An improved particle swarm optimization based maximum power point tracking strategy with variable sampling time |
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| Affiliation: | 1. Department of Electrical of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia;2. Power Electronics and Renewable Energy Research Laboratory (PEARL), Department of Electrical of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia;1. Laboratory of Energy Systems Modeling, o.kraa@mselab.org, university of Biskra, Algeria;2. Industrial Hybrid Vehicle Applications, ayadmy@gmail.com, France;3. FCLab FR CNRS 3539 FEMTO-ST UMR CNRS 6174, mohamed.becherif@utbm.fr, UTBM Belfort, France;1. Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia;2. Karachi Institute of Economics and Technology, Karachi 75190, Pakistan;3. Faculty of Electrical Engineering, Universiti Teknologi Malaysia, UTM 81310, Skudai, Johor Bahru, Malaysia;1. Institute of Power Engineering, Department of Electrical Power Engineering, College of Engineering, Universiti Tenaga National, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysia;2. Electrical Engineering Dept., Faculty of Engineering, Fayoum University, Fayoum, Egypt;3. Institute of Sustainable Energy, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysia;4. Department of Electrical and Electronic Engineering, University of Peradeniya, Galaha Rd, 20400, Sri Lanka;1. Department of Digital Systems, University of Piraeus, Greece;2. Technological Education Institute of Piraeus, Department of Automation, Greece;3. Agricultural University of Athens, Department of Natural Resources and Agricultural Engineering, 75, Iera Odos Str., Athens 11855, Greece;4. Group Building Environmental Studies, Physics Department, University of Athens, Athens, Greece;1. Electrical Engineering Department, Faculty of Engineering, Jouf University, Saudi Arabia;2. Electrical Power and Machine Department, Faculty of Engineering, Zagazig University, Egypt;3. College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Saudi Arabia;4. Electrical Engineering Department, Faculty of Engineering, Minia University, Egypt;5. Electrical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum, Egypt |
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| Abstract: | This paper presents an improved maximum power point tracking (MPPT) strategy for photovoltaic (PV) systems based on particle swarm optimization (PSO). The capability of the PSO algorithm to cope with partially shaded conditions (PSCs) is the primary motivation of this research. Unlike conventional PSO-based MPPT systems, a variable sampling time strategy (VSTS) based on the investigation of the dynamic behavior of converter current is deployed to increase system tracking time. The performance of the proposed system is evaluated using MATLAB simulation and experimentation, in which a digital signal controller is used to implement the proposed algorithm on a real boost converter connected to a PV simulator. The main advantage of the proposed algorithm is fast and accurate performance under different conditions, including PSCs. |
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| Keywords: | Maximum power point tracking Particle swarm optimization Partially shaded condition Variable sampling time |
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