An Effective Optimization Algorithm for Parameters Identification of Photovoltaic Models

Renewable energy is becoming more popular due to environmental concerns about the previous energy source. Accurate solar photovoltaic (PV) system model parameters substantially impact the efficiency of solar energy conversion to electricity. In this matter, swarm and evolutionary optimization algorithms have been widely utilized in dealing with practical problems due to their more straightforward concepts, efficacy, flexibility, and easy-to-implement procedural frameworks. However, the nonlinearity and complexity of the PV parameter identification caused swarm and evolutionary optimizers to exhibit Immaturity in the obtained solutions. In this study, an effective metaheuristic algorithm based on tunicate swarm optimization (TSA) is proposed for parameter identification of PV models. The proposed improved algorithm (ITSA) has two main phases at each iteration: searching all around the search space based on a randomly selected tunicate and improving the search using the position of the best tunicate. This modification improves the algorithm's exploration ability while also preventing premature convergence. The suggested algorithm's performance is confirmed using ten mathematical test functions and the outcomes are compared with TSA as well as some effective optimization algorithms. The proposed ITSA optimally identifies various parameters in the PV model, such as single diode (SDM), double diode (DDM), and PV modules. Based on the comprehensive comparisons, results indicate that the improved ITSA algorithm has higher convergence accuracy and better stability than the original TSA and other studied algorithms.