Dr. Angelo A. Beltran Jr. was born in Quezon City, Philippines. He received the B.S. degree in Electronics and Communications Engineering from the AMA Computer University, Philippines, the M.Eng. degree in Electronics and Communications Engineering and the Ph.D. in Electronics Engineering, all in Mapua University in Manila, Philippines. He is currently with the Department of Electronics Engineering at Adamson University, Philippines. From 2015 – 2017, he was a Visiting Scholar in the Department of Electrical Engineering, Chung Yuan Christian University, Taiwan. He is a Professional Electronics Engineer in the Philippines and a former Research and Development Engineer for Power Electronics and Motor Control in Taiwan. He had authored or co-authored over 27 papers in proceedings and international journals and serves as a reviewer for several conferences, engineering and scientific journals. He received the Best Paper Award in Chung Yuan Christian University, First Place Best Department Chair Research Award, and Third Place Best Department Chair Research Award, at the Technological Institute of the Philippines in 2016, 2012, and 2011, respectively. His research interests include renewable energy systems, computational intelligence based control and estimation, embedded systems, applied swarm intelligence algorithms and evolutionary computing techniques, self organizing and adaptive control systems, chaos theoretic concepts and applications.
Title of Talk: Chaos Enhanced PSO with Adaptive Parameters and Its Application to MPPT
Abstract: An enhanced particle swarm optimization (PSO) scheme is presented in this talk to further improve the performance of the standard PSO algorithm. The enhanced PSO is based on the technique of chaos search to overcome the problems of stagnations, being trapped in a local optima, and the risk of premature convergence. A type constriction is incorporated to help strengthen the stability and quality of convergence, and an adaptive learning coefficients is applied to further enhance the exploitation and exploration search characteristics of the algorithm. A several well known benchmark functions are operated to verify the effectiveness of the enhanced PSO. Test performance is compared with the other popular population-based algorithms in the literature. Results show that the enhanced PSO technique exhibits faster rate of convergence, escapes being trapped in a local minima, avoids premature convergence, and stagnation in a high-dimensional problem space. In addition, the validity of the enhanced PSO is further demonstrated by a fuzzy logic-based maximum power point tracking control technique for a stand-alone solar photovoltaic system.
Keywords: Chaos, Fuzzy Logic, Maximum Power Point Tracking, Particle Swarm Optimization, PhotoVoltaic