• Hybrid MPPT combines fuzzy logic and gradient descent for dynamic conditions. • Ensures accurate tracking under PV faults and fluctuating irradiance. • Uses real experimental data and Lookup Tables for simulation. • Outperforms classical P&O and INC in speed and robustness. • Maintains stability across dust, shading, and partial faults. Maximum Power Point Tracking (MPPT) under real-time faults and dynamic irradiance remains a persistent, yet underexplored, challenge in photovoltaic (PV) systems. Unlike existing studies that address MPPT or PV fault diagnosis independently, this work presents a unified framework that integrates both domains through a novel control and simulation strategy. A Hybrid Fuzzy–Gradient Perturb and Observe (HFG-P&O) algorithm is proposed, which adaptively adjusts the step size using a dual-mode decision mechanism. Specifically, a fuzzy logic controller is activated during abrupt power changes caused by irradiance fluctuations or fault transitions, while an adaptive gradient descent mode ensures precise, drift-free convergence under steady-state conditions. What distinguishes this work is the development of an experimental fault-injection platform, where five real-world PV anomalies, dust, partial shading, degradation, open-circuit, and short-circuit, were physically reproduced. Their I–V and P–V curves were acquired, cleaned, and transformed into custom lookup tables that simulate fault-specific PV behavior under dynamic irradiance. These data-driven models were integrated into MATLAB/Simulink, enabling high-fidelity, fault-aware MPPT simulations. Benchmarking results under identical irradiance profiles demonstrate that HFG-P&O achieves faster convergence (0.007 s), minimal RMSE (98.8%) across all fault conditions. It outperforms conventional P&O, Modified P&O, and INC algorithms, which suffer from higher oscillations and efficiency loss (>5%). This study provides the first experimentally grounded, fault-aware MPPT evaluation framework, offering a reproducible and scalable platform for next-generation adaptive PV control systems.
Bennoui et al. (Fri,) studied this question.