Comparative Analysis of Class-Weighted Random Forest, XGBoost, and SVM for Multi-Class Photovoltaic Fault Classification

This research develops a machine learning-based approach for accurate fault classification in photovoltaic (PV) systems using Random Forest, XGBoost, and Support Vector Machine (SVM). The study compares the performance of these models to identify an effective and reliable approach for detecting and classifying PV system faults.