Evaluation of Wavelet Transform Based Feature Extraction Techniques for Detection and Classification of Faults on Transmission Lines Using WAMS Data

Abstract

The smart grid is an intelligent power system network that should be reliable and resilient for sustainable operation. Wide-Area Measurement Sys- tems (WAMS) are deployed in the power grid to provide real-time situational awareness to the power grid oper- ators. An excellent strategy for exploiting the WAMS data effectively is to extract relevant insights from the increasing volume of data collected. Feature extrac- tion techniques are pivotal in developing data-driven models for power systems. This paper proposes an ensemble feature extraction method for developing intelligent data-driven models for transmission line fault detection and classification. A comparative ef- ficacy analysis of the proposed ensemble feature extrac- tion method is carried out with state-of-the-art feature extraction methods. The models developed and eval- uated with the feature data derived with the proposed method give an accuracy of 100 % for fault detection and 99.78 % for fault classification. This method also has the advantage of significantly reducing training and testing time. Features are extracted from the WAMS data collected by simulating an IEEE 39 bus test system in the PowerWorld simulator.