Development of a Machine Learning Model for Fault Classification of Photovoltaic Systems

The utilization of photovoltaic (PV) energy has witnessed significant recent growth, positioning it as the fastest-growing renewable energy technology. This rise in deployment underscores the importance of monitoring PV systems, which has become an increasingly critical area of research. However, the heightened focus on monitoring has also revealed a key challenge: various types of faults often remain undetected, potentially causing severe performance degradation. This study proposes a machine learning-based fault classification method that integrates Principal Component Analysis (PCA) for feature extraction with Kullback–Leibler (KL) divergence for distribution-based classification. The approach is applied to a publicly available dataset on a single benchmark of a 5 kW PV plant containing voltage, current, temperature, and irradiance measurements for five operating conditions: normal operation, short circuit, open circuit, partial shading, and degradation. A dedicated normalization strategy and optimal bin-width selection are employed to enhance the stability and accuracy of probability density function estimation. Experimental results demonstrate that the proposed PCA–KLD framework achieves superior accuracy compared to existing methods applied to the same dataset. These results confirm the effectiveness and robustness of the approach for fault classification in PV systems and highlight its potential for handling challenging fault scenarios.