Comprehensive Survey on Metaheuristic Approaches for Photovoltaic Parameter Identification

Abstract

This comprehensive survey delves into the critical task of parameter estimation in photovoltaic (PV) systems, a fundamental challenge in the design and modeling of solar energy solutions. Diode-based models, including the single-diode, double-diode, and three-diode configurations, are widely employed to represent the intricate behavior of PV cells. Accurate parameter identification within these models is essential for precise system performance prediction, component sizing, and economic feasibility assessment. Metaheuristic algorithms, renowned for their effectiveness in tackling complex optimization problems, have emerged as promising tools for PV parameter estimation. This survey meticulously examines a diverse array of metaheuristic algorithms, encompassing genetic algorithms, particle swarm optimization, differential evolution, and contemporary techniques such as grey wolf optimization and whale optimization algorithm. A comparative analysis of these algorithms is conducted, high-lighting their respective strengths, weaknesses, and suitability for PV parameter estimation. Moreover, the survey explores the influence of various factors on the performance of meta-heuristic algorithms in this context. These factors include initialization strategies, parameter tuning, and problem representation. By meticulously investigating these aspects, the paper aims to equip researchers and practitioners with valuable insights for selecting the most appropriate techniques tailored to their specific PV system applications. Through this comprehensive exploration, this survey seeks to contribute to the advancement of PV system design and optimization by providing a clear understanding of the state-of-the-art meta-heuristic algorithms for parameter estimation.