Enhancing Fault Detection and Diagnosis in Grid-Connected PV Systems under Irradiance Variations: A Novel Approach Using New Data Normalization Techniques on RBFNN Algorithm

Abstract

With the global shift towards renewable energy, the reliability and efficiency of photovoltaic (PV) systems have become paramount. A crucial aspect of this is the timely detection and accurate classification of faults within the system. In fact, accurate fault detection and classification in the diagnostic systems can prevent severe damages, enhance operational efficiency, and prolong the lifespan of the installations. This paper introduces a novel data normalization technique tailored for enhancing the performance of meta-heuristic algorithms (namely RBFNN) applied to single-phase inverter current signals for fault detection in PV systems. While conventional normalization technics have shown their limitations in capturing the nuances of PV system faults, our proposed strategy demonstrates a more effective approach in preserving fault-specific features in the data. The proposed method is evaluated using multiple datasets encompassing a variety of fault scenarios. Empirical results show a significant improvement in both fault detection rates and classification accuracy. This study not only presents a breakthrough in PV system fault diagnosis but also sheds light on the broader potential of specialized normalization techniques in all signal-processing field, such as biomedical signal processing, industrial process control, or financial time series analysis, and other domains. The supremacy of the proposed diagnostic in reducing false alarms and providing more clear-cut classifications is highlighted by several simulations under Matlab-Simulink.