Smart bacterial foraging algorithm based controller for speed control of switched reluctance motor drives |
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| Affiliation: | 1. Young Researchers and Elite Club, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran;2. Department of Electrical Engineering. Faculty of Engineering, University of Isfahan, Isfahan, Iran;1. The Key Laboratory of Automotive Engineering, Xihua University, Chengdu 610039, People׳s Republic of China;2. School of Electromechanical and Automobile Engineering, Yantai University, Yantai 264005, People׳s Republic of China;1. Department of Electrical and Electronics Engineering, Karunya Institute of Technology and Sciences, Karunya University, Karunya Nagar, Coimbatore 641114, India;2. Dr. N.G.P Institute of Technology, Dr. N.G.P Nagar, Kalapatti Road, Coimbatore 641048, India;1. Department of Electrical Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt;2. Department of Electric Power and Machines, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt;1. MIPS Laboratory - University of Haute Alsace, 4 Rue des Frères Lumière, 68093 Mulhouse, France;2. GREEN Laboratory - INSA de Strasbourg, 24 Boulevard de la Victoire, 67084 Strasbourg, France;3. University Lille, Centrale Lille, Arts et Metiers ParisTech, HEI, EA 2697 - L2EP – Laboratoire d’Electrotechnique et d’Electronique de Puissance, 59000 Lille, France;4. LGECO Laboratory - INSA de Strasbourg, 24 Boulevard de la Victoire, 67084 Strasbourg, France |
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| Abstract: | In this paper, a innovative methodology for Switched Reluctance Motor (SRM) drive control using Smart Bacterial Foraging Algorithm (SBFA) is presented. This method mimics the chemotactic behavior of the E. Coli bacteria for optimization. The proposed algorithm uses individual and social intelligences, so that it can search responses among local optimums of the problem adaptively. This method is used to tune the coefficients of a conventional Proportion–Integration (PI) speed controller for SRM drives with consideration of torque ripple reduction. This matter is done by applying the proposed algorithm to a multi-objective function including both speed error and torque ripple. This drive is implemented using a DSP-based (TMS320F2812) for an 8/6, 4-kW SRM. The simulation and experimental results confirm the improved performance of adjusted PI controller using SBFA in comparison with adjusted PI controller using standard BFA. Excellent dynamic performance, reduced torque ripple and current oscillation can be achieved when the coefficients of PI controller are optimized by using SBFA. |
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| Keywords: | Smart bacteria foraging algorithm Speed control Switched reluctance motor Torque ripple |
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