A Robust Salp Swarm Algorithm for Photovoltaic Maximum Power Point Tracking Under Partial Shading Conditions

Boyan Huang (), Kai Song, Shulin Jiang, Zhenqing Zhao (), Zhiqiang Zhang, Cong Li and Jiawen Sun
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Boyan Huang: College of Electrical Engineering and Information, Northeast Agricultural University, Harbin 150030, China
Kai Song: College of Electrical Engineering and Information, Northeast Agricultural University, Harbin 150030, China
Shulin Jiang: College of Electrical Engineering and Information, Northeast Agricultural University, Harbin 150030, China
Zhenqing Zhao: College of Electrical Engineering and Information, Northeast Agricultural University, Harbin 150030, China
Zhiqiang Zhang: College of Electrical Engineering and Information, Northeast Agricultural University, Harbin 150030, China
Cong Li: College of Electrical Engineering and Information, Northeast Agricultural University, Harbin 150030, China
Jiawen Sun: College of Electrical Engineering and Information, Northeast Agricultural University, Harbin 150030, China

Mathematics, 2024, vol. 12, issue 24, 1-17

Abstract: Currently, numerous intelligent maximum power point tracking (MPPT) algorithms are capable of tackling the global optimization challenge of multi-peak photovoltaic output power under partial shading conditions, yet they often face issues such as slow convergence, low tracking precision, and substantial power fluctuations. To address these challenges, this paper introduces a hybrid algorithm that integrates an improved salp swarm algorithm (SSA) with the perturb and observe (P&O) method. Initially, the SSA is augmented with a dynamic spiral evolution mechanism and a Lévy flight strategy, expanding the search space and bolstering global search capabilities, which in turn enhances the tracking precision. Subsequently, the application of a Gaussian operator for distribution calculations allows for the adaptive adjustment of step sizes in each iteration, quickening convergence and diminishing power oscillations. Finally, the integration with P&O facilitates a meticulous search with a small step size, ensuring swift convergence and further mitigating post-convergence power oscillations. Both the simulations and the experimental results indicate that the proposed algorithm outperforms particle swarm optimization (PSO) and grey wolf optimization (GWO) in terms of convergence velocity, tracking precision, and the reduction in iteration power oscillation magnitude.

Keywords: photovoltaic power generation; maximum power point tracking; improved salp swarm algorithm; Lévy flight strategy; dynamic spiral evolution mechanism (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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