Parameters identification of photovoltaic cell and module using LSHADE

Identifying circuit model parameters for photovoltaic cell and module is a challenging issue that often translates into an optimization problem. Recently, the most mainstream solution to such problems is based on metaheuristic optimization algorithms. Although there are many metaheuristic algorithms for the problem, the obtained parameters are often not very accurate and reliable. Therefore, a linear population reduction success-history based parameter adaptation for differential evolution (LSHADE) is applied to accurately and reliably identify the parameters of photovoltaic models. In LSHADE, the population size is continually decreased according to a linear function. The effectiveness of LSHADE is evaluated by identifying the parameters of the single diode model, the double diode model and the photovoltaic module model. The experimental results show that LSHADE outperforms other well-established parameters identification algorithms with respect to accuracy, stability, and rapidity.