed for predicting pollutant concentrations that effectively estimates future air quality conditions. In developing this proposed model, we suggest the use of a back propagation neural network algorithm, combined with a probabilistic parameter model and data preprocessing techniques, to address the uncertainties involved in future air quality prediction. Meanwhile, a pre-analysis is implemented, primarily by using optimized distribution functions to examine and analyze statistical characteristics and emission behaviors of air pollutants. The second method, which is developed as part of the second module, is based on fuzzy set theory and the Analytic Hierarchy Process, and it performs air quality assessments to provide a clear and intelligible description of air quality conditions. Using data from the Ministry of Environmental Protection of China and six stages of air quality classification levels, specifically good, moderate, lightly polluted, moderately polluted, heavily polluted and severely polluted, two cities in China, Chengdu and Hangzhou, are used as illustrative examples to verify the effectiveness of the developed early-warning system. The results demonstrate that the proposed methods are effective and reliable for use by environmental supervisors in air pollution monitoring and management.
Yang, H, Jiang, Z & Lu, H 2017, 'A hybridwind speed forecasting system based on a 'decomposition and ensemble' strategy and fuzzy time series', Energies, vol. 10, no. 9, pp. 1-30.View/Download from: Publisher's site
© 2017 by the authors. Licensee MDPI, Basel, Switzerland. Accurate and stable wind speed forecasting is of critical importance in the wind power industry and has measurable influence on power-system management and the stability of market economics. However, most traditional wind speed forecasting models require a large amount of historical data and face restrictions due to assumptions, such as normality postulates. Additionally, any data volatility leads to increased forecasting instability. Therefore, in this paper, a hybrid forecasting system, which combines the 'decomposition and ensemble' strategy and fuzzy time series forecasting algorithm, is proposed that comprises two modules-data pre-processing and forecasting. Moreover, the statistical model, artificial neural network, and Support Vector Regression model are employed to compare with the proposed hybrid system, which is proven to be very effective in forecasting wind speed data affected by noise and instability. The results of these comparisons demonstrate that the hybrid forecasting system can improve the forecasting accuracy and stability significantly, and supervised discretization methods outperform the unsupervised methods for fuzzy time series in most cases.
Wu, J, Wang, J, Qin, S & Lu, H 2016, 'Suitable error evaluation criteria selection in the wind energy assessment via the K-means clustering algorithm', International Journal of Green Energy, vol. 13, no. 11, pp. 1145-1162.View/Download from: Publisher's site
© 2016 Taylor & Francis Group, LLC. In this paper, wind energy potential of four locations in Xinjiang region is assessed. The Weibull distribution as well as the Logistic and the Lognormal distributions are applied to describe the distributions of the wind speed at different heights. In determining the parameters in the Weibull distribution, four intelligent parameter optimization approaches including the differential evolutionary, the particle swarm optimization, and two other approaches derived from these two algorithms and combined advantages of these two approaches are employed. Then the optimal distribution is chosen through the Chi-square error (CSE), the Kolmogorov–Smirnov test error (KSE), and the root mean square error (RMSE) criteria. However, it is found that the variation range of some criteria is quite large, thus these criteria are analyzed and evaluated both from the anomalous values and by the K-means clustering method. Anomaly observation results have shown that the CSE is the first one should be considered to be eliminated from the consequent optimal distribution function selection. This idea is further confirmed by the K-means clustering algorithm, by which the CSE is clustered into a different group with KSE and RMSE. Therefore, only the reserved two error evaluation criteria are utilized to evaluate the wind power potential.
Xiao, L, Shao, W, Wang, C, Zhang, K & Lu, H 2016, 'Research and application of a hybrid model based on multi-objective optimization for electrical load forecasting', APPLIED ENERGY, vol. 180, pp. 213-233.View/Download from: Publisher's site
Jiang, H, Wang, J, Dong, Y & Lu, H 2015, 'Comprehensive assessment of wind resources and the low-carbon economy: An empirical study in the Alxa and Xilin Gol Leagues of inner Mongolia, China', Renewable and Sustainable Energy Reviews, vol. 50, pp. 1304-1319.View/Download from: Publisher's site
© 2015 Elsevier Ltd. All rights reserved. Due to atmospheric pollution from fossil fuels, the reduction of wind turbine costs, and the rise of the low-carbon economy, wind energy conversion systems have become one of the most significant forms of new energy in China. Therefore, to reduce investment risk and maximize profits, it is necessary to assess wind resources before building large wind farms. This paper develops a comprehensive system containing four steps to evaluate the potential of wind resources at two sites in Xilin Gol League and at additional two sites in Alxa League of Inner Mongolia, China: (1) By calculating the total scores of three indexes, including the effective wind power density (EWPD), wind available time (WAT) and population density (PD), an indexes method is applied to assess the theoretical wind energy potential from 2001 to 2010. (2) To judge the fluctuations in the wind speed, the Fisher optimal partition method and the Jonckheere-Terpstra test are used to analyze the changes in the average monthly and yearly wind speeds from 2001 to 2010. (3) Three probability density functions, i.e., Weibull, Gamma and Lognormal, are used to assess the wind speed frequency distribution in 2010. To enhance the evaluation accuracy, three intelligent optimization parameter estimation algorithms, i.e., the particle swarm optimization algorithm (PSO), differential evolution algorithm (DE) and ant colony algorithm (ACO), are used to estimate the parameters of these distributions. (4) It is helpful to analyze the wind characteristics when assessing wind resources and selecting wind turbines. Therefore, the optimal frequency distribution based on the best parameter estimation method can be chosen to calculate the wind power density, the most probable wind speed and the wind speed carrying the maximum energy. The experimental results show that Site 1 and Site 4 are more suitable for large wind farms than Site 2 or Site 3.
Li, W, Dai, Y, Hao, H, Lu, H, Albinson, R & Li, Z 2015, 'Oil-saving pathways until 2030 for road freight transportation in China based on a cost-optimization model', Energy, vol. 86, pp. 369-384.View/Download from: Publisher's site
© 2015 Elsevier Ltd. This paper proposed a COSM (cost-optimization superstructure model) and derived the optimized oil-saving pathways for road freight transportation in China until 2030. The optimization target of the COSM was to minimize the accumulated energy and vehicle costs from 2010 to 2030 by choosing the most cost-effective fuel option for newly registered trucks each year. Based on the COSM, three scenarios were developed to evaluate the oil-saving pathway in terms of imported crude oil price, available alternative fuels and GHG emission reduction. The scenario analysis results indicate that: (1) for scenario A, the accumulated oil-saving potential was approximately about 13%, while the oil-saving potential of improving fuel consumption rate and load running rate was 17% and 16%; (2) for scenario B, the accumulated oil-saving potential increased to 82% in reference oil price and 23% in low oil price; (3) for scenario C, to reduce per ton of GHG emission, the increased cost will increase from 34 USD to 450 USD when the GHG emission target decreased from 15.4 billion tons to the turn point of 13.5 billion tons.
Al-Hassan, M, Lu, H & Lu, J 2015, 'A semantic enhanced hybrid recommendation approach: A case study of e-Government tourism service recommendation system', DECISION SUPPORT SYSTEMS, vol. 72, pp. 97-109.View/Download from: Publisher's site
Su, Z, Wang, J, Lu, H & Zhao, G 2014, 'A new hybrid model optimized by an intelligent optimization algorithm for wind speed forecasting', ENERGY CONVERSION AND MANAGEMENT, vol. 85, pp. 443-452.View/Download from: Publisher's site
Zhao, W, Wang, J & Lu, H 2014, 'Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model', Omega-International Journal Of Management Science, vol. 45, pp. 80-91.View/Download from: Publisher's site
Electricity consumption forecasting has been always playing a vital role in power system management and planning. Inaccurate prediction may cause wastes of scarce energy resource or electricity shortages. However,forecasting electricity consumption has proven to be a challenging task due to various unstable factors. Especially,China is under going a period of economic transition,which highlights this difficulty. This paper proposes a time-varying-weight combining method,i.e.High-order Markov chain based Time-varying Weighted Average(HM-TWA) method to predict the monthly electricity consumption in China. HM-TWA first calculates the in-sample time-varying combining weights by quadratic programming for the individual forecasts.Then it predicts the out-of-sample time-varying adaptive weights through extrapolating these in-sample weights using a high-order Markov chain model. Finally,the combined forecasts can be obtained. In addition,to ensure that the sample data have the same properties as the required forecasts,a reasonable multi-step-ahead forecasting scheme is designed forHM-TWA.The out-of-sample forecasting performance evaluation shows that HM-TWA outperforms the component models and traditional combining methods,and its effectiveness is further verified by comparing it with some other existing models.
Alqahtani, A, Lu, H & Lu, J 2014, 'Knowledge-based life event model for e-government service integration with illustrative examples', Intelligent Decision Technologies, vol. 8, no. 3, pp. 189-205.View/Download from: Publisher's site
The advancement of information and communications technology and web services offers an opportunity for e-government service integration, which can help improve the availability and quality of services offered. However, few of the potential service integration applications have been adopted by governments to increase the accessibility of and satisfaction with government services and information for citizens. Recently, the 'life event' concept was introduced as the core element of integrating complexity of service delivery to improve the efficiency and reusability of e-government services, web-based information management systems. In addition, a semantic web-based ontology is considered to be the most powerful conceptual approach for dealing with challenges associated with developing seamless systems in distributed environments. Among these challenges are interoperability, which can be loosely defined as the technical capability for interoperation. Despite the conceptual emergence of semantic web-based ontology for life events, the question remains of what methodology to use when designing a semantic web-based ontology for life events. This paper proposes a semantic web-based ontology model for life events for e-government service integration created using a methodology that implements the model using the ontology modelling tool Protégé and evaluates the model using Pellet Reasoner and the SPARQL query language. In addition, this model is illustrated by two examples, the Saudi Arabia King Abdullah Scholarship and Hafiz, to show the advantages of integrated systems compared with standalone systems. These examples show that the new model can effectively support the integration of standalone e-government services automatically so that citizens do not need to manually execute individual services. This can significantly improve the accessibility of e-government services and citizen's satisfaction. © 2014-IOS Press.
Nguyen, TTS, Lu, HH & Lu, J 2014, 'Web-Page Recommendation Based on Web Usage and Domain Knowledge', IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 10, pp. 2574-2587.View/Download from: Publisher's site
Guo, Y, Zhu, J, Lu, H, Li, Y & Jin, J 2014, 'Core loss computation in a permanent magnet transverse flux motor with rotating fluxes', IEEE Transactions on Magnetics, vol. 50, no. 11.View/Download from: Publisher's site
© 2014 IEEE. This paper presents the core loss computation in a permanent magnet transverse flux motor (TFM) with soft magnetic composite stator core and mild steel rotor yoke, in which the magnetic fluxes rotate. The computation is based on modified core loss models and finite element magnetic field analysis [finite element analysis (FEA)]. The coefficients for the core loss models are obtained by curve-fitting measurements on samples, and the magnetic flux density patterns in the motor are obtained by time-stepping FEA while operating conditions are considered. The computations of the motor core losses agree with the measured values on the TFM prototype.
Islam, MR, Youguang, G, Jianguo, Z, Haiyan, L & Jian, XJ 2014, 'High-Frequency Magnetic-Link Medium-Voltage Converter for Superconducting Generator-Based High-Power Density Wind Generation Systems', IEEE Transactions on Applied Superconductivity, vol. 24, no. 5, pp. 1-5.View/Download from: Publisher's site
Luo, Z, Hu, Z, Song, Y, Xu, Z & Lu, H 2013, 'Optimal Coordination of Plug-In Electric Vehicles in Power Grids With Cost-Benefit Analysis-Part I: Enabling Techniques', IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 3546-3555.View/Download from: Publisher's site
Plug-in electric vehicles (PEVs) appear to offer a promising option for mitigating greenhouse emission. However, uncoordinated PEV charging can weaken the reliability of power systems. The proper accommodation of PEVs in a power grid imposes many challenges on system planning and operations. This work aims to investigate optimal PEV coordination strategies with cost-benefit analysis. In Part I, we first present a new method to calculate the charging load of PEVs with a modified Latin hypercube sampling (LHS) method for handling the stochastic property of PEVs. We then propose a new two-stage optimization model to discover the optimal charging states of PEVs in a given day. Using this model, the peak load with charging load of PEVs is minimized in the first stage and the load fluctuation is minimized in the second-stage with peak load being fixed as the value obtained in the first stage. An algorithm based on linear mixed-integer programming is provided as a suitable solution method with fast computation. Finally, we present a new method to calculate the benefit and cost for a PEV charging and discharging coordination strategy from a social welfare approach. These methods are useful for developing PEV coordination strategies in power system planning and supporting PEV-related policy making.
