Estimation of parameters of photovoltaic cells using Lévy flight mayfly optimization algorithm with chaotic local search

Accurate parameter estimation for photovoltaic (PV) cell or module models is crucial for enhancing the performance of PV systems through simulation, evaluation, and control. However, due to the nonlinear and multi-modal nature of PV models, parameter extraction remains a challenging task. To address this issue, this study proposes an improved mayfly optimization algorithm (MOA) for PV cell parameter estimation using measured current–voltage data. To strengthen the algorithm’s global search capability, Lévy-distributed random numbers are introduced to replace uniformly distributed numbers in the mayflies’ position updates. The heavy-tailed property of Lévy flight enables a broader search range, effectively preventing the algorithm from converging to local optima. Furthermore, a chaotic local search operator is incorporated into MOA to refine its local exploitation ability. The proposed algorithm, termed LFC-MOA (Lévy Flight and Chaos-based Mayfly Optimization Algorithm), is rigorously evaluated on three benchmark PV cell datasets. Experimental results demonstrate its effectiveness in accurately identifying PV cell parameters.