Parameter identification for solar cells and module using a Hybrid Firefly and Pattern Search Algorithms

Accurate estimation the electrical equivalent circuit parameters of photovoltaic arrays of solar cells is needed to enhance the performance of solar energy systems. Thus this field has attracted the attention of various researchers. Since the current versus voltage I-V characteristics of photovoltaic is nonlinear, thus an optimization technique is necessary to adjust experimental data to the solar cell model. Some optimization algorithms have been used to estimate the electrical parameters of the model. However, more investigation is needed to improve estimation of the model. The Firefly algorithm is one of the recently proposed swarm intelligence based optimization algorithm that showed impressive performance in solving optimization problems. This algorithm is good for exploring solution if applied alone but need a local optimization method to improve exploitation. In this study, we combine pattern search as a local optimization method with firefly algorithm to improve this algorithm. The proposed algorithm is applied for parameter estimation of single and double diode solar cell models. To show the performance of this algorithm the results are compared, with the other optimization algorithms for parameters of photovoltaic. The results show that the proposed algorithm is a competitive algorithm to be considered in the modeling of solar cell systems.