Luo, Z, Hu, Z, Song, Y, Xu, Z & Lu, H 2013, 'Optimal Coordination of Plug-in Electric Vehicles in Power Grids With Cost-Benefit AnalysisPart II: A Case Study in China', IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 3556-3565.View/Download from: Publisher's site
Continuing with a set of enabling techniques for the optimal coordination of plug-in electric vehicles (PEVs) in Part I, we present a case study in this paper using techniques based on the data collected in the BeijingTianjinTangshan Region (BTTR) China to discover optimal PEV coordination strategies and assess the attractiveness of these strategies. In Part II, we first present the charging characteristics for different categories of PEVs in BTTR and predict the optimal seasonal daily loads with PEVs under different PEV penetration levels using a two-stage optimization model in both 2020 and 2030. The simulation results indicate that optimal PEV coordination effectively reduces the peak load and smooths the load curve. Finally, we present a cost-benefit analysis of optimal coordination strategies by taking a social welfare approach. The analysis shows that the optimal coordination strategies are beneficial in terms of the reduction in capital investment in power grid expansion and that the attractiveness of a coordination strategy is related to the coordination level. The results also show that the fully coordinated charging and vehicle to grid are not the most attractive strategies. This case study is useful for better understanding the costs and benefits of PEV coordination strategies and for supporting PEV-related decision and policy making from a power system planning perspective.
Wu, J, Wang, J, Lu, H, Dong, Y & Lu, X 2013, 'Short Term Load Forecasting Technique Based On The Seasonal Exponential Adjustment Method And The Regression Model', Energy Conversion and Management, vol. 70, no. 1, pp. 1-9.View/Download from: Publisher's site
For an energy-limited economy system, it is crucial to forecast load demand accurately. This paper devotes to 1-week-ahead daily load forecasting approach in which load demand series are predicted by employing the information of days before being similar to that of the forecast day. As well as in many nonlinear systems, seasonal item and trend item are coexisting in load demand datasets. In this paper, the existing of the seasonal item in the load demand data series is firstly verified according to the Kendall t correlation testing method. Then in the belief of the separate forecasting to the seasonal item and the trend item would improve the forecasting accuracy, hybrid models by combining seasonal exponential adjustment method (SEAM) with the regression methods are proposed in this paper, where SEAM and the regression models are employed to seasonal and trend items forecasting respectively. Comparisons of the quartile values as well as the mean absolute percentage error values demonstrate this forecasting technique can significantly improve the accuracy though models applied to the trend item forecasting are eleven different ones. This superior performance of this separate forecasting technique is further confirmed by the paired-sample T tests.
Guo, Z, Zhao, W, Lu, H & Wang, J 2012, 'Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model', Renewable Energy, vol. 37, no. 1, pp. 241-249.View/Download from: Publisher's site
In this paper, a modified EMD-FNN model (empirical mode decomposition (EMD) based feed-forward neural network (FNN) ensemble learning paradigm) is proposed for wind speed forecasting. The nonlinear and non-stationary original wind speed series is first decomposed into a finite and often small number of intrinsic mode functions (IMFs) and one residual series using EMD technique for a deep insight into the data structure. Then these sub-series except the high frequency are forecasted respectively by FNN whose input variables are selected by using partial autocorrelation function (PACF). Finally, the prediction results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original wind speed series. Further more, the developed model shows the best accuracy comparing with basic FNN and unmodified EMD-based FNN through multi-step forecasting the mean monthly and daily wind speed in Zhangye of China.
Wang, J, Lu, H, Dong, Y & Chi, D 2012, 'The Model Of Chaotic Sequences Based On Adaptive Particle Swarm Optimization Arithmetic Combined With Seasonal Term', Applied Mathematical Modelling, vol. 36, no. 3, pp. 1184-1196.View/Download from: Publisher's site
Within a competitive electric power market, electricity price is one of the core elements, which is crucial to all the market participants. Accurately forecasting of electricity price becomes highly desirable. This paper propose a forecasting model of el
Nguyen, TTS, Lu, H, Tran, TP & Lu, J 2012, 'Investigation of sequential pattern mining techniques for web recommendation', International Journal of Information and Decision Sciences, vol. 4, no. 4, pp. 293-312.View/Download from: Publisher's site
Increased application of sequence mining in web recommender systems (WRS) requires a better understanding of the performance and a clear identification of the strengths and weaknesses of existing algorithms. Among the commonly used sequence mining methods, the tree-based approach, such as pre-order linked WAP-tree mining algorithm (PLWAP-Mine) and conditional sequence mining algorithm (CS-Mine), has demonstrated high performance in web mining applications. However, its advantages over other mining methods are not well explained and understood in the context of WRS. This paper firstly reviews the existing sequence mining algorithms, and then studies the performance of two outstanding algorithms, i.e., the PLWAP-Mine and CS-Mine algorithms, with respect to their sensitivity to the dataset variability, and their practicality for web recommendation. The results show that CS-Mine performs faster than PLWAP-Mine, but the frequent patterns generated by PLWAP-Mine are more effective than CS-Mine when applied in web recommendations. These results are useful to WRS developers for the selection of appropriate sequence mining algorithms. © 2012 Inderscience Enterprises Ltd.
Guo, Y, Zhu, J, Lu, H, Lin, Z & Li, Y 2012, 'Core Loss Calculation for Soft Magnetic Composite Electrical Machines', IEEE Transactions On Magnetics, vol. 48, no. 11, pp. 3112-3115.View/Download from: Publisher's site
Soft magnetic composite (SMC) materials are especially suitable for developing electrical machines with complex structure and three-dimensional (3-D) magnetic flux path. In these SMC machines, the magnetic field is in general 3-D and rotational, so the mechanism and calculation of core loss may be quite different from that in traditional electrical machines with laminated steels in which the magnetic field is restrained. This paper investigates the calculation of core loss in a permanent magnet claw pole motor with SMC stator core. First, core loss models are developed based on the experimental data on SMC samples by using a 3-D magnetic property tester. Then, 3-D magnetic time-stepping field finite element analysis (FEA) is conducted to find the flux density locus in each element when the rotor rotates. The core loss is computed based on the magnetic field FEA results by using the developed core loss models. The calculations agree well with the experimental measurements on the SMC motor prototype.
Guo, Z, Wu, J, Lu, H & Wang, J 2011, 'A Case Study On A Hybrid Wind Speed Forecasting Method Using BP Neural Network', Knowledge-Based Systems, vol. 24, no. 7, pp. 1048-1056.View/Download from: Publisher's site
Wind energy, which is intermittent by nature, can have a significant impact on power grid security, power system operation, and market economics, especially in areas with a high level of wind power penetration. Wind speed forecasting has been a vital part of wind farm planning and the operational planning of power grids with the aim of reducing greenhouse gas emissions. Improving the accuracy of wind speed forecasting algorithms has significant technological and economic impacts on these activities, and significant research efforts have addressed this aim recently. However, there is no single best forecasting algorithm that can be applied to any wind farm due to the fact that wind speed patterns can be very different between wind farms and are usually influenced by many factors that are location-specific and difficult to control. In this paper, we propose a new hybrid wind speed forecasting method based on a back-propagation (BP) neural network and the idea of eliminating seasonal effects from actual wind speed datasets using seasonal exponential adjustment. This method can forecast the daily average wind speed one year ahead with lower mean absolute errors compared to figures obtained without adjustment, as demonstrated by a case study conducted using a wind speed dataset collected from the Minqin area in China from 2001 to 2006.
Wang, J, Chi, D, Wu, J & Lu, H 2011, 'Chaotic Time Series Method Combined With Particle Swarm Optimization And Trend Adjustment For Electricity Demand Forecasting', Expert Systems With Applications, vol. 38, no. 7, pp. 8419-8429.View/Download from: Publisher's site
Electricity demand forecasting plays an important role in electric power systems planning. In this paper, nonlinear time series modeling technique is applied to analyze electricity demand. Firstly, the phase space, which describes the evolution of the be
Zheng, L, Jin, J, Guo, Y, Xu, W, Lu, H & Zhu, J 2011, 'Characteristics and Optimization of a PMLSM for HTS Magnetic Suspension Propulsion System', Journal of Applied Superconductivity and Electromagnetics, vol. 1, no. 1, pp. 66-74.
¾Permanent magnet (PM) linear synchronous motors (PMLSMs) can be integrated with a high temperature superconducting (HTS) magnetic suspension system to be used in such as electromagnetic aircraft launcher and maglev transportation which have a levitated object moving on a long linear track. This paper presents the design and electromagnetic characteristic analysis of a long-primary single-sided PMLSM for a HTS bulk-PM guideway repulsion magnetic suspension propulsion system. Based on the characteristics and performance analysis of the PMLSM, a new type of HTS suspension propulsion system driven by a double-sided PMLSM with an optimal PM structure is then proposed. The running characteristics of the linear propulsion systems are studied through finite element analysis (FEA) with comprehensive performance results obtained for practical development.
Energy crisis has made it urgent to find alternative energy sources for sustainable energy supply; wind energy is one of the attractive alternatives. Within a wind energy system, the wind speed is one key parameter; accurately forecasting of wind speed can minimize the scheduling errors and in turn increase the reliability of the electric power grid and reduce the power market ancillary service costs. This paper proposes a new hybrid model for long-term wind speed forecasting based on the first definite season index method and the Autoregressive Moving Average (ARMA) models or the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) forecasting models. The forecasting errors are analyzed and compared with the ones obtained from the ARMA, GARCH model, and Support Vector Machine (SVM); the simulation process and results show that the developed method is simple and quite efficient for daily average wind speed forecasting of Hexi Corridor in China. Copyright © 2010 Zhenhai Guo, et al.
Lu, H, Sriyanyong, P, Song, Y & Dillon, TS 2010, 'Experimental study of a new hybrid PSO with mutation for economic dispatch with non-smooth cost function', International Journal of Electrical Power & Energy Systems, vol. 32, no. 9, pp. 921-935.View/Download from: Publisher's site
Particle swarm optimization (PSO) is a population-based evolutionary technique. Advancements in the PSO development over the last decade have made it one of the most promising optimization algorithms for a wide range of complex engineering optimization problems which traditional derivative-based optimization techniques cannot handle. The most attractive features of PSO are its algorithmic simplicity and fast convergence. However, PSO tends to suffer from premature convergence when applied to strongly multi-modal optimization problems. This paper proposes a method of incorporating a real-valued mutation (RVM) operator into the PSO algorithms, aimed at enhancing global search capability. Three variants of PSO algorithms are considered. The resultant hybrid PSO-RVM algorithms are experimentally investigated along with the PSO variants and an existing PSO with Gaussian mutation using six typical benchmark functions. It is interesting to see that the effectiveness of RVM varies for different PSO variants as well as different kinds of functions. It has been found that one of the hybrid algorithms, CBPSO-RVM, which is an integration of the PSO with the constriction factor and inertia weight (CBPSO) and the RVM operator, exhibits significantly better performance in most of the test cases compared to the other algorithms under consideration. Furthermore, this algorithm is superior to most of the existing algorithms used in this study when applied to two practical ED problems with non-smooth cost function considering the multiple fuel type and/or valve-point loading effects.
Wang, J, ZHU, S, Zhang, W & Lu, H 2010, 'Combined Modeling For Electric Load Forecasting With Adaptive Particle Swarm Optimisation', Energy, vol. 35, no. 4, pp. 1671-1678.View/Download from: Publisher's site
Electric load forecasting is crucial for managing electric power systems economically and safely. This paper presents a new combined model for electric load forecasting based on the seasonal ARIMA forecasting model, the seasonal exponential smoothing mod
Guo, Y, Zhu, J, Lu, H, Lin, Z, Zhong, J, Wang, S & Jin, J 2010, 'Modeling of Vector Magnetic Hysteresis of Soft Magnetic Composite Material', Journal of Applied Superconductivity and Electromagnetics, vol. 1, no. 1, pp. 142-146.View/Download from: Publisher's site
Thanks to the unique magnetic properties, soft magnetic composite (SMC) materials and their application in electromagnetic devices have achieved significant development. The typical application example of SMC is the electrical machine with complex structure, such as claw pole and transverse flux machines, in which the magnetic field is basically rotary. To design and analyze such a device, vector magnetic properties of the core material should be properly determined, modeled and applied. This paper presents the modeling of vector magnetic hysteresis of SMC based on a Stoner-Wohlfarh (S-W) elemental operator. A phenomenological mean-field approximation is used to consider the interaction between particles. With the presented model, the magnetization processes of SMC under both alternating and rotating fluxes are numerically simulated. The simulations have been verified by experimental measurements.
