Gregory P Hunter received the BEng (Hons) degree from the University of Sydney, Australia in 1975 and the PhD degree from the University of Technology Sydney, Australia in 1998, both in electrical engineering. He has worked in the power electronics industry for most of his career both as an employee and a consultant, specialising in the design of switched mode power supplies, uninterruptible power supplies, grid-connect inverters, and motor drives using both PWM inverters and cycloconverters. From 1998 to 2013 he was a Senior Research Fellow at the University of Technology, Sydney and is now a Visiting Fellow. His research interests include sensorless motor drives, wind turbines, electric wheelchair controllers and power electronics for implanted medical devices. Details of current research can be found on my personal website.
Power Electronics for Industry
Chai, R, Ling, SS, Hunter, G, Tran, YH & Nguyen, HT 2014, 'Brain-Computer Interface Classifier for Wheelchair Commands Using Neural Network With Fuzzy Particle Swarm Optimization', IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 5, pp. 1614-1624.View/Download from: UTS OPUS or Publisher's site
This paper presents the classification of a three-class mental task-based braincomputer interface (BCI) that uses the Hilbert-Huang transform for the features extractor and fuzzy particle swarm optimization with cross-mutated-based artificial neural network (FPSOCM-ANN) for the classifier. The experiments were conducted on five able-bodied subjects and five patients with tetraplegia using electroencephalography signals from six channels, and different time-windows of data were examined to find the highest accuracy. For practical purposes, the best two channel combinations were chosen and presented. The three relevant mental tasks used for the BCI were letter composing, arithmetic, and Rubiks cube rolling forward, and these are associated with three wheelchair commands: left, right, and forward, respectively. An additional eyes closed task was collected for testing and used for onoff commands. The results show a dominant alpha wave during eyes closure with average classification accuracy above 90%. The accuracies for patients with tetraplegia were lower compared to the able-bodied subjects; however, this was improved by increasing the duration of the time-windows.TheFPSOCM-ANNprovides improved accuracies compared to genetic algorithm-based artificial neural network (GA-ANN) for three mental tasks-based BCI classifications with the best classification accuracy achieved for a 7-s time-window: 84.4% (FPSOCM-ANN) compared to 77.4% (GA-ANN).More comparisons on feature extractors and classifiers were included. For two-channel classification, the best two channels were O1 and C4, followed by second best at P3 and O2, and third best at C3 and O2. Mental arithmetic was the most correctly classified task, followed by mental Rubik's cube rolling forward and mental letter composing.
Ramsden, VS, Watterson, PA, Hunter, G, Zhu, J, Holliday, B, Lovatt, H, Wu, W, Kalan, B, Collocott, SJ, Dunlop, J, Gwan, P & Mecrow, BC 2001, 'High Performance Electric Machines for Renewable Energy Generation and Efficient Drives', J. of Renewable Energy, vol. 22, pp. 159-167.View/Download from: UTS OPUS or Publisher's site
Pham, DH, Hunter, G, Li, L & Zhu, JG 2015, 'Advanced microgrid power control through grid-connected inverters', Proceedings of the Advanced microgrid power control through grid-connected inverters, IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), IEEE, Brisbane, Australia, pp. 1-6.View/Download from: Publisher's site
Recently, researches on microgrids have attracted more and more attention all over the world. Numerous studies and experiments about microgrid converters have been done. This paper presents a method to improve the power quality by controlling the real and reactive power flow between the main grid and the microgrid through the grid-interfaced inverter. Firstly, the desired values of Id and Iq can be obtained by transforming the currents into d-q stationary frame and decoupling the active and reactive power for the use of both traditional proportional-integral (PI) and feedforward control. Besides the traditional direct power control (DPC), model predictive control (MPC) has emerged as one powerful method to obtain the active and reactive power needed directly with some clear advantages to the system such as higher reliability and enhanced stability. Both PI with feedforward and MPC methods to control the grid-interfaced inverter have been studied and simulated in this paper.
Pham, H, Hunter, G, Li, L & Zhu, J 2013, 'Feedforward Decoupling Control Method in Grid-interfaced Inverter', 23nd Australasian Universities Power Engineering Conference, Australasian Universities Power Engineering Conference, IEEE, Hobart Australia, pp. 1-5.View/Download from: UTS OPUS or Publisher's site
Recently, microgrid has been studied and applied widely all over the world. More and more experimental microgrids are being connected to the utility grid. This paper presents an improvement in the real and reactive power control of three-phase grid-interfaced inverter for microgrid applications. Based on the traditional PI feedback current control, the desirable values of P and Q can be achieved by controlling the currents in d-q stationary frame. Moreover, the feed forward control method also brings some advantages to the systems such as higher reliability and enhanced stability. One of the most important improvements is to decouple the real and reactive power, i.e. P and Q are controlled separately. In this paper, the controller with feedforward algorithm has been simulated and shows some promiscuous results.
