Open Access

ARTICLE

Adaptive Multi-Learning Cooperation Search Algorithm for Photovoltaic Model Parameter Identification

Xu Chen^1,*, Shuai Wang¹, Kaixun He²

1 School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013, China
2 College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, China

* Corresponding Author: Xu Chen. Email:

(This article belongs to the Special Issue: Advanced Bio-Inspired Optimization Algorithms and Applications)

Computers, Materials & Continua 2025, 85(1), 1779-1806. https://doi.org/10.32604/cmc.2025.066543

Received 10 April 2025; Accepted 17 July 2025; Issue published 29 August 2025

Abstract

Accurate and reliable photovoltaic (PV) modeling is crucial for the performance evaluation, control, and optimization of PV systems. However, existing methods for PV parameter identification often suffer from limitations in accuracy and efficiency. To address these challenges, we propose an adaptive multi-learning cooperation search algorithm (AMLCSA) for efficient identification of unknown parameters in PV models. AMLCSA is a novel algorithm inspired by teamwork behaviors in modern enterprises. It enhances the original cooperation search algorithm in two key aspects: (i) an adaptive multi-learning strategy that dynamically adjusts search ranges using adaptive weights, allowing better individuals to focus on local exploitation while guiding poorer individuals toward global exploration; and (ii) a chaotic grouping reflection strategy that introduces chaotic sequences to enhance population diversity and improve search performance. The effectiveness of AMLCSA is demonstrated on single-diode, double-diode, and three PV-module models. Simulation results show that AMLCSA offers significant advantages in convergence, accuracy, and stability compared to existing state-of-the-art algorithms.

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Keywords

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Supplementary Material

Supplementary Material File

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