Parameters identification of photovoltaic models using modified Rao-1 optimization algorithm

Abstract

Accurate and robust parameter identification method is helpful to the imitation, control and optimization of photovoltaic (PV) system. Therefore, it is necessary to create some more accurate and robust algorithms. A new metaheuristic algorithm modified Rao-1(MRao-1) is proposed by combining Rao-1 with two-way updating strategy based on random individuals. In this strategy, the current or random individuals are chosen as updated starting point and the difference between random individuals is taken as updated direction. MRao-1 inherits the advantages of the original Rao-1 algorithm without additional special parameters and improves the global search ability of Rao-1 significantly without increasing the time complexity of Rao-1. MRao-1 is evaluated on benchmark functions and applied to parameter identification of PV models. The results show that the accuracy and robustness of MRao-1 algorithm are superior to the original algorithm and other recent excellent algorithms. Therefore, MRao-1 is a promising parameter identification algorithm.