Fault Detection and Classification for Wide Area Backup Protection of Power Transmission Lines Using Weighted Extreme Learning Machine

Abstract

The changing landscape of power grids with distributed energy sources and power electronic devices has led to increasing relay maloperations. Wide area backup protection is necessary for the resolution of faults and for a reliable power grid. This paper presents detecting and classifying faults in transmission lines for wide-area backup protection using phasor measurement units (PMU) data. The faults are detected and classified using a Weighted Extreme Learning Machine (WELM) algorithm, which considers the variable distribution of data among the different classes using a weighted approach. The PMU signal data used were generated by the simulation of an IEEE 39 bus test system in the PowerWorld/OpenPDC/MATLAB environment. For classification, the input features data were derived using a wavelet transform-based ensemble feature extraction technique, and the WELM classifier was optimized using Particle Swarm Optimization (PSO). The PSO optimized WELM (PSO-WELM) model trained on PMU data detected faults with 100% accuracy and classified them into different types with an accuracy of 99.85%. It is validated that the PSO-WELM outperforms other known classifiers on performance comparison. The model also classified noisy data with a signal-to-noise ratio (SNR) as low as 10 dB and with an accuracy of 97%.