Fault Detection and Classification in Power Transmission Lines Using Discrete Wavelet Transform-Based Swish Recurrent Neural Network

Transmission lines are an essential power grid component that is responsible for managing electricity from a production station to consumers and substations. The power network's transmission lines are primarily met with various faults. However, to maintain normal operation and minimize damage because of transmission line faults, it requires detecting and classifying faults. In this research, the Discrete Wavelet Transform-based Swish Recurrent Neural Network (DWT-SRNN) is proposed to detect and classify faults in power transmission lines using Deep Learning (DL). Initially, the data is acquired from the transmission line workspace by monitoring devices and sensors. Then, the Butterworth filtering is employed to increase the data quality. The DWT is performed to extract the features that transfer the time domain signal to the time-frequency signal during faulty and non-faulty conditions by decomposition. Finally, the SRNN is established to detect and classify faults in power transmission lines effectively. The existing techniques like Graph Convolutional Neural Network (GCNN), Machine Learning (ML), and Long Short-Term Memory (LSTM) are compared with DWT-SRNN. The proposed DWT-SRNN achieves a better accuracy of 99.89% compared to GCNN, ML, and LSTM respectively.