Guo, Y, Jin, J, Zheng, L, Zhu, J & Lu, H 2008, 'A Permanent Magnet Linear Synchronous Motor Drive for HTS Maglev Transportation Systems', Journal of Electronic Science and Technology of China - Zhongguo Dianzi Keji, vol. 6, no. 2, pp. 125-129.
A permanent magnet linear synchronous motor (pMLSM) for a high temperature superconducting (HTS) maglev system has been studied, including the motor structure, control strategy, and analysis techniques. Finite elemeut analysis (FEA) of magnetic field is conducted to accurately calculate major motor parameters. Equivalent electrical circuit is used to predict the drive's steady-state characteristics, and a phase variable model is applied to predict the dynamic performance. Preliminary experiment with a prototype has been made to verify the theoretical analysis and the HTS-PM synchronons driving technology.
Guo, Y, Zhu, J, Lin, Z, Lu, H, Wang, X & Chen, J 2008, 'Influence of inductance variation on performance of a permanent magnet claw pole soft magnetic composite motor', Journal of Applied Physics, vol. 103, no. 7, pp. 1-3.View/Download from: Publisher's site
Winding inductance is an important parameter in determining the performance of electrical machines, particularly those with large inductance variation. This paper investigates the influence of winding inductance variation on the performance of a three-phase three-stack claw pole permanent magnet motor with soft magnetic composite (SMC) stator by using an improved phase variable model. The winding inductances of the machine are computed by using a modified incremental energy method, based on three-dimensional nonlinear time-stepping magnetic field finite element analyses. The inductance computation and performance simulation are verified by the experimental results of an SMC claw pole motor prototype.
Guo, Y, Zhu, J, Zhong, J, Lu, H & Jin, J 2008, 'Measurement and Modeling of Rotational Core Losses of Soft Magnetic Materials Used in Electrical Machines - A Review', IEEE Transactions On Magnetics, vol. 44, no. 2, pp. 279-291.View/Download from: Publisher's site
In many situations, for example, in the cores of a rotating electrical machine and the T-joints of multiphase transformers, the magnetic flux varies with time in terms of both magnitude and direction, i.e., the local flux density vector rotates with varying magnitude and varying speed. Therefore, it is important that the magnetic properties of the core materials under various rotational magnetizations be properly investigated, modeled, and applied in the design and analysis of electromagnetic devices with rotational flux. Drawing from the huge amount of papers published by various researchers in the past century, this paper presents an extensive survey on the measurement and modeling of rotational core losses of soft magnetic materials used in electrical machines, particularly from the view of practical engineering application. The paper aims to provide a broad picture of the historical development of measuring techniques, measuring apparatus, and practical models of rotational core losses.
Dou, Y, Guo, Y, Zhu, J & Lu, H 2007, 'Effect Of Armature Reaction Of A Permanent-magnet Claw Pole SMC Motor', IEEE Transactions On Magnetics, vol. 43, no. 6, pp. 2561-2563.View/Download from: Publisher's site
The finite-element method enables an accurate analysis for the study on effects of armature reaction in electromagnetic devices, particularly those with complex structures and three-dimensional (3-D) magnetic flux paths. This paper investigates the effec
Guo, Y, Chen, J, Zhu, J, Lu, H & Jin, J 2007, 'Performance Analysis of an SMC Transverse Flux Motor with Brushless DC Control Scheme', Journal of JSAEM, Japanese Society of Applied Electromagnetics and Mechanics, vol. 15, pp. 81-84.
Guo, Y, Jin, J, Zhu, J & Lu, H 2007, 'Design And Analysis Of A Prototype Linear Motor Driving System For Hts Maglev Transportation', IEEE Transactions On Applied Superconductivity, vol. 17, no. 2, pp. 2087-2090.View/Download from: Publisher's site
High temperature superconductors (HTSs) can produce a strong magnetic levitation force with self-stabilizing feature and hence have attracted much attention for applications in maglev transportation systems. For the linear motion of transportation, a lin
Guo, Y, Zhu, J & Lu, H 2007, 'Effects of armature reaction on the performance of a claw pole motor with soft magnetic composite stator by finite-element analysis', IEEE Transactions On Magnetics, vol. 43, no. 3, pp. 1072-1077.View/Download from: Publisher's site
We investigated the effects of armature reaction on the performance of a three-phase three-stack claw pole motor with soft magnetic composite stator core by using three-dimensional finite-element analysis (FEA), which is an effective approach to accurate
Guo, Y, Zhu, J, Lin, Z, Zhong, J, Lu, H & Wang, S 2007, 'Determination of 3D magnetic reluctivity tensor of soft magnetic composite material', Journal Of Magnetism And Magnetic Materials, vol. 312, no. 2, pp. 458-463.View/Download from: Publisher's site
Soft magnetic composite (SMC) materials are especially suitable for construction of electrical machines with complex structures and three-dimensional (3D) magnetic fluxes. In the design and optimization of such 3D flux machines, the 3D vector magnetic pr
Guo, Y, Zhu, J, Lu, H & Jin, J 2007, 'Computation of Incremental Inductances for Nonlinear Dynamic Analysis of a PM Claw Pole SMC Motor', Journal of JSAEM, Japanese Society of Applied Electromagnetics and Mechanics, vol. 15, pp. 254-257.
Guo, Y, Zhu, J, Lu, H & Jin, J 2007, 'Parameter Computation and Performance Prediction of a Claw Pole/Transverse Flux Motor with Soft Magnetic Composite Core', Journal of Science, Technology and Engineering, vol. 1, no. 1, pp. 13-16.
Along with the trends of higher and higher frequency operations and smaller and smaller physical volumes of power electronic systems, the transformers and inductors used in the power electronic systems are facing challenge to operate at high frequencies. This paper presents a survey on the soft magnetic materials used in the high frequency high power density (HFHPD) transformers and inductors in the power electronic systems. Various types of magnetic material, such as electrical sheets, soft ferrites and amorphous magnetic alloys, are reviewed. It is revealed that soft ferrites seem the most suitable for the core materials of HFHPD transformers.
Lu, H, Zhu, J, Guo, Y & Hui, SY 2007, 'A practical circuit model of high frequency transformers in power electronic systems', Australian Journal of Electrical and Electronics Engineering, vol. 3, no. 3, pp. 211-224.
Zhong, J, Guo, Y, Zhu, J, Lu, H & Jin, J 2007, 'Techniques and Apparatus for Measuring Rotational Core Losses of Soft Magnetic Materials', Journal of Electronic Science and Technology of China - Zhongguo Dianzi Keji, vol. 5, no. 3, pp. 218-225.
Lu, H, Zhong, JJ, Guo, YG, Zhu, JG & Jin, JX 2007, 'Techniques and Apparatus for Measuring Rotational Core Losses of Soft Magnetic Materials', Journal of Electronic Science and Technology of China, vol. 5, no. 3, pp. 218-225.
Lu, H, Zhu, J & Hui, SY 2007, 'Measurement And Modeling Of Thermal Effects On Magnetic Hysteresis Of Soft Ferrites', IEEE Transactions On Magnetics, vol. 43, no. 11, pp. 3952-3960.View/Download from: Publisher's site
We present experimental measurement of thermal effects on magnetic hysteresis of soft ferrite cores commonly used in high-frequency inductors and transformers and propose a method to model the thermal effects. We measured the major hysteresis loops of so
Guo, Y, Zhu, J & Lu, H 2006, 'Accurate determination of parameters of a claw-pole motor with SMC stator core by finite-element magnetic-field analysis', IEE Proceedings-Electric Power Applications, vol. 153, no. 4, pp. 568-574.View/Download from: Publisher's site
Effective and accurate prediction of key motor parameters, such as winding flux, back electromotive force, inductance and core losses, is crucial for design of high-performance motors. Particularly, for electrical machines with new materials and nonconve
Lu, H & Song, Y 2005, 'Brief Introduction to the Development of Electric Power Industry in UK', Modern Electric power, vol. 22, no. 2, pp. 91-94.
Jay, B, Lu, H & Nguyen, Q 2004, 'The Polymorphic Imperative: a Generic Approach to In-place Update', Electronic Notes in Theoretical Computer Science - Proceedings of Computing: The Australasian Theory Symposium (CATS) 2004, vol. 91, pp. 195-211.
The constructor calculus supports generic operations defined over arbitrary data types including abstract data types. This paper extends the basic constructor calculus to handle constructed locations. The resulting calculus is able to define a generic assignment operation that performs in-place whenever appropriate and allocates fresh memory otherwise. This approach may eliminate many of the space overheads associated with higher-order polymorphic languages. In combination with existing generic programming techniques it can express some very powerful algorithms such as the visitor pattern.
Lu, H, Zhu, J & Ron Hui, SY 2003, 'Experimental determination of stray capacitances in high frequency transformers', IEEE Transactions On Power Electronics, vol. 18, no. 5, pp. 1105-1112.View/Download from: Publisher's site
Aik, KC, Lai, LL, Lee, KY, Lu, H, Park, JB, Song, YH, Srinivasan, D, Vlachogiannis, JG & Yu, IK 2007, 'Applications to Power System Scheduling' in Modern Heuristic Optimization Techniques: Theory and Applications to Power Systems, pp. 337-402.View/Download from: Publisher's site
Song, YH, Lu, H, Lee, KY & Yu, IK 2007, 'Fundamentals of Ant Colony Search Algorithms' in Modern Heuristic Optimization Techniques: Theory and Applications to Power Systems, pp. 89-100.View/Download from: Publisher's site
Islam, MR, Lu, HH, Hossain, MJ & Li, L 2019, 'A Comparison of Performance of GA, PSO and Differential Evolution Algorithms for Dynamic Phase Reconfiguration Technology of a Smart Grid', 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, 2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand, pp. 858-865.View/Download from: Publisher's site
© 2019 IEEE. Increasing penetration of Distributed Generations (Photovoltaic solar energy (PV), Wind energy, and Battery Energy Storage) and PEVs (Plug-in Electric Vehicles) into smart grid induce network imbalance which reduces power quality. The uncertainty of demand-generation requires balancing for mitigating network imbalance. Several researchers have used various optimization methods for mitigating unbalance. Moreover, a few researchers have done comparative studies of optimization methods for mitigating unbalance till now. This paper proposes a method to mitigate unbalance and reduce the total power loss by optimizing load distribution among phases. This paper compares the performance of Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms on the application of phase balancing. Finally, the efficacy of these algorithms are evaluated for the proposed unbalance mitigation technique, and it is found that the proposed technique using DE algorithm can reduce a significant amount of unbalance at all the buses of the distribution grid with less computational effort.
Song, Y, Zhang, G, Lu, H & Lu, J 2019, 'A Noise-tolerant Fuzzy c-Means based Drift Adaptation Method for Data Stream Regression', IEEE International Conference on Fuzzy Systems, IEEE International Conference on Fuzzy Systems, IEEE, New Orleans, LA, USA.View/Download from: Publisher's site
© 2019 IEEE. Concept drift referring to the changes of data distributions has been one critical challenge typically associated with mining data streams. Current drift detection and adaptation methods focus on how to immediately detect the distribution changes once the concept drift occurs and swiftly update the model to be applicable to the newly arrived data instances. Most of those methods assume the data does not have noise or the noise is too weak to affect the modeling procedure. However, realworld data are normally contaminated, and denoise techniques are highly preferred as a necessary preprocess. This issue is more complex for a data stream with concept drift because the noise is very likely to be confused with drift. Motivated by that, this paper proposes a Noise-tolerant Fuzzy c-means based drift Adaptation method (NFA) which can adapt to the changing distributions and is suitable for noisy data streams. The concept drift problem is solved by using a fuzzy c-means based regression model to continuously include the most relevant data instances to the latest pattern in the training set. In addition, a denoise technique is designed in NFA to remove noise, and the ability of incremental updating enables it to be embedded in the incremental drift adaptation process, and therefore NFA can solve concept drift and noise problems at the same time. Experimental evaluation results also show good performance of our method on handling data streams with concept drift and noise.
