Identification of Photovoltaic Cell Parameters Using Mayflies Optimization Algorithm with Lévy Mutation

Deriving the true parameters of photovoltaic cells' model from measured data is an essential issue for providing reasonable analysis, simulation, optimization and control of photovoltaic systems. However, the parameter identification of PV cells still faces great difficulty due to its nonlinearity and multi‐modality. To address this issue, an improved mayflies optimization algorithm, called Lévy mutation mayflies optimization algorithm (LM‐MOA) is proposed to identify the parameters of PV cells by minimizing the error between the simulated and measured current. In LM‐MOA, a mutation operation based on Lévy random number is performed on each mayflies after updating their position, which enables mayflies to perform more efficient search in an enlarged range. In this way, the global search ability of mayflies is enhanced. The performance of LM‐MOA is examined through extensive experiments in identifying the parameters of single diode model, double diode model and PV module. Experimental results show that the performance of LM‐MOA is superior to most existing meta‐heuristic algorithms and can obtain more accurate and reliable identification results. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.