Adaptive Oscillatory Flow Direction Algorithm with Lambert W-Based Fitness Evaluation for Accurate Photovoltaic Parameter Extraction
Abstract
Accurate extraction of photovoltaic (PV) model parameters is essential for reliable PV-system simulation, maximum power point tracking, fault diagnosis, and performance assessment. However, this task is a nonlinear, multimodal, and strongly coupled optimization problem, particularly for double-diode models. This paper proposes an Adaptive Oscillatory Flow Direction Algorithm (AOFDA) for accurate parameter estimation of single-diode and double-diode PV models. AOFDA extends the Flow Direction Algorithm with three mechanisms: adaptive oscillatory flow movement for population diversification and directional exploration, adaptive exploration–exploitation probability to regulate the global-to-local search transition, and an elite-guided learning strategy to enhance information exchange among high-quality solutions. A Lambert W-based fitness-evaluation scheme is employed to calculate the PV output current explicitly, avoiding repeated iterative solutions and reducing computational burden. The method is assessed on four PV datasets (RTC France, amorphous silicon, PWP201, and LSM20) and compared with the original FDA and several metaheuristics using RMSE, boxplots, and Wilcoxon tests. Results show that AOFDA provides accurate and stable PV parameter estimates, achieving RMSE values of 6.460534 × 10−4 for RTC France and 2.454593 × 10−5 for the amorphous-silicon double-diode model. Overall, AOFDA improves convergence consistency and estimation accuracy, especially for higher-dimensional PV parameter-identification problems.