Effectiveness of supervised machine learning models for electrical fault detection in solar PV systems.

Even though Photovoltaic (PV) systems have emerged as a viable substitute for non-renewable energy sources, their widespread integration into the electrical grid presents several issues today. On the other hand, various faults are a key concern affecting PV plants' production and longevity. The current study uses Machine Learning (ML) algorithms such as Decision Tree (DT), Naïve Bayes (NB), Random Forest (RF), Support Vector Machine (SVM) and XGBoost to detect and classify PV errors corresponding to Short Circuits (SC), Open Circuits (OC), Ground Faults (GF), and Mismatch Faults (MF). Simulations were conducted in MATLAB/Simulink to analyse voltage, current, and power variations during fault conditions and study their impact. The proposed results show that the effectiveness of ML in electrical fault detection, with the following classification accuracies: SVM - 97.40%, DT- 97.20%, RF - 97.20%, NB - 97.60%, and XGBoost - 98.0%. The effectiveness of the classification is confirmed through confusion matrices and correlation heatmaps. This research highlights the need for integrating intelligent monitoring, real-time IoT-based detection, and prediction analytics to improve PV system reliability.