A novel hybrid algorithm based on rat swarm optimization and pattern search for parameter extraction of solar photovoltaic models

Parameter extraction of photovoltaic (PV) models based on measured current-voltage data plays an important role in the control, simulation, and optimization of PV systems. Despite the fact that various parameter extraction strategies have been dedicated to solving this problem, they may have certain drawbacks. In this paper, an effective hybrid optimization method based on adaptive rat swarm optimization (ARSO) and pattern search (PS) is presented for effectively and consistently extracting PV parameters. The proposed method employs the global search ability of ARSO and the local search ability of PS. The performance of the new algorithm is investigated using a set of benchmark test functions, and the results are compared with those of the standard RSO and some other methods from the literature. The extraction of parameters from several PV models, such as single-diode, double-diode, and PV modules, confirms the performance of the suggested method. Simulation results show that the proposed method surpasses other state-of-the-art procedures in terms of accuracy, reliability, and convergence speed.