Islam, MR, Helen Lu, H, Hossain, MJ & Li, L 2019, 'Improving Power Quality of Distributed PV-EV Distribution Grid by Mitigating Unbalance', 2019 IEEE International Conference on Industrial Technology (ICIT), 2019 IEEE International Conference on Industrial Technology (ICIT), IEEE, Melbourne, Australia.View/Download from: Publisher's site
Islam, MR, Lu, H, Fang, G, Li, L & Hossain, MJ 2019, 'Optimal Dispatch of Electrical Vehicle and PV Power to Improve the Power Quality of an Unbalanced Distribution Grid', 2019 Proceedings of International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS), 2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS), IEEE, Shenzhen, China.View/Download from: Publisher's site
Islam, MR, Lu, H, Hossain, MJ & Li, L 2019, 'Compensating Neutral Current, Voltage Unbalance and Improving Voltage of an Unbalanced Distribution Grid Connected with EV and Renewable Energy Sources', 2019 22nd International Conference on Electrical Machines and Systems (ICEMS), International Conference on Electrical Machines and Systems, IEEE, Harbin, China.View/Download from: Publisher's site
Coordinating electric vehicle (EV) charging offers several possible solutions, e.g., charging or discharging rate, and schedule time to improve performances of the distribution network. But EV charging or discharging schedule can be affected due to the punctuality of EV users and equipment failures. The growing penetration of EVs is expected to affect the distribution network performances (voltage unbalance, neutral current, and voltage) as well as generation scheduling due to EV uncertainties. Most of the proposed EV charging control strategies improve the network performance ignoring comfortability (change charging or discharging rate) and lack of punctuality of EV users. This paper investigates the impact of EV uncertainty on the imbalance of the network in a higher penetrated distribution grid. A centralized control algorithm is proposed to coordinate EVs and DESs service point of connection (SPOC) among phases to mitigate the network imbalance and improve the voltage. Using the proposed control approach, the candidate DES number is reduced to participate, whereas EV users do not require to participate. Results obtained using the proposed control approach shows that the neutral current reduces 82.98%, voltage unbalance up to 99.08% and improve voltage up to 17.08%.
Islam, MR, Lu, HH, Hossain, MJ & Li, L 2019, 'A Comparison of Performance of GA, PSO and Differential Evolution Algorithms for Dynamic Phase Reconfiguration Technology of a Smart Grid', 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, 2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand, pp. 858-865.View/Download from: Publisher's site
© 2019 IEEE. Increasing penetration of Distributed Generations (Photovoltaic solar energy (PV), Wind energy, and Battery Energy Storage) and PEVs (Plug-in Electric Vehicles) into smart grid induce network imbalance which reduces power quality. The uncertainty of demand-generation requires balancing for mitigating network imbalance. Several researchers have used various optimization methods for mitigating unbalance. Moreover, a few researchers have done comparative studies of optimization methods for mitigating unbalance till now. This paper proposes a method to mitigate unbalance and reduce the total power loss by optimizing load distribution among phases. This paper compares the performance of Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms on the application of phase balancing. Finally, the efficacy of these algorithms are evaluated for the proposed unbalance mitigation technique, and it is found that the proposed technique using DE algorithm can reduce a significant amount of unbalance at all the buses of the distribution grid with less computational effort.
Song, Y, Zhang, G, Lu, H & Lu, J 2018, 'A Self-adaptive Fuzzy Network for Prediction in Non-stationary Environments', 2018 IEEE International Conference on Fuzzy Systems, International Conference on Fuzzy Systems, Rio de Janeiro, Brazil, pp. 1-8.
ElShaweesh, O, Hussain, FK, Lu, H, Al-Hassan, M & Kharazmi, S 2017, 'Personalized Web Search Based on Ontological User Profile in Transportation Domain', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 24th International Conference on Neural Information Processing 2017, Springer, Guangzhou, China, pp. 239-248.View/Download from: Publisher's site
Lu, H, Heng, J & Wang, C 2017, 'An AI-Based Hybrid Forecasting Model for Wind Speed Forecasting', Neural Information Processing Part IV (LNCS), International Conference on Neural Information Processing, Springer, Guangzhou, China, pp. 221-230.View/Download from: Publisher's site
© 2017, Springer International Publishing AG. Forecasting of wind speed plays an important role in wind power prediction for management of wind energy. Due to intermittent nature of wind, accurately forecasting of wind speed has been a long standing research challenge. Artificial neural networks (ANNs) is one of promising approaches to predict wind speed. However, since the results of ANN-based models are strongly dependent on the initial weights and thresholds values which are usually randomly generated, the stability of forecasting results is not always satisfactory. This paper presents a new hybrid model for short term forecasting of wind speed with high accuracy and strong stability by optimizing the parameters in a generalized regression neural network (GRNN) using a multi-objective firefly algorithm (MOFA). To evaluate the effectiveness of this hybrid algorithm, we apply it for short-term forecasting of wind speed from four wind power stations in Penglai, China, along with four typical ANN-based models, which are back propagation neural network (BPNN), radical basis function neural network (RBFNN), wavelet neural network (WNN) and GRNN. The comparison results clearly show that this hybrid model can significantly reduce the impact of randomness of initialization on the forecasting results and achieve good accuracy and stability.
Yang, G, Dai, Y, Zhao, H, Hirota, K & Lu, H 2017, 'Intelligent web-based experiment management system using multi-agent concept', Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society, Annual Conference of the IEEE Industrial Electronics Society, IEEE, Beijing, China, pp. 8508-8514.View/Download from: Publisher's site
© 2017 IEEE. Many web-based online learning systems focus more on textual and/or image based content delivery without including experiment systems, or if included they are usually operated under pre-defined conditions, such as fixed scenarios and pre-determined delivery orders. These limitations hinder personalized learning and collaboration between students and discourage student engagement. To circumvent these limitations, an Intelligent Web-based Experiment Management System (IWEMS) using multi-agent concept is presented. In the system, three kinds of software agents are used: (i) Student-Agent, responsible for assessing the knowledge levels of students. A fuzzy set based algorithm is used and the results are plotted through a dynamic polar chart; (ii) Teacher-Agent, responsible for tracking experiment progress of each student and recommending personalized the next-to-do experiment to him or her; and (iii) Co-Agent, responsible for group formation based on similar knowledge levels to facilitate collaborative learning between students. A prototype of this system is developed using a Java Agent Development Framework(JADE), where a client/server architecture and a MySQL database are used. It demonstrates the validity of the design and effectiveness of this system's functionality, achieves the personalization recommendation of next-to-do experiment and collaborative learning environment.
R. Kridalukmana, H. Y. Lu & M. Naderpour 2017, 'An object oriented Bayesian network approach for unsafe driving maneuvers prevention system', 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), International Conference on Intelligent Systems and Knowledge Engineering, IEEE, Nanjing, China, pp. 1-6.View/Download from: Publisher's site
As the main contributor to the traffic accidents, unsafe driving maneuvers have taken attentions from automobile industries. Although driving feedback systems have been developed in effort of dangerous driving reduction, it lacks of drivers awareness development. Therefore, those systems are not preventive in nature. To cover this weakness, this paper presents an approach to develop drivers awareness to prevent dangerous driving maneuvers. The approach uses Object-Oriented Bayesian Network to model hazardous situations. The result of the model can truthfully reflect a driving environment based upon situation analysis, data generated from sensors, and maneuvers detectors. In addition, it also alerts drivers when a driving situation that has high probability to cause unsafe maneuver to be detected. This model then is used to design a system, which can raise drivers awareness and prevent unsafe driving maneuvers.
Song, Y, Zhang, G, Lu, J & Lu, H 2017, 'A fuzzy kernel c-means clustering model for handling concept drift in regression', IEEE International Conference on Fuzzy Systems, International Conference on Fuzzy Systems, IEEE, Naples, Italy, pp. 1-6.View/Download from: Publisher's site
© 2017 IEEE. Concept drift, given the huge volume of high-speed data streams, requires traditional machine learning models to be self-adaptive. Techniques to handle drift are especially needed in regression cases for a wide range of applications in the real world. There is, however, a shortage of research on drift adaptation for regression cases in the literature. One of the main obstacles to further research is the resulting model complexity when regression methods and drift handling techniques are combined. This paper proposes a self-adaptive algorithm, based on a fuzzy kernel c-means clustering approach and a lazy learning algorithm, called FKLL, to handle drift in regression learning. Using FKLL, drift adaptation first updates the learning set using lazy learning, then fuzzy kernel c-means clustering is used to determine the most relevant learning set. Experiments show that the FKLL algorithm is better able to respond to drift as soon as the learning sets are updated, and is also suitable for dealing with reoccurring drift, when compared to the original lazy learning algorithm and other state-of-the-art regression methods.
Chou, KP, Li, DL, Prasad, M, Pratama, M, Su, SY, Lu, H, Lin, CT & Lin, WC 2017, 'Robust Facial Alignment for Face Recognition', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017 International Conference on Neural Information Processing, Guangzhou, China, pp. 497-504.View/Download from: Publisher's site
© 2017, Springer International Publishing AG. This paper proposes a robust real-time face recognition system that utilizes regression tree based method to locate the facial feature points. The proposed system finds the face region which is suitable to perform the recognition task by geometrically analyses of the facial expression of the target face image. In real-world facial recognition systems, the face is often cropped based on the face detection techniques. The misalignment is inevitably occurred due to facial pose, noise, occlusion, and so on. However misalignment affects the recognition rate due to sensitive nature of the face classifier. The performance of the proposed approach is evaluated with four benchmark databases. The experiment results show the robustness of the proposed approach with significant improvement in the facial recognition system on the various size and resolution of given face images.
Padmanabha A G, A, Appaji M, A, Prasad, M, Lu, H & Joshi, S 2017, 'Classification of diabetic retinopathy using textural features in retinal color fundus image', Intelligent Systems and Knowledge Engineering (ISKE), 2017 12th International Conference on, International Conference on Intelligent Systems and Knowledge Engineering, IEEE, Nanjing, China, pp. 1-5.View/Download from: Publisher's site
Early, diagnosis is essential for diabetic patients
to avoid partial or complete blindness. This work presents
a new analysis method of texture features for
classification of Diabetic Retinopathy (DR). The proposed
method masks the blood vessels and optic disk segmented
and directly extracts the textural features from the
remaining retinal region. The proposed method is much
simpler with comparison of the other methods that detect
the defective regions first and then extract the required
features for classification. The Haralick texture measures
calculated are used for classification of DR. The proposed
method is evaluated through a classification of DR using
both Support Vector Machine (SVM) and Artificial
Neural Network (ANN). The results of SVM have a better
accuracy (87.5%) over ANN (79%).The performance of
the proposed method is presented also in terms of
sensitivity and specificity.
Sohaib, O, Lu, H & Hussain, W 2017, 'Internet of Things (IoT) in E-commerce: For People with Disabilities', 12th IEEE Conference on Industrial Electronics and Applications (ICIEA), IEEE Conference on Industrial Electronics and Applications, IEEE, Cambodia.View/Download from: Publisher's site
Lu, H, Zhang, K, Xiao, Y & Wang, C 2016, 'Hybrid model for short-term wind speed forecasting based on non-positive constraint combination theory', Uncertainty Modelling in Knowledge Engineering and Decision Making - Proceedings of the 12th International FLINS Conference, FLINS 2016, The 12th International Conference for Fuzzy Logic and Intelligent Technologies in Nuclear Science, Roubaix, France, pp. 240-245.View/Download from: Publisher's site
© 2016 by World Scientific Publishing Co. Pte. Ltd. Short-term wind speed forecasting plays an irreplaceable role in efficient management of wind energy systems and accurate forecasting results could provide effective future plans for operators of utilities and wind energy systems. Aiming at improving the accuracy of short-term wind forecasting, this paper presents a new forecasting model based on the non-positive constraint combination theory. In this model, a modified optimization algorithm is used to optimize the weight coefficients of the constituent models based on the non-positive constraint combination theory. The combined model is tested using three sets of 10-min wind speed data from real-world wind farms. The testing results show that the forecasting accuracy of new model is significantly better than the constituent models.
Nguyen, TTS & Lu, H 2016, 'Domain ontology construction using web usage data', Proceedings of AI 2016: Advances in Artificial Intelligence (LNCS), Australasian Joint Conference on Artificial Intelligence, Springer, Hobart, Australia, pp. 338-344.View/Download from: Publisher's site
© Springer International Publishing AG 2016.Ontologies play an important role in conceptual model design and the development of machine-readable knowledge bases. They can be used to represent various knowledge not only about content concepts, but also explicit and implicit relations. While ontologies exist for many application domains of websites, the implicit relations between domain and accessed Web-pages might be less concerned and unclear. These relations are crucial for Web-page recommendation in recommender systems. This paper presents a novel method developing an ontology of Web-pages mapped to domain knowledge. It will focus on solutions of semi-automating ontology construction using Web usage data. An experiment of Microsoft Web data is implemented and evaluated.
