He, T, Zhu, J, Lu, DDC, Zheng, L, Aghdam, MM & Zhang, J 2017, 'Comparison study of electric vehicles charging stations with AC and DC buses for bidirectional power flow in smart car parks', Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society, Annual Conference of the IEEE Industrial Electronics Society, IEEE, Beijing, China, pp. 4609-4614.View/Download from: UTS OPUS or Publisher's site
© 2017 IEEE. This paper presents a comparison study of AC bus and DC bus topologies for electric vehicle (EV) battery chargers in smart car parks. The two charger systems are compared from two aspects: the features from the user side and the power quality on the grid side. Considering the system reliability, cost, size and conversion stage, the pros and cons of AC and DC buses systems are introduced and summarized. To compare the electric parameters of the power quality, model predictive control (MPC) algorithm is proposed and applied to operate in grid-to-vehicle (G2V), vehicle-to-grid (V2G), vehicle-for-grid (V4G) modes. An cost function is designed in MPC to track the active and reactive powers references provided by the main grid. The controllers for AC and DC buses to determine the the active and reactive powers relationships among the grid, charging stations and the storage system are designed, respectively. In terms of power ripple, total harmonic distortion (THD) and execute time, comparative simulations of the two topologies are performed under the proposed operations in Matlab/Simulink. The obtained results show that both the two system structures can operate effectively and the commands from the grid and EVs customers can be satisfied. Fast dynamic performance and good steady-state response are achieved in two systems. However, the power ripple, THD, and the execute time obtained from DC bus topology are much lower compared with the AC bus system.
Mahdavi, M, Aguilera, R, Li, L & Zhu, J 2017, 'Fuzzy-Based Self-Tuning Model Predictive Direct Power Control of Grid-Connected Power Converters', 2017 20th International Conference on Electrical Machines and Systems (ICEMS), International Conference on Electrical Machines and Systems, IEEE, Sydney, NSW, Australia, pp. 1-6.View/Download from: UTS OPUS or Publisher's site
This paper proposes a self-tuning model predictive direct power control (MPDPC) strategy for power flow control and power quality improvement in grid-connected power converters. At each sampling instant, a fuzzy logic controller is used to determine online the best weighting factor values for a given operating point. These values are then used to solve the multi-objective optimal control problem associated to the MPDPC. The optimal solution that minimizes the multi-objective cost function is chosen as the input (power switch state). The proposed method is examined through a case study and verified numerically via MAT LAB SIMULINK. A comparative study is conducted to demonstrate the effective performance of this approach. As a result of the proposed weighting factor online tuning, an improved performance in terms of total harmonic distortion and average switching frequency is attained when compared with fixed weighting factors.
Mahdavi Aghdam, M, Li, L, Zhu, J, He, T & Zhang, J 2017, 'Time-delayed model predictive direct power control for vehicle to grid and grid to vehicle applications', Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society, Annual Conference of the IEEE Industrial Electronics Society, IEEE, Beijing, China, pp. 4662-4667.View/Download from: UTS OPUS or Publisher's site
© 2017 IEEE. This paper presents a time-delayed model predictive control for power converters used in vehicle to grid and grid to vehicle systems. Finite-based model predictive control has proven to be an alternate digital control method for power converters. However, there are some real-time implementation issues, including specifically time delay, that have to be addressed in order to achieve the system reliability and stability as well as better performance. The proposed method compensates the delay time arising from measuring, calculating, and applying the optimal control sequence in the digital processor. In this way, the delay time is considered in the system input and optimal switching states are applied to the converter once they are available. The proposed method is studied through two benchmarks and verified numerically via MATLAB/Simulink.
Zhang, J, Li, L, He, T, Mahdavi Aghdam, M & Dorrell, DG 2017, 'Investigation of direct matrix converter working as a versatile converter (AC/AC, AC/DC, DC/AC, DC/DC conversion) with predictive control', 43rd Annual Conference of the IEEE Industrial Electronics Society (IECON 2017), Annual Conference of the IEEE Industrial Electronics Society, IEEE, Beijing, China.View/Download from: UTS OPUS or Publisher's site
The three-phase direct matrix converter has been researched exclusively as a direct AC/AC converter, being a competitive alternative to the conventional AC/DC/AC converter. Other possibilities of the matrix converter such as AC/DC, DC/AC and DC/DC conversion still remain unexplored. This paper firstly explores these possibilities and puts forward a concept of the versatile converter. With one matrix converter, different conversion purposes can be accomplished as required. The matrix converter based conversion has some advantages compared with other converters. Model predictive control (MPC) is applied in this work to control the matrix converter to perform the required conversion goals. A generalized model is obtained for all types of conversion in this work. With MPC, different objectives and constraints can be easily included in the control scheme. In addition, the observers are used to reduce the number of voltage and current sensors. Simulation results verify the effectiveness and feasibility of AC/DC, DC/AC and DC/DC conversion with the matrix converter.
Beiranvand, A, Mahdavi Aghdam, M, Li, L, Zhu, S & Zheng, J 2016, 'Finding the Optimal Place and Size of an Energy Storage System for the Daily Operation of Microgrids Considering Both Operation Modes Simultaneously', 2016 IEEE International Conference on Power System Technology, POWERCON 2016, International Conference on Power System Technology (POWERCON), http://ieee-powercon.org/accepted-papers-at-ieee-pes-powercon-2016/, Wollongong.View/Download from: UTS OPUS or Publisher's site
Mahdavi Aghdam, M, Li, L & Zhu, J 2016, 'A Model Predictive Control of Parallel Inverters for Distributed Generations in Microgrids', 2016 IEEE International Conference on Power System Technology (POWERCON), International Conference on Power System Technology (POWERCON), IEEE, Wollongong, Australia.View/Download from: UTS OPUS or Publisher's site
Mahdavi Aghdam, M, Li, L & Zhu, J 2016, 'A Model Predictive Power Control Method with Longer Prediction Horizon for Distributed Power Generations', 14th International Conference on Control, Automation, Robotics and Vision (ICARCV), 2016, International Conference on Control, Automation, Robotics and Vision, IEEE, Phuket, Thailand.View/Download from: UTS OPUS or Publisher's site
Mahdavi Aghdam, M, Li, L, Zhu, J & Palizban, O 2016, 'Finite Control Set Model Predictive Control-A Powerful Control Algorithm for Grid-Connected Power Converters', Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016, IEEE Conference on Industrial Electronics and Applications, IEEE, Hefei, China.View/Download from: UTS OPUS or Publisher's site
Mahdavi Aghdam, M, Li, L, Zhu, J & Mekhilef, S 2015, 'An adaptive Neuro-Fuzzy Controller for maximum power point tracking of photovoltaic systems', IEEE Region 10 Annual International Conference, Proceedings/TENCON, IEEE Tencon (IEEE Region 10 Conference), IEEE, MACAU, pp. 1-6.View/Download from: UTS OPUS or Publisher's site
This paper presents a high performance tracking method for maximum power generated by photovoltaic (PV) systems. Based on adaptive Neuro-Fuzzy inference systems (ANFIS), this method combines the learning abilities of artificial neural networks and the ability of fuzzy logic to handle imprecise data. It is able to handle non-linear and time varying problems hence making it suitable for accurate maximum power point tracking (MPPT) to ensure PV systems work effectively. The performance of the proposed method is compared to that of a fuzzy logic based MPPT algorithm to demonstrate its effectiveness.