Deep-Learning-Enhanced Hybrid WOA-FMO Algorithm for Accurate PV Parameter Estimation in Single-, Double-, and Triple-Diode Models

Abstract

The accurate modeling of photovoltaic (PV) systems is crucial in optimizing energy efficiency and operational reliability. To address challenges in parameter estimation under dynamic conditions, a hybrid deep learning (DL)-based optimization scheme is proposed. It is hypothesized that combining the global search capabilities of the Whale Optimization Algorithm (WOA) with local refinement of Fishier Mantis Optimization (FMO), supported by long short-term memory (LSTM)-based predictions, enhances accuracy and robustness. The method was validated through simulations on single-, double-, and triple-diode models (SDM, DDM, and TDM) using MATLAB 2021a version. The hybrid model achieved the lowest root mean square error (RMSE) of 6.96 × 10−4 across all models, outperforming standard metaheuristics and showing strong stability over multiple runs. These findings confirm the method’s superior accuracy and efficiency for PV parameter extraction.