Modified Levy-based Particle Swarm Optimization (MLPSO) with Boost Converter for Local and Global Point Tracking

Chanuri Charin (), Dahaman Ishak, Muhammad Ammirrul Atiqi Mohd Zainuri (), Baharuddin Ismail, Turki Alsuwian () and Adam R. H. Alhawari ()
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Chanuri Charin: School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Pulau Pinang 14300, Malaysia
Dahaman Ishak: School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Pulau Pinang 14300, Malaysia
Muhammad Ammirrul Atiqi Mohd Zainuri: Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Selangor 43600, Malaysia
Baharuddin Ismail: Faculty of Electrical Engineering and Technology, Universiti Malaysia Perlis, Perlis 02600, Malaysia
Turki Alsuwian: Electrical Engineering Department, College of Engineering, Najran University, Najran 11001, Saudi Arabia
Adam R. H. Alhawari: Electrical Engineering Department, College of Engineering, Najran University, Najran 11001, Saudi Arabia

Energies, 2022, vol. 15, issue 19, 1-30

Abstract: This paper presents a modified Levy particle swarm optimization (MLPSO) to improve the capability of maximum power point tracking (MPPT) under various partial shading conditions. This method is aimed primarily at resolving the tendency to trap at the local optimum particularly during shading conditions. By applying a Levy search to the particle swarm optimization (PSO), the randomness of the step size is not limited to a specific value, allowing for full exploration throughout the power-voltage (P-V) curve. Therefore, the problem such as immature convergence or being trapped at a local maximum power point can be avoided. The proposed method comes with great advantages in terms of consistent solutions over various environmental changes with a small number of particles. To verify the effectiveness of the proposed idea, the algorithm was tested on a boost converter of a photovoltaic (PV) energy system. Both simulation and experimental results showed that the proposed algorithm has a high efficiency and fast-tracking speed compared to the conventional HC and PSO algorithm under various shading conditions. Based on the results, it was found that the proposed algorithm successfully converges most rapidly to the global maximum power point (GMPP) and that the tracking of GMPP under complex partial shading is guaranteed. Furthermore, the average efficiency for all test conditions was 99% with a tracking speed of 1.5 s to 3.0 s and an average output steady-state oscillation of 0.89%.

Keywords: photovoltaic; partial shading condition; global maximum power point; local maximum power point; dynamic irradiance change (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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