Applications of Machine Learning Algorithms in Renewable Energies
Marouan Ben El Haj; Said Ziani
Abstract
Reliable extraction of PV module electrical parameters constitutes a critical step toward enabling fault detection and performance assessment. The strong dependence of these parameters on environmental factors, particularly solar irradiance and cell operating temperature, introduces significant nonlinearity ...
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Reliable extraction of PV module electrical parameters constitutes a critical step toward enabling fault detection and performance assessment. The strong dependence of these parameters on environmental factors, particularly solar irradiance and cell operating temperature, introduces significant nonlinearity that complicates their accurate identification. This paper proposes a hybrid metaheuristic framework, combining the Whale Optimization Algorithm (WOA) for global search with the Fishier Mantis Optimizer (FMO) for local refinement, for the parameter identification of single-, double-, and triple-diode PV models (SDM, DDM, TDM). A Bidirectional Long Short-Term Memory (BiLSTM) network is additionally investigated as a candidate residual corrector between the equivalent-circuit simulation and the measured current. Experimental validation uses current-voltage (I-V) data measured from the Solarex MSX60 module at three irradiance levels (600, 800, 1000 W/m², 25 °C). In a fair benchmark where PSO, GA, standard WOA, and two modern (post-2019) metaheuristics — the Arithmetic Optimization Algorithm (AOA) and the Slime Mould Algorithm (SMA) — are equipped with the same Latin-Hypercube initialization, opposition-based learning and reflection-based boundary handling as the proposed method, WOA-FMO attains a physical-parameter RMSE of 3.71×10⁻² A on all three topologies, significantly outperforming every baseline, classic and modern alike (Wilcoxon rank-sum, p<0.01 in all ten pairwise comparisons), at a comparable computational cost.