Comprehensive overview of meta-heuristic algorithm applications on PV cell parameter identification

Accurate parameter identification is crucial for a precise PV cell modelling and analysis of characteristics of PV systems, while high nonlinearity of output I-V curve makes this problem extremely thorny. Hence, a large number of researches have aroused extensive interests in the past few years. Due to the rapid advancement of computer technology and swarm intelligence, various promising meta-heuristic algorithms have been proposed to further accelerate this trend. This paper aims to undertake a comprehensive review on meta-heuristic algorithms and related variants which have been applied on PV cell parameter identification. Particularly, these algorithms are classified into four categories, e.g., biology-based algorithms, physics-based algorithms, sociology-based algorithms and mathematics-based algorithms. Meanwhile, the evaluation criteria and identification performance of each algorithm are thoroughly addressed. Besides, in order to quantitatively evaluate and compare various algorithms, the identified PV parameters including the specific error and the simulated output IV or P-V curves are provided at the end of each algorithm. Moreover, a comprehensive summary is also introduced to more specifically guide the readers to grasp and utilize these approaches. Lastly, based on the covered twenty-eight algorithms, conclusion presents some perspectives and recommendations for future development.