Chai, R, Ling, SS, Hunter, G, Tran, YH & Nguyen, HT 2013, 'Classification of wheelchair commands using brain computer interface: comparison between able-bodied persons and patients with tetraplegia', Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Osaka, Japan, pp. 989-992.View/Download from: UTS OPUS or Publisher's site
This paper presents a three-class mental task classification for an electroencephalography based brain computer interface. Experiments were conducted with patients with tetraplegia and able bodied controls. In addition, comparisons with different time-windows of data were examined to find the time window with the highest classification accuracy. The three mental tasks used were letter composing, arithmetic and imagery of a Rubiks cube rolling forward; these tasks were associated with three wheelchair commands: left, right and forward, respectively. An eyes closed task was also recorded for the algorithms testing and used as an additional on/off command. The features extraction method was based on the spectrum from a Hilbert-Huang transform and the classification algorithm was based on an artificial neural network with a fuzzy particle swarm optimization with cross-mutated operation. The results show a strong eyes closed detection for both groups with average accuracy at above 90%. The overall result for the combined groups shows an improved average accuracy of 70.6% at 1s, 74.8% at 2s, 77.8% at 3s, 79.6% at 4s and 81.4% at 5s. The accuracy for individual groups were lower for patients with tetraplegia compared to the able-bodied group, however, does improve with increased duration of the time-window.
Pham, H, Hunter, G, Li, L & Zhu, J 2012, 'Microgrid Topology for Different Applications in Vietnam', 22nd Australasian Universities Power Engineering Conference, Australasian Universities Power Engineering Conference, IEEE, Bali, Indonesia, pp. 1-6.View/Download from: UTS OPUS
This paper proposes a common microgrid including distributed energy resources (DER) like diesel generation, photovoltaic cells (PV cells), wind turbine or other renewable energy sources (RES), an energy storage system and both ac and dc loads. This micro grid topology is applicable to various areas such as city buildings, a factory, a household, a small village or a rural farm. Case study for several areas will also be presented in this paper. For different cases, depending on the strength of the utility grid, the number of available DER and user convenience, ac, dc or hybrid microgrid can be applied. To improve the reliability of the microgrid, an energy storage system made of batteries connected in series is established to support the bus voltage immediately when the microgrid is disconnected from the main grid and when in stand-alone mode. This energy storage system can be charged from the main bus voltage through a converter. Overall, the microgrid is controlled by a microgrid control center. As an example, establishment and operation of microgrid for a rural farm will be shown.
Prasetya, S, Li, L, Hunter, G & Zhu, J 2012, 'Prospect of Renewable Energy Utilization in a Indonesian City through Microgrid Approach', Proceedings of the Green Smart Grid System, Australasian Universities Power Engineering Conference, AUPEC, Bali Indonesia, pp. 1-6.View/Download from: UTS OPUS
Indonesia is a developing country which has an increasing yearly energy consumption, particularly in cities. This paper describes the study of renewable energy expansion for an urban area in Indonesia. The goal of the study is to propose a recommendation utilizing local renewable energy resources in a city using Jakarta as the model. First, literature survey summaries as the foundation research is presented in this paper. Finally, a microgrid concept to overcome energy needs particularly electricity and urban problems by exploiting local potentials in a developing city is proposed.
Jafari, M, Hunter, G & Zhu, J 2012, 'A New Topology of Multi-Input Multi-Output Buck-Boost DC-DC Converter for Microgrid Applications', 2012 IEEE International Conference on Power and Energy (PECON 2012), IEEE International Conference on Power and Energy, IEEE, Kota Kinabalu, Malaysia, pp. 286-291.View/Download from: UTS OPUS or Publisher's site
This paper proposes a new topology for multi-input multi-output Buck-Boost DC-DC converter based on the concept of matrix and Buck-Boost Converters to interface efficiently between DC loads and various DC power sources in a microgrid. A power sharing process can be applied among the input sources and output loads to control the contribution of each input sources in supplying the output loads. The input sources can be used in various power and voltage ranges and the output voltages can change from values greater than the maximum to lower than the minimum input voltages. Theoretical analysis and simulation results are presented.