Guo, Y, Zhu, J, Lu, H & Lei, G 2016, 'Design considerations of electric motors with soft magnetic composite cores', 2016 IEEE 8th International Power Electronics and Motion Control Conference, IPEMC-ECCE Asia 2016, Power Electronics and Motion Control Conference (IPEMC-ECCE Asia), IEEE, Hefei, China, pp. 3007-3011.View/Download from: Publisher's site
© 2016 IEEE.Soft magnetic composite (SMC) materials possess many unique properties, which are particularly suitable for development of novel structure electric motors for various electric drive systems. The unique properties of SMC material include three-dimensional (3-D) magnetic and thermal isotropy, very low eddy current loss, and prospect of very low cost mass production. Therefore, the application of SMC materials in electrical appliance, particularly in electric motors, has attracted great interest in research. However, SMC materials also have some drawbacks, e.g. low permeability, high hysteresis loss and low mechanical strength, and hence a direct replacement of electrical steels by SMC would not necessarily lead to satisfaction or improvement of motor performance. To fully explore the application potential of the SMC materials, their unique properties should be fully employed and at the same time the effects of their drawbacks should be avoided or minimized. This paper aims to present some key issues on design of SMC electric motors based on the extensive research in the past two decades by various researchers including the authors of this paper. The key design issues are discussed and some conclusions are drawn for future effort in this area.
Zhang, Y, Xu, X, Lu, H & Dai, Y 2014, 'Two-stage obstacle detection based on stereo vision in unstructured environment', Proceedings - 2014 6th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2014, International Conference on Intelligent Human-Machine Systems and Cybernetics, IEEE, Hangzhou, PEOPLES R CHINA, pp. 168-172.View/Download from: Publisher's site
© 2014 IEEE. In an unstructured environment, there are many challenges for obstacle detection. This paper presents an improved method to detect obstacles based on stereo vision in unstructured environments based on salient obstacle extraction. This method can achieve same or higher level of accuracy of obstacle detection compared to the existing salient obstacle detection with significant reduction of computation time. This method consists of two stages. In the first stage, it extracts the salient obstacles which stand out from the background in the stereo images using a fast salient obstacle detection method. In the second stage, it refines the detection of small obstacles by computing the geometric relationships among 3D points using an improved space-variant resolution (SVR) with the continuity and the height constraints. The experiment results show that this improved method can reduce computation time and improve detection accuracy.
Hu, Z, Zhang, S, Zhang, F & Lu, H 2013, 'SCUC with battery energy storage system for peak-load shaving and reserve support', IEEE Power and Energy Society General Meeting, IEEE Power and Energy Society General Meeting, IEEE, Vancouver, BC, Canada.View/Download from: Publisher's site
This paper aims to investigate the benefit of deploying battery energy storage system (BESS) in a power system for reducing production cost, shaving peak-load and providing reserve support. A BESS model is built which takes into account the charging and discharging efficiencies, charging/discharging power limits, and reserve capacity limits. This model is incorporated into the security-constrained unit commitment (SCUC) problem. The new SCUC problem is solved to optimally allocate the charging and discharging power and reserve capacity of each BESS. Tests are carried out on the IEEE 24-bus system and simulation results show that lower operational cost can be achieved by using BESS for both peak-load shaving/shifting and reserve support. © 2013 IEEE.
Sriyanyong, P & Lu, H 2013, 'Implementation and Comparison of PSO-Based Algorithms for Multi-Modal Optimization Problems', Proceedings of 2013 International Symposium on Computational Models for Life Sciences vol 1559, Issue 1, International Symposium on Computational Models for Life Sciences, AIP publishing, Sydney, Australia, pp. 165-174.View/Download from: Publisher's site
This paper aims to compare the global search capability and overall performance of a number of Particle Swarm Optimization (PSO) based algorithms in the context solving the Dynamic Economic Dispatch (DED) problem which takes into account the operation limitations of generation units such as valve-point loading effect as well as ramp rate limits. The comparative study uses six PSO-based algorithms including the basic PSO and hybrid PSO algorithms using a popular benchmark test IEEE power system which is 10-unit 24-hour system with non-smooth cost functions. The experimental results show that one of the hybrid algorithms that combines the PSO with both inertia weight and constriction factor, and the Gaussian mutation operator (CBPSO-GM) is promising in achieving the near global optimal of a non-linear multi-modal optimization problem, such as the DED problem under the consideration
Guo, Y, Jin, J, Zhu, JG & Lu, HY 2013, 'Performance Analysis of a Linear Motor with HTS Bulk Magnets for Driving a Prototype HTS Maglev Vehicle', Applied Mechanics and Materials – Linear Drives for Industry Applications XI, Linear Drives for Industry Applications, Trans Tech Publications, Hangzhou, China, pp. 33-37.View/Download from: Publisher's site
This paper presents the performance analysis of a linear synchronous motor which employs high-temperature superconducting (HTS) bulk magnets on the mover and normal copper windings on the stator. The linear motor is designed to drive a prototype HTS maglev vehicle in which the mover is suspended by the levitation force between HTS bulks on the mover and permanent magnets on the ground. Finite element magnetic field analysis is conducted to calculate the major parameters of the linear motor and an equation is derived to calculate the electromagnetic thrust force. Theoretical calculations are verified by the measured results on the prototype.
Guo, Y, Zeng, J, Zhu, J, Lu, H & Jin, J 2013, 'B-H Relations of Magnetorheological Fluid under 2-D Rotating Magnetic Field Excitation', Proceedings of 2013 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, IEEE, Biejing, China, pp. 94-97.View/Download from: Publisher's site
This paper presents the investigation of the B-H relations of a magnetorheological (MR) fluid under one-dimensional (1-D) alternating and two-dimensional (2-D) rotating magnetic field excitations where B is magnetic flux density and H is magnetic field strength. The measurement is carried out by using a single sheet tester with an MR fluid sample. The measurement principle and structure of the testing system are described. The calibration of the B and H sensing coils are also reported. The relations between B and H on the MR fluid sample under 2-D rotating magnetic field excitations have been measured and compared with the results under 1-D excitations showing that the B-H relations under 2-D excitations are significantly different from the 1-D case. These data would be useful for design and analysis of MR smart structures like MR dampers.
Islam, R, Guo, Y, Zhu, J, Lu, H & Jin, J 2013, 'Medium-Frequency-Link Power Conversion for High Power Density Renewable Energy Systems', 2013 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, IEEE, Beijing, China, pp. 102-106.View/Download from: Publisher's site
Recent advances in solid-state semiconductors and magnetic materials have provided the impetus for medium frequency-link based medium voltage power conversion systems, which would be a possible solution to reducing the weight and volume of renewable power generation systems. To verify this new concept, in this paper, a laboratory prototype of 1.26 kVA medium-frequency-link power conversion system is developed for a scaled down 1 kV grid applications. The design and implementation of the prototyping, test platform, and the experimental results are analyzed and discussed. It is expected that the proposed new technology would have a great potential for future renewable and smart grid applications.
Luo, Z, Hu, Z, Song, Y, Xu, Z, Liu, H, Jia, L & Lu, H 2012, 'Economic analyses of plug-in electric vehicle battery providing ancillary services', 2012 IEEE International Electric Vehicle Conference, IEVC 2012, Electric Vehicle Conference (IEVC), IEEE, Institute of Electrical and Electronics Engineers, Greenville, SC, USA.View/Download from: Publisher's site
This paper explores the potential financial return for using plug-in electric vehicles (PEVs) as a grid resource. There are two methods for PEVs to provide ancillary services call interruptible load and vehicle to grid The contract market is introduced first, then the method to calculate the cost benefit of plug-in electric vehicles (PEVs) to provide ancillary services are proposed. Additionally, the expected profits and profits of providing the ancillary services when considering the uncertainties of driving behaviors are both calculated and compared. The calculation results indicate that profits of participating in frequency regulation are higher than that of reserve services. When penalty is neglected or the penalty coefficient is low, the revenue of regulation down services is relatively high. However, with the increasing of penalty factor, the profits decrease dramatically. When the penalty coefficient is sufficiently high, participating in regulation up services in V2G mode is most profitable. © 2012 IEEE.
Al-hassan, MW, Lu, H & Lu, J 2010, 'Personalized e-Government Services: Tourism Recommender System Framework', Web Information Systems and Technologies - Lecture Notes in Computer Science Vol 75 Part III, International Conference on Web Information Systems and Technologies, Springer, Valencia, Spain, pp. 173-178.View/Download from: Publisher's site
Most governments around the globe use the internet and information technologies to deliver information and services for citizens and businesses. One of the main directions in the current e-government (e-Gov) development strategy is to provide better online services to citizens such that the required information can be located by citizens with less time and effort. Tourism is one of the main focused areas of e-Gov development strategy because it is one of the major profitable industries. Significant efforts have been devoted by governments to improve tourism services. However, the current e-Gov tourism services are limited to simple online presentation; intelligent e-Gov tourism services are highly desirable. Personalization techniques, particularly recommendation systems, are the most promising techniques to deliver personalized e-Gov (Pe-Gov) tourism services. This study proposes ontology-based personalized e-Gov tourism recommender system framework, which would enable tourism information seekers to locate the most interesting destinations and find the most preferable attractions and activities with less time and effort. The main components of the proposed framework and some outstanding features are presented along with a detailed description of a scenario.
Al- Hassan, MW, Lu, H & Lu, J 2010, 'A Framework for Delivering Personalized E-Government Tourism Services', WEBIST 2010 - Proceedings of the 6th International Conference on Web Information Systems and Technology, WEBIST, Institute for Systems and Technologies of Information, Control and Communication, Valencia, Spain, pp. 263-270.
E-government (e-Gov) has become one of the most important parts of government strategies. Significant efforts have been devoted to e-Gov tourism services in many countries because tourism is one of the major profitable industries. However, the current e-Gov tourism services are limited to simple online presentation of tourism information. Intelligent e-Gov tourism services, such as the personalized e-Gov (Pe-Gov) tourism services, are highly desirable for helping users decide âwhere to go, and what to do/seeâ amongst massive number of destinations and enormous attractiveness and activities. This paper proposes a framework of Pe-Gov tourism services using recommender system techniques and semantic ontology. This framework has the potential to enable tourism information seekers to locate the most interesting destinations with the most suitable activities with the least search efforts. Its workflow and some outstanding features are depicted with an example.
Al Qahtani, A, Lu, H & Lu, J 2010, 'Towards Semantic-Aware and Ontology-Based e-Government Service Integration - An Applicative Case Study of Saudi Arabia's King Abdullah Scholarship Program', Advances in Intelligent Decision Technologies - Proceedings of the Second KES International Symposium IDT 2010, The Second KES International Symposium IDT, Springer-Verlag, Baltimore, USA, pp. 403-411.View/Download from: Publisher's site
By improving the quality of e-government services by enabling access to services across different government agencies through one portal, services integration plays a key role in e-government development. This paper proposes a conceptual framework of ontology based e-government service integration, using Saudi Arabia's King Abdullah Scholarship Program (SAKASP) as a case study. SAKASP is a multi-domain program in which students must collect information from various Ministries to complete applications and the administering authority must verify the information supplied by the Ministries. The current implementation of SAKASP is clumsy because it is a mixture of online submission and manual collection and verification of information; its time-consuming and tedious procedures are inconvenient for the applicants and inefficient for the administrators. The proposed framework provides an integrated service by employing semantic web service (SWS) and ontology, improving the current implementation of SAKASP by automatically collecting and processing the related information for a given application. The article includes a typical scenario that demonstrates the workflow of the framework. This framework is applicable to other multi-domain e-government services.
Nguyen, T, Lu, H & Lu, J 2010, 'Ontology-Style Web Usage Model for Semantic Web Applications', Proc. of the 10th International Conference on Intelligent Systems Design and Applications (ISDA 2010), International Conference on Intelligent Systems Design and Applications, IEEE, Egypt, pp. 784-789.View/Download from: Publisher's site
Current semantic recommender systems aim to exploit the website ontologies to produce valuable web recommendations. However, Web usage knowledge for recommendation is presented separately and differently from the domain ontology, this leads to the complexity of using inconsistent knowledge resources. This paper aims to solve this problem by proposing a novel ontology-style model of Web usage to represent the non-taxonomic visiting relationship among the visited pages. The output of this model is an ontology-style document which enables the discovered web usage knowledge to be sharable and machine-understandable in semantic Web applications, such as recommender systems. A case study is presented to show how this model is used in conjunction of the web usage mining and web recommendation. Two real-world datasets are used in the case study.
