Deep Learning based Fault Detection in Power Transmission Lines
T. Kumar, RadheyShyam Meena, Prathap Mani, S. Ramya, K. Lakshmi Khandan, Arshad Mohammed, M. Siva Ramkumar
Abstract
The transmission line is crucial in the electrical system. Transmission lines allow energy to be transmitted As the world's demand for power has grown considerably in recent years, transmission lines have become increasingly important in electricity transport from the manufacturing location to the transmission networksElectrical transmission line safeguarding is a critical aspect of power system design.since transmission lines cause 85–87 percent of power system problems because of fault. By immediately recognizing and deactivating bad cables before they cause serious damage, the equipment can be safe guarded Utility workers may utilize accurate fault detection to eliminate persistent problems to prevent power outages and the occurrence of faults. Fault diagnosis in power lines is crucial to the protection and improvement of the energy system. In this research, the type of power transmission fault is identified using the voltage and current information. To do this work the data from Kaggle is utilized and processed Two Deep Learning (DL) networks are employed for the classification task. The two models are Artificial Neural Network (ANN), and Convolutional Neural Network (CNN). Both models are validated using accuracy and loss metrics. From the comparison, it is found that CNN outperforms the ANN in terms of accuracy.