Parameter identification of photovoltaic cell model based on improved grasshopper optimization algorithm

Abstract

An accurate mathematical model has practical applications in the design and research of solar cells. Many intelligent optimization algorithms are currently used to extract solar cell parameters. However, optimization algorithms including grasshopper optimization algorithm (GOA) are easy to fall into local optimums. An improved grasshopper optimization algorithm (IGOA) was proposed in this paper. Chaotic initialization was carried out to improve the quality of initial population. Then differential evolution strategy was introduced to maintain the diversity of the population through mutation, crossover and selection process, leading to getting rid of local optimums and searching for better solutions in GOA. To accelerate the convergence speed of the algorithm, the positions of particles were updated with current particle optimal values as targets as in particle swarm optimization. The optimization experiments on standard test functions shown the superiority of IGOA compared with other intelligent optimization algorithms. IGOA was then used to identify the parameters of polycrystalline silicon solar cells. The identification accuracy and stability of IGOA are much higher than harris hawks optimization, grey wolf optimization and ant lion optimization. The effectiveness of IGOA in identifying parameters of solar cell under different illuminations was also verified by experiments.