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A hybrid intelligent GMPPT algorithm for partial shading PV system |
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| Affiliation: | 1. College of Electrical Engineering and Automation, Tianjin University, Tianjin 300071, China;2. School of Electrical Engineering, Tianjin University of Technology, Tianjin 300384, China;1. Faculty of Electric Power Engineering, Kunming University of Science and Technology, 650500, Kunming, China;2. College of Engineering, Shantou University, 515063, Shantou, China;3. College of Electric Power, South China University of Technology, 510640, Guangzhou, China;4. Department of Electrical Engineering & Electronics, University of Liverpool, Liverpool, L69 3GJ, United Kingdom;5. Guangzhou Shuimuqinghua Technology Co. LTD., 510898, Guangzhou, China;1. Sustainable Energy Technologies Center, King Saud University, Riyadh 11421, Saudi Arabia;2. Electrical Engineering Department, Mansoura University, Mansoura, Egypt;3. Eectrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia;1. SET Laboratory, Electronics Department, Blida 1 University, BP 270 Blida, Algeria;2. Electrical Engineering Department, Faculty of Technology, University of M''sila, BP 166 Ichbilia, Algeria;3. Electronic Engineering Department, Universitat Politècnica de Catalunya, C/ Jordi Girona 1-3, Campus Nord UPC, 08034 Barcelona, Spain |
| Abstract: | Maximum power extraction for PV systems under partial shading conditions (PSCs) relies on the optimal global maximum power point tracking (GMPPT) method used. This paper proposes a novel maximum power point tracking (MPPT) control method for PV system with reduced steady-state oscillation based on improved particle swarm optimization (PSO) algorithm and variable step perturb and observe (P&O) method. Firstly, the grouping idea of shuffled frog leaping algorithm (SFLA) is introduced in the basic PSO algorithm (PSO–SFLA), ensuring the differences among particles and the searching of global extremum. Furthermore, adaptive speed factor is introduced into the improved PSO to improve the convergence of the PSO–SFLA under PSCs. And then, the variable step P&O (VSP&O) method is used to track the maximum power point (MPP) accurately with the change of environment. Finally, the superiority of the proposed method over the conventional P&O method and the standard PSO method in terms of tracking speed and steady-state oscillations is highlighted by simulation results under fast variable PSCs. |
| Keywords: | Global maximum power point tracking (GMPPT) Particle swarm optimization (PSO) Shuffled frog leaping algorithm (SFLA) Variable step P&O (VSP&O) Adaptive speed factor Photovoltaic (PV) system Under partial shading conditions (PSCs) |
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