Adaptive Fault Diagnosis of DC-DC Boost Converters in Photovoltaic Systems Based on Sliding Mode Observers with Dynamic Thresholds

A robust methodology for parametric fault diagnosis in photovoltaic systems is proposed, focusing on DC-DC boost converters. The methodology uses Adaptive Sliding Mode Observers (ASMO) combined with adaptive thresholding. Specifically, an observer-based scheme detects and isolates faults in passive components of the converter, achieving complete isolation in about 0.05 s, even under varying environmental conditions. In addition, a dynamic fault discrimination approach is introduced, based on adaptive thresholds derived from Exponentially Weighted Moving Average (EWMA). This minimizes false alarms caused by transient conditions. Stability and robustness are guaranteed through Lyapunov-based conditions. Simulation results under sequential and simultaneous fault scenarios confirm rapid and precise fault detection, highly specific isolation, and exceptional resilience against environmental disturbances.