Zhang, G, Lu, H & Zhang, G 2010, 'A new hybrid evolutionary algorithm with quasi-simplex technique', 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010, International Conference on Machine Learning and Cybernetics, IEEE, Qingdao, China, pp. 1811-1816.View/Download from: Publisher's site
This paper proposes a new parallel search algorithm using an evolutionary algorithm and quasi-simplex techniques (EAQST) for non-linear constrained function optimization. EAQST produces the offspring in parallel by using the Gaussian mutation, the Cauchy
Guo, Y, Jin, J, Zhu, J, Lu, H & Xu, W 2010, 'Performance Analysis of a Linear Synchronous Motor with HTS Bulk Magnets', Proceedings of the XIX International Conference on Electrical Machines (ICEM10), International Conference on Electrical Machines, IEEE, Rome, Italy, pp. 1-5.View/Download from: Publisher's site
The study on high temperature superconducting (HTS) bulk magnets and their application has attracted much attention. This paper presents the performance analysis of a linear synchronous motor (LSM) with HTS bulk magnets for driving an HTS maglev vehicle model. Finite element magnetic field analysis is carried out for computing the magnetic field distribution and key parameters of the LSM, and an equivalent electrical circuit is then applied to predict the motor performance, showing that the presented motor is appropriate for driving the model vehicle. The LSM has been prototyped, installed in the maglev vehicle model, and tested for validating the theoretical analysis.
Guo, Y, Zhu, J, Wang, Y, Lu, H & Lin, Z 2010, 'Performance Analysis of a Permanent Magnet Claw Pole SMC Motor with a Nonlinear Inductance Model', Proceedings of Asia-Pacific Symposium on Applied Electromagnetics and Mechanics (APSAEM2010), Japan Society of Applied Electromagnetics and Mechanics, Kuala Lumper, Malaysia, pp. 348-351.
Nguyen, T & Lu, H 2009, 'Experimental Investigation of PSO Based Web User Session Clustering', The International Conference on SOft Computing and PAttern Recognition, International Conference on Soft Computing and Pattern Recognition, IEEE Computer Society, Malacca, Malaysia, pp. 647-652.View/Download from: Publisher's site
Web user session clustering is very important in web usage mining for web personalization. This paper proposes a particle swarm optimization (PSO) based sequence clustering approach and presents an experimentally investigation of the PSO based sequence clustering methods, which use three original PSO variants and their corresponding variants of a hybrid PSO with real value mutation. The investigation was conducted in 45 test cases using five web user session datasets extracted from a real world web site. The experimental results of these methods are compared with the results obtained from the traditional k-means clustering method. Some interesting observations have been made. In the most of test cases under consideration, the PSO and PSO-RVM methods have better performance than the k-means method. Furthermore, the PSO-RVM methods show better performance than the corresponding PSO methods in the cases in which the similarity measure function is more complex.
Al- Hassan, MW, Lu, H & Lu, J 2009, 'A Framework for Delivering Personalized e-Government Services from a Citizen-Centric Approach', Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services (iiWAS2009), Information Integration and Web-based Applications and Services, ACM in cooperation with the Austrian Computer Society, Kuala Lumpur, Malaysia, pp. 434-438.View/Download from: Publisher's site
E-government is becoming more attentive towards providing intelligent personalized online services to citizens so that citizens can receive better services with less time and effort. This paper proposes a new conceptual framework for delivering personalized e-government services to citizens from a citizen-centric approach, called Pe-Gov service framework. This framework outlines the main components and their interconnections. Detailed explanations about these components are given and the special features of this framework are highlighted. This framework has the potential to outperform the existing e-Gov service systems as illustrated by two real life examples.
Guo, Y, Xu, W, Zhu, J, Lu, H, Wang, Y & Jin, J 2009, 'Design and Analysis of a Linear Induction Motor Drive for a Prototype HTS Maglev Transportation System', Proceedings of IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, IEEE, Chengdu, China, pp. 81-84.
This paper investigates the design and analysis of a linear induction motor (LIM) drive for a prototype transportation system, which is levitated by the interaction force between high temperature superconducting (HTS) bulks placed on the ground and permanent magnets (PMs) mounted on the bottom of the vehicle, while the driving force is provided by a linear induction motor system on the side of the prototype vehicle. An equivalent electrical circuit is applied to predict the motor characteristics and the computation results show that the proposed LIM drive system is appropriate for driving the HTS maglev transportation prototype.
Guo, Y, Zhu, J, Dorrell, D, Lu, H & Wang, Y 2009, 'Development of a Claw Pole Permanent Magnet Motor with a Molded Low-Density Soft Magnetic Composite Stator Core', 2009 IEEE ENERGY CONVERSION CONGRESS AND EXPOSITION, VOLS 1-6, IEEE Energy Conversion Congress and Exposition, IEEE, San Jose, CA, pp. 703-710.
Guo, Y, Zhu, J, Lu, H, Wang, Y & Jin, J 2009, 'Design and Analysis of a Permanent Magnet Motor with Soft Magnetic Composite Core for Driving Dishwasher Pump', Proceedings of IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, IEEE, 138, pp. 138-141.View/Download from: Publisher's site
This paper presents the development of a permanent magnet motor with soft magnetic composite (SMC) stator core for driving a dishwasher pump. The unique properties of the SMC, such as 3D magnetic isotropy and molding production technique, have been taken into account in the design. 3D magnetic field finite element analysis is carried out to accurately compute the motor parameters and an equivalent electrical circuit is derived to predict the motor performance. Analysis results show that the developed motor is appropriate.
Zheng, L, Jin, J, Guo, Y, Lu, H & Zhu, J 2009, 'Design and Electromagnetic Analysis of a HTS Linear Synchronous Motor', Proceedings of IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, IEEE, Chengdu, China, pp. 5-10.
High temperature superconducting (HTS) linear synchronous motor (LSM) integrated with HTS magnetic levitation system, can realize self-levitation and self-guidance without any sliding friction, which will have important applications in many fields such as electromagnetic aircraft launch system, maglev transportation. This paper presents the design and electromagnetic analysis of a HTS LSM, which is levitated by a magnetic levitation system consisting of HTS bulkspermanent magnet (PM) guideways. Numerical analysis and magnetic field finite element analysis (FEA) methods are applied to analyze the thrust, levitation and guidance force characteristics, and the electromagnetic performance of HTS LSM under no-load and load situations are studied with the analysis results are given. Finally, the primary motor running testing results are provided.
Dong, J, Wang, J, Sun, D & Lu, H 2008, 'The Research of Software Product Line Engineering Process and Its Integrated Development Environment Model', Proceedings of International Symposium on Computer Science and Computational Technology, International Symposium on Computer Science and Computational Technology, the IEEE Computer Society, Shanghai, China, pp. 66-71.View/Download from: Publisher's site
In order to realize the industrialization production of software, people have carried out research on and analysis the software product line architecture of the growing maturity, component technology and development methods for product line. In this paper, a novel software engineering process model is proposed based on the modern industrial production systems and automated production method: that is ldquoN-life-cycle modelrdquo. Based on this new model, not only integrated software engineering environment model and framework have been proposed, which are based on the product line development process model, but also study systematically on theirs implementation. "N-life-cycle model" and "integrated software engineering environment model based on the product line" which are set up in the article are brand-new open models possessing modern manufacturing production characteristic. The models can impel the research development quickly of product line engineering and product line software engineering environment towards the industrialisation and automatization of the software industry.
Dong, J, Zeng, F, Wang, J & Lu, H 2009, 'Price Forecasting of Supply Chain Product Based on Dynamic Fractal Dimension', International Conference on Information Management, Innovation Management and Industrial Engineering - Vol 2, International Conference on Information Management, Innovation Management and Industrial Engineering, IEEE Computer Society, Taipei, Taiwan, pp. 153-156.View/Download from: Publisher's site
Supply chain management (SCM) is an emerging field that has commanded attention and support from the industrial community. Demand forecast taking inventory into consideration is an important issue in SCM. This paper presents a novel computerized system for implementing the forecasting activities required in SCM. It can help the supply chain enterprises find hidden forms, trends and relationships in the date of supply chain by the dynamic fractal dimension of fractal theory, find that dynamic fractal dimension not only can overcome the delay of the existing technical analysis on the price forecasts, and but also can instruct the supply chain product prices in advance.
Wang, J, Che, J, Liang, J & Lu, H 2008, 'A Fuzzy Pattern Recognition System Based on SOM Clustering and RBF Neural Networks for Prediction', Proceedings of the 6th International Conference on Information Collection and Data Disposal.
Wang, J, Liang, H, Sun, D & Lu, H 2008, 'An Integrated Early Warning Information System based on the GRA and ANN for Air Pollution and Meteorological Disease', Proceedings of the 3nd China Workshop on Information System for Crisis Response and Management & the Post-Conference Meeting to the International Disaster Reduction Conference, the 3rd International China Workshop on Information Systems for Crisis Response and Management (ISCRAM-CHINA) and the 4th International Symposium on Geo-Information for Disaster Management (Gi4DM), Harbin Engineering University Press, Harbin, China, pp. 553-558.
Wang, J, Wang, H, Sun, D & Lu, H 2008, 'Ontology-Based Assembly Design and Information Sharing for Supply Chain Information,', Proceedings of the 38th International Conference on Computers and Industrial Engineering, Hawaii International Conference on System Sciences, Organizing committee of the 38th International Conference on Computers and Industrial Engineering, Beijing, China, pp. 1220-1226.
Nowadays, supply chain information is increasingly used by many entities around the world. Then, the need of sharing information from different sources is an obvious consequence from such proliferation of systems. Unfortunately, integrating supply chain information is not a trivial issue, because data and knowledge exchange among users of supply chain information systems presents many challenges. We must deal with the heterogeneity problem, which increase complexity of integration approaches. This paper discusses issues related to the use of ontologies in the development of supply chain information systems and proposes the creation of software components from diverse ontologies as a way to share data and supply chain information.
Wang, J, Wang, Y, Sun, D & Lu, H 2008, 'Supply Chain Safety Stock Quantity's Fractal Forecast and Study', Proceedings of the 4th International Conference on Wireless Communications, Networking, and Mobile Computing, International Conference on Wireless Communications, Networking and Mobile Computing, the IEEE Conference and Cistom Publishing Department, Dalian, China, pp. 1-4.View/Download from: Publisher's site
Safety stock, a very important composing of the stock management, which is significant for reducing the cost and increasing interests for an enterprise, is the base of the setting of enterprise's stocks, so it is crucial for stock management to presume a rational and exact SS. In this paper, introducing fractal theory, a new method based on the fractal collage theorem and the iterative process of fractal interpolation functions was proposed to forecast the safety stock. Based on the fractal collage theorem, it uses the iterated function system whose attractor is close to the historical data to establish the fractional function, and set the forecasting model proposed according to the iterative process of fractional function. Then by an example the rationality and reliability of this method is verified, and the result show that it is helpful indeed to set the reasonable safety stock for enterprise.
Wang, J, Wen, J, Sun, D & Lu, H 2008, 'Detecting and Disposing Abnormal Signal Outliers with Masking Effect by Using Data Accumulated Generating Operation', Proceedings of the 2008 Congress on Image and Signal Processing, Congress on Image and Signal Processing, the IEEE Computer Society, Hainan, pp. 426-430.View/Download from: Publisher's site
In this paper, we study a signal processing problem which concentrates on outlier detection and data mining in order to rediscover some useful information. Moreover, the great difficulty of the subject is caused by both the external environment and the internal mechanism. In the external, owing to limit of impersonal condition, such as, realistic disturbance from noise signal can not avoid. In the internal, the masking effect appears in outlying observation so as to produce some unreasonable results by data analysis. Therefore, based not only on the requirement of external quality assurance schemes, but also on internal quality control where screening for outliers should probably be part of the procedure for our main goal. As a consequence, we have applied data accumulated generating operation and sample median test for solving the conundrum.
Wang, J, Zheng, G, Liang, J & Lu, H 2008, 'A Extended Cultural Algorithm Based on Fuzzy Adaptive PSO', ADVANCES IN BUSINESS INTELLIGENCE AND FINANCIAL ENGINEERING, International Conference on Business Intelligence and Financial Engineering, ATLANTIS PRESS, Changsha Univ Sci & Technol, Sch Econ & Management, Changsha, PEOPLES R CHINA, pp. 591-+.
