Enhanced Lévy-flight bat algorithm for reliable robust photovoltaic parameter estimation under faulty and degraded operating conditions

Accurate photovoltaic (PV) parameter extraction is important for performance monitoring, fault diagnosis, and reliable integration of PV systems into renewable energy networks. This study proposes an Enhanced Lévy Flight Bat Algorithm (ELBA) for estimating the parameters of PV modules under faulty operating conditions. The proposed method is applied to both single-diode and double-diode PV models using measured current–voltage data. Three common fault conditions are considered: partial shading, discoloration, and cracking. Each fault is evaluated under clean, dirty, and shaded operating states. ELBA combines a diversification stage for wider global exploration with a Lévy-flight-based local search to improve convergence stability and reduce premature stagnation. The objective function minimizes the root mean square error (RMSE) between measured and simulated current values while keeping all estimated parameters within physical bounds. The proposed method achieved low RMSE values for both models. For the single-diode model, the RMSE values were 0.0036, 0.0039, and 0.0108 under clean, dirty, and shaded states, respectively. For the double-diode model, the corresponding RMSE values were 0.0029, 0.0033, and 0.0098. Comparative analysis showed that ELBA outperformed established metaheuristic algorithms and produced stable parameter estimates under fault conditions. The Wilcoxon signed-rank test confirmed that the improvement was statistically significant (p < 0.001). These results show that ELBA can provide accurate and robust PV model parameter estimation, which can support fault-aware PV performance modeling, monitoring, and renewable energy system operation.