Nguyen, QK, Nguyen, HT, Hunter, G & Ha, QP 2012, 'FPGA-based Sensorless PMSM Drive using Reduced-Order Extended Kalman Filter', Proc. 2012 International Conference on Control, Automation and Information Sciences, International Conference on Control, Automation and Information Sciences, IEEE, Saigon Hochiminh City Vietnam, pp. 164-169.View/Download from: UTS OPUS or Publisher's site
This paper presents the design and evaluation of a Field Programmable Gate Array (FPGA)-based speed sensorless controller for Permanent Magnet Synchronous Motor (PMSM). The estimation of the rotor position and speed is achieved by using a parallel reduced-order Extended Kalman Filter (EKF) to alleviate the need of physical sensors. Compared with the traditional method of EKF, the system order is reduced, the process of iteration of speed estimation algorithm is greatly simplified and it is easy to realize the digital system. To achieve this objective, a comparison is made between the parallel reduced-order EKF, full-order EKF and sliding mode observer (SMO). The developed controller has been implemented in a FPGA-based environment and the very high speed integrated circuit-hardware description language (VHDL) is adopted to describe advantageous features of the proposed control system. The validity of the approach is verified through simulation results based on the Modelsim/Simulink co-simulation method.
Chai, R, Hunter, G, Ling, SS & Nguyen, HT 2012, 'Real-Time Microcontroller based Brain Computer Interface for Mental Task Classifications using Wireless EEG Signals from Two Channels', Proceedings of the Ninth IASTED International Conference on Biomedical Engineering, IASTED International Conference on Biomedical Engineering, ACTA Press, Innsbruck, Austria, pp. 336-342.View/Download from: UTS OPUS or Publisher's site
A brain computer interface (BCI) using electroencephalography (EEG) to measure brain activities could provide severely disabled people with alternative means of control and communication. In a practical system, portability, low power and real-time operation are the keys requirements. This could be accomplished by using an embedded microcontroller based system. The main contribution of this paper shows the development of a real-time BCI prototype system to classify groups of mental tasks based on such a system. The relevant mental tasks used are mental arithmetic, figure rotation, letter composing, visual counting and eyes closed action. Moreover, the system uses a separate two channels only wireless EEG measurement module with the active positions at parietal and occipital lobes. The result shows the wireless EEG module has a good performance with a CMRR of more than 95dB. In addition, the size of the module is small (36x36 mm2) and current consumption is low enough to operate off a 3V coin cell battery. The mental tasks were classified using a feed-forward back-propagation artificial neural network (ANN) trained with the Levenberg-Marquardt algorithm. An accuracy of around 70% was achieved with bit rate at around 0.4 bits/trial for six subjects tested to select between three separate mental tasks.
Chai, R, Ling, SH, Hunter, GP & Nguyen, HT 2012, 'Mental task classifications using prefrontal cortex electroencephalograph signals', Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE Xplore, San Diego, CA, USA, pp. 1831-1834.View/Download from: UTS OPUS or Publisher's site
For an electroencephalograph (EEG)-based brain computer interface (BCI) application, the use of gel on the hair area of the scalp is needed for low impedance electrical contact. This causes the set up procedure to be time consuming and inconvenient for a practical BCI system. Moreover, studies of other cortical areas are useful for BCI development. As a more convenient alternative, this paper presents the EEG based-BCI using the prefrontal cortex non-hair area to classify mental tasks at three electrodes position: Fp1, Fpz and Fp2. The relevant mental tasks used are mental arithmetic, ringtone, finger tapping and words composition with additional tasks which are baseline and eyes closed. The feature extraction is based on the Hilbert Huang Transform (HHT) energy method and the classification algorithm is based on an artificial neural network (ANN) with genetic algorithm (GA) optimization. The results show that the dominant alpha wave during eyes closed can still clearly be detected in the prefrontal cortex. The classification accuracy for five subjects, mental tasks vs. baseline task resulted in average accuracy is 73% and the average accuracy for pairs of mental task combinations is 72%. © 2012 IEEE.
