Parameter identification and sensitivity analysis of solar cell models with cat swarm optimization algorithm

Solar cell model is used in various studies of photovoltaic system. Different methods have been developed to determine model parameters. In this paper, an optimization technique based on cat swarm optimization (CSO) algorithm is proposed to estimate the unknown parameters of single and double diode models. To investigate the effectiveness of proposed approach, comparative studies with other techniques are presented. The evaluation for the quality of identified parameters is also given. Results demonstrate the high performance of developed approach, high accuracy of estimated parameters, and calculated I-V curve is in good agreement with experimental I-V data. In addition, the sensitivity of performance to control parameter of CSO is also investigated. Results show the proposed CSO algorithm can be an effective tool to solve the optimization problem of parameter identification of solar cell models.