Wang, J, ZHU, S, SUN, D & Lu, H 2008, 'Combining Principal Component Analysis and Fuzzy Cluster Analysis for Chinas Oil Security', Proceedings of the 2008 2nd International Symposium on Information Technologies and Applications in Education (ISITAE2008), International Symposium on Information Technologies and Applications in Education, American Publishing Association of Technology and Education, Xiamen, China, pp. 287-292.View/Download from: Publisher's site
It is significant to evaluate the situations of oil security for taking national oil security measures, because very important relationships exist between the oil security and the national security. A novel combined method is presented in this paper, which is based on using principal component analysis (PCA) and fuzzy cluster analysis (FCA) modeling. It is used for assessing oil security. In this paper, the data of 13 years is taken as sample and 10 indexes are selected to constitute an evaluation system for oil security. PCA is used for reducing the dimensions of indexes, and the new indexes are formed for oil security. Then FCA is used for classifying the 13 samples. At last a dialed analysis is made and some suggestions are put forward to ensure oil security.
Wang, J, Zhu, W, Sun, D & Lu, H 2008, 'Application of SVM Combined with Mackov Chain for Inventory Prediction in Supply Chain', Proceedings of the 4th International Conference on Wireless Communications, Networking, and Mobile Computing, International Conference on Wireless Communications, Networking and Mobile Computing, IEEE, Dalian, China, pp. 1-4.View/Download from: Publisher's site
The aim of this paper is to predict the inventory of the relevant upstream enterprises in supply chain. The support vector machine, a novel artificial intelligence-based method developed from statistical learning theory, is adopted herein to establish a short-term stage forecasting model. However, take the fact into account that demand signal is affected by variant random factors and behaves big uncertainty, the predicted accuracy of SVM is not approving when the data show great randomness. It is obligatory that we present Markov chain to improve the predicted accuracy of SVM. This combined model takes advantage of the high predictable power of SVM model and at the same time take advantage of the prediction power of Markov chain modeling on the discrete states based on the SVM modeling residual sequence. Then we use the statistical data of the output of the gasoline of China from Feb-06 to Dec-07 for a validation of the effectiveness of the above model.
Wang, J, Zhu, W, Sun, D & Lu, H 2008, 'Application of SVM Combined with Mackov Chain for Inventory Prediction in Supply Chain', 2008 4TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND MOBILE COMPUTING, VOLS 1-31, 4th International Conference on Wireless Communications, Networking and Mobile Computing, IEEE, Dalian, PEOPLES R CHINA, pp. 6531-+.
Wang, J-Z, Che, J-X, Liang, J-Z & Lu, H 2008, 'Research on Real Estate Early Warning System based on Decision Tree and Fuzzy Recognition Theory', FIFTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 3, PROCEEDINGS, 5th International Conference on Fuzzy Systems and Knowledge Discovery, IEEE COMPUTER SOC, Jinan, PEOPLES R CHINA, pp. 414-+.View/Download from: Publisher's site
Wen, J, Wang, J & Lu, H 2008, 'An improved new approach for electric capacity forecasting based on historical data of GDP', 2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008, IEEE Conference in Industrial Electronics and Applications, IEEE, Singapore, pp. 2487-2491.View/Download from: Publisher's site
Prediction is important for the electricity capacity management. Accurate prediction can help the policymaker make correct decision and promote the decision making quality. For improving an accuracy of prediction, in this paper, we adopt the theory of Grey prediction to develop a new forecasting approach that integrates historical data of the gross domestic products (GDP) into an electric capacity forecasting. We adopted Grey prediction as a forecasting means because of its fast calculation with as few as four data inputs needed. As a result, our study considered that Wu and Chen proposed a modeling method of the improved grey relational analysis and main shows that the general Grey model, GM (1, 1), which is an especial case, is adequate to handle an electrical power system. In this study, the prediction is improved significantly by applying the transformed Grey model and the concept of average system slope. The adaptive value of a in the Grey differential equation is obtained quickly with the average system slope technique. In such a way, the wastage of electric consumption can be avoided. That is, it is another achievement of virtual electric power plant. Â©2008 IEEE.
Guo, Y, Dou, Y, Zhu, J, Lu, H & Jin, J 2008, 'Numerical Magnetic Field Analysis and Parameter Computation of a PM Synchronous Generator', ICEMS 2008: PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON ELECTRICAL MACHINES AND SYSTEMS, VOLS 1- 8, 11th International Conference on Electrical Machines and Systems, WORLD PUBLISHING CORPORATION, Huazhong Univ Sci & Technol, Wuhan, PEOPLES R CHINA, pp. 2866-+.
Guo, Y, Dou, Y, Zhu, J, Lu, H & Jin, J 2008, 'Numerical Magnetic Field Analysis and Performance Computation of a Permanent Magnet Synchronous Generator', Proceedings of the 11th International Conference on Electrical Machines and Systems (ICEMS), World Publishing Corporation, Wuhan, China, pp. 2866-2869.
Guo, Y, Wang, X, Zhu, J & Lu, H 2008, 'Development of a Wound Rotor Brushless Doubly Fed Machine Based on Slot MMF Harmonics', Proceedings of the 43rd IEEE Industry Applications Society Annual Meeting, IEEE Industry Applications Society Annual Meeting, IEEE, USA, Edmonton, Canada, pp. 1-5.View/Download from: Publisher's site
In the rotor winding magnetomotive force (MMF) of an ac machine, there exist so-called slot harmonics which appear in pairs and the lower order harmonic of each pair rotates in the opposite direction against the fundamental component. In addition, the slot harmonics have the same winding factor as the fundamental component. Based on these properties, this paper develops a brushless doubly fed machine (BDFM) with wound rotor. The machine consists of two stator windings with p1 and p2 pole-pairs, respectively. The rotor has a normal symmetrical multi-phase winding, in which rotating MMFs with p1 and p2 pole-pairs are induced by their stator counterparts. When the number of rotor slots equals the sum p1 and p2, the two MMFs rotate in opposite directions with respect to the rotor, satisfying the requirement of a BDFM. The major advantage of such a machine is that for both p1 and p2 pole-pair MMFs the winding factor is as high as that of the fundamental component, leading to high utilization of rotor winding and electrical efficiency.
Guo, Y, Zhu, J, Chen, J, Su, SW, Lu, H & Jin, J 2008, 'Performance Analysis of a Claw Pole PM Motor', Proceedings of Australasian Universities Power Engineering Conference 2008, Australasian Universities Power Engineering Conference, University of New South Wales, Sydney, Australia, pp. 1-5.
This paper presents the performance analysis of a three-phase three-stack permanent magnet (PM) claw pole motor by using an improved phase variable model, which has been developed for accurate and efficient performance simulation of PM brushless dc motors. The improved model can take into account the effect of magnetic saturation and rotor position dependence of key parameters including back electromagnetic force, winding inductance, cogging torque and core loss, which are obtained from time-stepping nonlinear magnetic field finite element analysis (FEA). The presented model has been implemented in Simulink environment and employed to simulate the dynamic and steady-state performance of the three-phase three-stack PM claw pole motor with soft magnetic composite stator. Parameter computation and performance simulation are validated by experiments on the motor prototype.
Guo, Y, Zhu, J, Lu, H, Lin, Z, Wang, S & Jin, J 2008, '3-D Vector Magnetic Properties of SMC Material for Advanced Field Analysis of SMC Machine', Proceedings of Australasian Universities Power Engineering Conference, Australasian Universities Power Engineering Conference, University of New South Wales, Sydney, Australia, pp. 1-4.
In a rotating electrical machine or the T-joints of a multiphase transformer, the magnetic flux is basically three dimensional (3-D) and rotational. This paper presents the 3-D vector magnetic properties of soft magnetic composite (SMC) materials for advanced field analysis of electromagnetic devices with SMC core, which is particularly developed for application of electrical machines with complex structure and 3-D flux. The 3-D magnetic reluctivity tensor is derived from the magnetic measurements on a cubic SMC sample by using a 3-D magnetic property tester. The tensor consists of both diagonal and offdiagonal terms and the latter account for the effect of rotating flux. Practical techniques for employing the vector magnetic properties in field analysis are reviewed and discussed.
Zhu, J, Lu, H, Guo, Y & Lin, Z 2008, 'Development of Electromagnetic Linear Actuators for Micro Robots (Invited)', Proceedings of the 11th International Conference on Electrical Machines and Systems (ICEMS), International Conference on Electrical Machines and Systems, World Publishing Corporation, Wuhan, China, pp. 3673-3679.
For micro robotic applications, piezoelectric actuators are widely used, whereas electromagnetic actuators are not favored because of the complex structures and difficult fabrication. On the other hand, electromagnetic actuators have many merits that are suitable for robotic applications, such as relatively large displacement or stroke and no need of high voltage power supply. With the recent fast development of micro precision machining techniques, the fabrication of complex structures is no longer a problem. This paper presents our recent study of developing electromagnetic actuators for micro robotic applications, including a comparison between piezoelectric and electromagnetic actuators, design of two types of electromagnetic actuators, and fabrication and testing of a moving magnet tubular linear actuator.
Wang, J, Wu, L & Lu, H 2007, 'Special Periods Peak Load Analysis and Superior Forecasting Method Based on LS-SVM', Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition, the International Conference on Wavelet Analysis and Pattern Recognition, IEEE, Crowne Plaza Hotel, Beijing, China, pp. 245-253.View/Download from: Publisher's site
Abstract: People often try to smooth or eliminate load outliers all together in traditional power load forecasting. This, however, could result in the loss of important hidden information. In other words, the power load outliers themselves may be particular important. Hence there is a beforehand estimate to change and characteristic of power load, especially in power load outliers or peak load, is a precondition of power system carry through economy dispatch, reduce production cost and prevent widespread blackout or collapse on electric system. In this paper propose a novel method for special periods power peak load detection, mining and forecasting. It incorporates the characteristic of high level load and maximum peak load analysis with optimum forecasting algorithm based on support vector machine. The validity of the method is proved by real data calculation.
Guo, Y, Chen, J, Zhu, J, Lu, H, Lu, H & Jin, J 2007, 'Development of a PM Linear Motor for Driving HTS Maglev Vehicle', Proceedings of International Conference on Electrical Machines and Systems (ICEMS07), International Conference on Electrical Machines and Systems, IEEE, Seoul, pp. 824-827.View/Download from: Publisher's site
The phenomenon that a permanent magnet (PM) over a high temperature superconductor (HTS) bulk can produce strong levitation force with self-stabilizing feature has attracted strong interest of application in maglev transportation systems, in which a linear motion drive is an obvious advantage. This paper presents the development of a PM linear synchronous motor drive for a small-scale prototype vehicle which is levitated by PM-HTS bulks. Magnetic field finite element analyses are conducted to compute accurately the key motor parameters such as winding flux, back electromotive force (emf), inductance and cogging force. The steady state characteristic of the motor is predicted by using the classic phasor voltage equation, which can provide a reasonable result if the fundamental components of the applied voltage, back emf and current are dominant. A Matlab/Simulink-based model, capable of considering the dependence of key parameters on the mover position, is built to predict effectively the motor's dynamic performance under a brushless DC (BLDC) control scheme. The simulated results show that the developed linear motor can drive the HTS maglev vehicle prototype at the desired speed.
Guo, Y, Zhu, J, Liu, D, Lu, H & Wang, S 2007, 'Application of multi-level mult-domain modelling in the design and analysis of a PM transverse flux motor with SMC core', the 7th International Conference on Power Electronics and Drive Systems (PEDS07), International Conference on Power Electronics and Drive Systems, IEEE, Bangkok, Thailand, pp. 27-31.View/Download from: Publisher's site
This paper presents the design and analysis of a permanent magnet (PM) transverse flux motor with soft magnetic composite (SMC) core by applying multi-level multi-domain modeling. The design is conducted in two levels. The upper level is composed of a group of equations which describe the electrical and mechanical characteristics of the motor. The lower level consists of two domains: electromagnetic analysis and thermal calculation. The initial design, including structure, materials and major dimensions, is determined according to existing experience and empirical formulae. Then, optimization is carried out at the system level (the upper level) for the best motor performance by optimizing the structural dimensions. To successfully deal with such a multi-level multi-domain optimization problem, an effective modeling with both high computational accuracy and speed is required. For accurately computing the key motor parameters, such as back electromotive force, winding inductance and core loss, magnetic field finite element analysis is performed. The core loss in each element is stored for effective thermal calculation, and the winding inductance and back EMF are stored as a look-up table for effective analysis of the motor's dynamic performance. The presented approach is effective with good accuracy and reasonable computational speed.
