A review of solar cell parameters extraction using metaheuristic optimization methods based on various diode models

Accurate PV modeling will have a significant impact on the future energy landscape as the market share of photovoltaic (PV) technology continues to rise. Consequently, it is essential to use similar PV models to translate complex PV systems into mathematically accessible models. However, it is difficult to derive precise parameters from PV models due to their multi-peaked, non-linear, and tightly coupled properties. Regardless of gradients and function forms, metaheuristics may effectively handle these problems and are receiving more attention as a solution. This review examines several meta heuristics optimization methods based on various diode models for extracting parameters from the PV model and elucidates the behavior of various algorithms and diode models. The merits and demerits of various algorithms are then discussed after some commonly used performance indicators are tallied and compared to assess the efficiency, robustness, accuracy, competitiveness, and resources used. After analyzing the patterns of variation in the data taken from various external environments, the relevant literature was compiled. After that, difficulties with application scenarios and metaheuristics are examined. Ultimately, related research perspectives are summed up as a reliable resource for technical advancements in PV model parameter extraction.