Fault Detection in clean energy system using wavelet transform and machine learning techniques

The increasing integration of photovoltaic (PV) systems into power grids brings challenges in ensuring reliability and stability due to various faults. This paper proposes a hybrid approach using wavelet transform (WT) and machine learning (ML) techniques to address these challenges. WT extracts key transient features from voltage and current signals, while ML classifiers such as Support Vector Machines (SVM) and Random Forest (RF) are employed for accurate fault detection and classification. The proposed method achieves high accuracy, demonstrated through simulations under diverse fault scenarios.