Maximum Power Point Tracking for Photovoltaic Systems Operating under Partially Shaded Conditions Using SALP Swarm Algorithm

Lilia Tightiz, Saeedeh Mansouri, Farhad Zishan (), Joon Yoo () and Nima Shafaghatian
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Lilia Tightiz: School of Computing, Gachon University, 1342 Seongnamdaero, Seongnam 13120, Korea
Saeedeh Mansouri: Faculty of Electrical and Computer Engineering, Babol Noushirvaniy University of Technology, Babol 4714873113, Iran
Farhad Zishan: Department of Electrical Engineering, Sahand University of Technology, Tabriz 5513351996, Iran
Joon Yoo: School of Computing, Gachon University, 1342 Seongnamdaero, Seongnam 13120, Korea
Nima Shafaghatian: Electrical Engineering Departments, Zanjan University, Zanja 387914537, Iran

Energies, 2022, vol. 15, issue 21, 1-17

Abstract: This article presents a new method based on meta-heuristic algorithm for maximum power point tracking (MPPT) in photovoltaic systems. In this new method, the SALP Swarm Algorithm (SSA) is used instead of classic methods such as the Perturb and Observe (P&O) method. In this method, the value of the duty cycle is optimally determined in an optimization problem by SSA in order to track the maximum power. The objective function in this problem is maximizing the output power of the photovoltaic system. The proposed method has been applied on a photovoltaic system connected to the load, taking into account the effect of partial shade and different atmospheric conditions. The SSA method is compared with the Particle Swarm Optimization (PSO) algorithm and P&O methods. Additionally, we evaluated the effect of changes in temperature and radiation on solving the problem. The results of the simulation in the MATLAB/Simulink environment show the optimal performance of the proposed method in tracking the maximum power in different atmospheric conditions compared to other methods. To validate the proposed algorithm, it is compared with four important indexes: ISE, ITSE, IAE, and ITAE.

Keywords: photovoltaic system; partial shade; maximum power point tracking; SALP swarm algorithm (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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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