Parameters identification of solar cell models using LSHADE hybrid with TSO

Photovoltaic systems are commonly used in daily life as an important device for collecting solar energy. It is important to model PV systems, mainly to simulate the I-V response of solar cells under various conditions. In order to accurately estimate all unknown parameters of different PV models, a new hybrid algorithm, called LSHADE-TSO, is proposed by hybridizing LSHADE and the tuna swarm optimization (TSO). The spiral foraging search and parabolic foraging search of TSO are introduced into the mutation strategy in LSHADE to improve the exploration ability and population diversity. In addition, this paper adds the crossover factor (CR) ranking, top α r1 selection, strategies to improve the convergence efficiency. LSHADE-TSO is applied in solving photovoltaic parameter identification by comparing with well-known algorithms on photovoltaic parameter identification problems in recent years. The data results show that the LSHADETSO algorithm has better convergence and better search capability than other algorithms.