Guo, Y, Zhu, J, Lu, H, Wang, S & Jin, J 2007, 'Performance Analysis of an SMC Transverse Flux Motor with Modified Double-sided Stator and PM Flux Concentrating Rotor', Proceedings of International Conference on Electrical Machines and Systems (ICEMS07), International Conference on Electrical Machines and Systems, IEEE, Seoul, Korea, pp. 1553-1556.View/Download from: Publisher's site
This paper presents the design and performance analysis of a three-phase three-stack transverse flux motor with a modified double-sided stator and a permanent magnet (PM)flux concentrating rotor. Both stator and rotor cores employ SOMALOYtrade 500, a new soft magnetic composite (SMC) material specially developed for electrical machine application. By taking advantage of the unique properties of SMC, such as the magnetic isotropy, the motor is designed with three-dimensional (3D) magnetic flux path. To accurately compute the motor parameters and performance, improved formulations are applied in combination with 3D magnetic field finite element analysis. The designed motor shows superior characteristics to laminated machines.
Zhang, G, Lu, H, Li, G & Hie, X 2006, 'A new hybrid real-coded gentetic algorithm and application in dynamic economic dispatch', Proceedings of sixth world congress on intelligent control, World congress on intelligent control and automation, IEEE, Dalian, China, pp. 3627-3631.
Zhao, N, Lu, H & Song, Y 2006, 'Risk assessment of strategies using total time on test transformation', Proceedings of IEEE power engineering society, IEEE Power engineering society, IEEE Power engineering society, Montreal, Quebec, Canada, pp. 1-8.
Zhang, G, Zhang, G, Lu, J & Lu, H 2006, 'Environmental/economic dispatch using genetic algorithm and fuzzy number ranking method', Applied Artificial Intelligence - Proceedings of the 7th International Fuzzy Logic and Intelligent Technologies in Nuclear Science Conference FLINS, International Fuzzy Logic and Intelligent technologies in Nuclear Science Conference, World Scientific, Genova, Italy, pp. 59-65.
Guo, Y, Chen, J, Zhu, J, Lu, H, Lu, H & Jin, J 2006, 'An Improved Phase Variable Model for Dynamic Performance Analysis of a PM Claw Pole SMC Motor with Brushless DC Control', Proceedings of the Australasian Universities Power Engineering Conference, Australasian Universities Power Engineering Conference, Victoria University, Melbourne, Australia, pp. 1-6.
Guo, Y, Jin, J, Zhu, J & Lu, H 2006, 'Design and Performance Evaluation of a PM Linear Synchronous Motor for Maglev Transportation', Proceedings of the Australasian Universities Power Engineering, Australasian Universities Power Engineering Conference, Victoria, University, Melbourne, Australia, pp. 1-5.
Guo, YG, Zhu, JG, Lin, ZW, Zhong, JJ, Lu, HY & Wang, SH 2006, '3D magnetic reluctivity tensor of soft magnetic composite material', 12th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2006.View/Download from: Publisher's site
Soft magnetic composite (SMC) materials are particularly suitable for construction of electrical machines with complex structure and 3D magnetic flux. For design and analysis of such 3D flux machines, 3D magnetic properties of the magnetic materials should be properly determined, modeled and applied for calculating the magnetic field distribution, parameters and performance. This paper presents the 3D magnetic property measurement and determination of 3D reluctivity tensor of SMC. The reluctivity tensor is a key factor for the numerical analysis of magnetic field in a 3D flux SMC motor. ©2006 IEEE.
Lu, H, Zhu, J & Guo, Y 2006, 'Calculation of differential inductances for nonlinear dynamic analysis of a tubular linear PM actuator', INTERMAG 2006 - IEEE International Magnetics Conference, p. 766.View/Download from: Publisher's site
Yan, Y, Zhu, J, Guo, Y & Lu, H 2006, 'Modelling and Simulation of Direct Torque Controlled PMSM Drive System Incorporating Structural and Saturation Saliencies', Proceedings of the 41st IEEE Industry Applications Society Annual Meeting, IEEE Industry Applications Society Annual Meeting, IEEE Industry Applications Society, IEEExplorer, pp. 76-83.View/Download from: Publisher's site
The direct torque controlled (DTC) permanent magnet synchronous motor (PMSM) drive has become competitive compared with other types of drive systems because of its simple and sensorless control algorithm. The application of the system, however, is handicapped by the difficulty of starting under full load due to the unknown initial rotor position. This paper presents a nonlinear model of PMSMs which incorporates both the structural and saturation saliencies to enable the numerical simulation of initial rotor position detection algorithms. In this model, the phase inductances are expressed by Fourier series as functions of the stator current and rotor position. The inductances of a surface mounted PMSM is measured with different rotor positions and DC offset currents, which emulate the effect of the three phase stator currents. By using the proposed model, the DTC PMSM is simulated and the results are compared with those obtained by the PMSM model in the Simulink library. With the model, an initial rotor position estimation scheme using voltage pulses is investigated by numerical simulation. The scheme is also experimentally tested and the results are compared with the inductance variation to verify the validity of the method. The effectiveness of the scheme to estimate the initial rotor position for the testing SPMSM is analyzed and verified by numerical simulation before physical implementation
Zheng, L, Jin, J, Guo, Y, Lu, H & Zhu, J 2006, 'Technology and Development of High Temperature Superconducting Linear Motors Conference', Proceedings of the Australasian Universities Power Engineering Conference, Australasian Universities Power Engineering Conference, Victoria, University, Melbourne, Australia, pp. 1-6.
Zhang, G, Lu, H, Li, G & Zhang, G 2005, 'Dynamic Economic Load Dispatch Using Hybrid genetic algorithm and the Method of Fuzzy Number Ranking', Proceedings at 2005 International Conference on Machine learning and Cybernetics, International Conference on Machine Learning and Cybernetics, IEEE, Guangzhou, China, pp. 2472-2477.View/Download from: Publisher's site
This paper proposes a new economic load dispatch model that considers cost coefficients with uncertainties and the constraints of ramp rate. The uncertainties are represented by fuzzy numbers, and the model is known as fuzzy dynamic economic load dispatch model (FDELD). A novel hybrid genetic algorithm with quasi-simplex techniques is proposed to handle the FDELD problem. The algorithm creates offspring by using generic operation and quasi-simplex techniques in parallel. The quasi-simplex techniques consider two potential optimal search directions in generating prospective offspring. One direction is the worst-opposite direction, which is used in the conventional simplex techniques, and the other is the best-forward direction, which is a ray from the centroid of a polyhedron whose vertexes are all the points but the best one towards the best point of the simplex. In addition, in order to reserve more fuzzy information, the fuzzy number ranking method is used to optimize the cost function, avoiding the lost some useful information by getting /spl lambda/-level set. The experimental study shows that FDELD is more practical model; the algorithm and techniques proposed are very efficient to solve FDELD problem.
Guo, Y, Zhu, J & Lu, H 2005, 'Design of SMC Motors Using Hybrid Optimization Techniques and 3D FEA with Increasing Accuracy', The Proceedings of the 8th International Conference on Electrical Machines and Systems, International Conference on Electrical Machines and Systems, International Academic Publisher/Beijing World Publishing Cooperation, Nanjing, China, pp. 2296-2301.
This paper presents the design and analysis of a three-phase three-stack permanent magnet claw pole motor with soft magnetic composite (SMC) stator core. 3D finite element analysis (FEA) of magnetic field is performed to accurately calculate key motor parameters and performance. Combined optimization techniques and 3D FEA with increasing accuracy are applied to effectively reduce the computational time. The designed motor has been fabricated and tested. The theoretical calculations are validated by the experimental results on the prototype.
Guo, YG, Zhu, JG & Lu, H 2005, 'Design and analysis of a permanent magnet claw pole/transverse flux motor with SMC core', Proceedings of the International Conference on Power Electronics and Drive Systems, pp. 1413-1418.
This paper presents the design and analysis of a claw pole/transverse flux motor (CPTFM) with soft magnetic composite (SMC) core and permanent magnet flux-concentrating rotor. Three-dimensional magnetic field finite element analysis is conducted to accurately calculate key motor parameters such as winding flux, back electromotive force, winding inductance, and core loss. Equivalent electric circuit is derived under optimum brushless DC control condition for motor performance prediction, and computer search techniques are applied for design optimization. All these computations and analyses have been implemented in a commercial software ANSYS for development of the SMC CPTFM prototype.
Yan, Y, Zhu, J, Lu, H, Guo, Y & Wang, S 2005, 'A PMSM Model Incorporating Structural and Saturation Saliencies', The Proceedings of the 8th International Conference on Electrical Machines and Systems, International Conference on Electrical Machines and Systems, International Academic Publisher/Beijing World Publishing Cooperation, Nanjing, China, pp. 194-199.
Sensorless permanent magnet synchronous motor (PMSM) drive systems have become very attractive due to their advantages, such as the reduction of hardware complexity and hence the reduced system cost and increased reliability. In order to accurately determine the rotor position required for correct electronic commutation, various methods have been proposed. Among them, the most versatile makes use of the structural and/or magnetic saturation saliencies of the PMSM. This paper presents a non-linear model for PMSMs with the saliencies. The phase inductances of a PMSM are measured and expressed by Fourier series at different rotor positions according to their patterns. The dynamic performance of the PMSM is simulated and compared with that based on a model without considering saliency to verify the effectiveness of the proposed model.
Lu, H & Zhang, G 2004, 'A New Parallel Search Algorithm for Non-Linear Function Operation', Proceedings of 2nd International Conference on Information Technology and Applications, International Conference on Information Technology and Applications, Macquarie Scientific Publishing, Harbin, China, pp. 338-343.
Lu, HY & Zhang, G 2004, 'A new parallel search algorithm for non-linear function optimization', Proceedings of the Second International Conference on Information Technology and Applications (ICITA 2004), pp. 109-114.
This paper proposes a new parallel search algorithm using evolutionary programming and quasi-simplex technique (EPQS). EPQS produces the offspring from three ways in parallel: 1) Using the Gaussian mutation, 2) Using the Cauchy mutation, and 3) Using the quasi-simplex techniques. The quasi-simplex technique uses the ideal of classical simplex technique and produces four prospective individuals by using the reflection, expansion and compression operations. EPQS selects the parents for the next generation from all the parents and offspring. EPQS takes the diversity of offerings into consideration by generating the offspring from as many as possible ways while it maintains a substantial convergence rate. Experimental studies on six typical benchmark functions have shown that the proposed algorithm is more effective than the competing algorithms.
Lu, H 2003, 'A New Thought about Modelling of Bilevel Programming Problems', The Third International Conference on Electronic Business (ICEB 2003). Business Paradigms: Strategic Transformation and Partnership, International Conference on e-Business, National University of Singapore, Singapore, pp. 1-9.
Lu, H, Zhu, J & Ramsden, VS 2000, 'Comparison of experimental techniques for determination of stray capacitances in high frequency transformers', Record of IEEE Power Electronics Specialist Conference (PESC 00), IEEE, Ireland, pp. 1645-1650.
Lu, HY, Zhu, J, Ramsden, VS & Hui, SYR 1999, 'Measurement and modeling of stray capacitances in high frequency transformers', PESC Record - IEEE Annual Power Electronics Specialists Conference, 30th Annual IEEE Annual Power Electronics Specialists Conference, Charleston, SC, USA, pp. 763-768.
This paper proposes an approach to incorporate stray capacitances into a dynamic circuit model of high frequency transformers. The measuring techniques for stray capacitances in high frequency transformers and inductors are presented. A 500W transformer in a full bridge inverter operating at 25 kHz has been simulated with the new model and the theoretical results are confirmed by experiments.
Zhu, JG, Lu, HY, Ramsden, VS & Tran, K 1997, 'Temperature dependence of magnetic hysteresis of soft ferrites', NON-LINEAR ELECTROMAGNETIC SYSTEMS, 8th International Symposium on Non-Linear Electromagnetic Systems (ISEM Braunschweig), I O S PRESS, BRAUNSCHWEIG, GERMANY, pp. 495-498.
Lu, HY, Zhu, JG, Hui, SYR & Ramsden, VS 1998, 'A generalized dynamic transformer circuit model including all types of core losses', PEDES 1998 - 1998 International Conference on Power Electronic Drives and Energy Systems for Industrial Growth, pp. 978-983.View/Download from: Publisher's site
© 1998 IEEE. This paper describes a generalized dynamic transformer circuit model that includes all types of core losses, nonlinear magnetic characteristics, skin effects of eddy currents in the core, and thermal effects on hysteresis of core materials. A TLM-based transform with variable time steps is employed in the simulation. This model can provide an accurate prediction of transformer performance and core losses and is suitable for simulation of high frequency switching mode converters using transformer isolated outputs. Some interesting issues such as stray capacitance, are discussed. Simulations of a 500 W transformer in a full bridge inverter operated at 15 kHz and 25 kHz have been confirmed by experiments.
- Tsinghua University, China
- Beijing Institute of Technology, China
- Dongbei University of Finance and Economics, China
- Beijing Wuzi University, China