Chai, R, Ling, SH, Hunter, GP & Nguyen, HT 2012, 'Toward fewer EEG channels and better feature extractor of non-motor imagery mental tasks classification for a wheelchair thought controller', Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE Xplore, San Diego, CA, USA, pp. 5266-5269.View/Download from: UTS OPUS or Publisher's site
This paper presents a non-motor imagery tasks classification electroencephalography (EEG) based brain computer interface (BCI) for wheelchair control. It uses only two EEG channels and a better feature extractor to improve the portability and accuracy in the practical system. In addition, two different features extraction methods, power spectral density (PSD) and Hilbert Huang Transform (HHT) energy are compared to find a better method with improved classification accuracy using a Genetic Algorithm (GA) based neural network classifier. The results from five subjects show that using the original eight channels with three tasks, accuracy between 76% and 85% is achieved. With only two channels in combination with the best chosen task using a PSD feature extractor, the accuracy is reduced to between 65% and 79%. However, the HHT based method provides an improved accuracy between 70% and 84% for the classification of three discriminative tasks using two EEG channels. © 2012 IEEE.
Chai, R, Ling, SS, Hunter, G & Nguyen, HT 2012, 'Mental non-motor imagery tasks classifications of brain computer interface for wheelchair commands using genetic algorithm-based neural network', Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN 2012), IEEE International Joint Conference on Neural Networks, IEEE, Brisbane, Australia, pp. 978-984.View/Download from: UTS OPUS or Publisher's site
A genetic algorithm (GA)-based neural network classification in the application of brain computer interface (BCI) for controlling a wheelchair is presented in this paper. This study uses an electroencephalography (EEG) as a non-invasive BCI approach to discriminate three non-motor imagery mental tasks for disabled individuals who may have difficulty in using BCI based motor imagery tasks. The three tasks classification is mapped into three wheelchair movements: left, right and forward and the relevant combination mental tasks used in this study are mental arithmetic, letter composing, Rubik's cube rolling, visual counting, ringtone imagery and spatial navigation. The results show the proposed system provides good classification performance after selecting the most effective of three discriminative tasks across combination of the different non-motor imagery mental tasks for the five subjects tested. The average classification accuracy is between 76% and 85 %, with information transfer rates varies from 0.5 to 0.8 bits per trial.
Hunter, G 2011, 'A Sensorless PMSM Fundamental Mode Controller with High Dynamic Full Range Speed Control', IECON 2011 - 37th Annual Conference on IEEE Industrial Electronics Society, Annual Conference of the IEEE Industrial Electronics Society, IEEE, Melbourne, Australia, pp. 1728-1733.View/Download from: UTS OPUS or Publisher's site
Previous methods of sensorless vector control of the PMSM operate by indirectly determining the rotor position usually from the back EMF or from saliency detection using HF injection. This paper introduces an entirely new method of sensorless control of the PMSM where instead of trying to determine the rotor position, the basic torque loop structure is rearranged to remove the need for position information allowing fundamental mode sensorless operation at zero speed. As a bonus, the new structure allows the use of a low bandwidth linear current controller for the control of torque, provides instantaneous response (within one sample period) to changes in the command torque and makes available instantaneous values of speed and position at all speeds including zero for use by outer speed and position feedback loops. Combined with an outer speed loop controller, it allows speed control of high dynamic performance at all speeds including zero. The new torque control method is called Feed Forward Torque Control.
Yan, Y, Zhu, J, Hunter, G & Guo, Y 2006, 'Initial Rotor Position Estimation of a Surface Mounted PMSM', Proceedings of the 9th International Conference on Electrical Machines, International Conference on Electrical Machines and Systems, IEEJ (Institute of Electrical Engineers Japan) Industry Applications Society, Nagasaki, Japan, pp. 1-6.
Watterson, PA, Collocott, SJ, Dunlop, J, Gwan, P, Hunter, G, Kalan, B & Lovatt, H 2005, 'High Torque Brushless DC Motor for a Valve Actuator', Proceedings of the Eighhth International Conference on Electrical Machines and Systems, International Conference on Electrical Machines and Systems, International Academic Publisher, Beijing World Publishing Corporation, Nanging, China, pp. 153-158.View/Download from: UTS OPUS
Dane, R, Watterson, PA, Holliday, B, Evans, C, Ramsden, VS, Ramaswamy, V & Hunter, G 2002, 'Marine Electric Hybrid Power Systems', Proceeding of the Pacific 2002 International Maritime Conference, Pacific 2002 International Maritime Conference, Institution of Engineers, Australia, Sydney, Australia, pp. 484-491.View/Download from: UTS OPUS
Cui, P, Zhu, J, Ha, QP, Hunter, G & Ramsden, VS 2001, 'Simulation of Non-linear Switched Reluctance Motor Drives with PSIM', Proc. of the Int. Conference on Electrical Machines and Systems (ICEMS'2001), International Conference on Electrical Machines and Systems, Proc. of the Int. Conference on Electrical Machines and Systems (ICEMS'2001), Shenyang China, pp. 1061-1064.