Wavelet Transform-based Fault Detection and ELM-based Fault Classification for Transmission Lines

This research emphasizes the necessity of fault detection and classification algorithms, specifically employing wavelet transform as a valuable tool for detecting faults that surpass predefined thresholds. The fault characteristics are extracted using Wavelet Singular Entropy (WSE) from each phase of the transmission lines. An Artificial Neural Network (ANN) is employed to classify these faults, utilizing an Extreme Learning Machine (ELM) based training algorithm. The IEEE Test 9 bus system is selected as a test case, where fault scenarios are created to conduct this study.