Applying Machine Learning Algorithms for Fault Detection and Classification in Transmission Lines

Today, it is essential to identify and classify transmission line problems in order to maintain stability and a steady supply of electricity. To ensure safety and reduce power loss owing to the defect, a faulty section must be removed from a healthy section. Nowadays, Machine learning (ML) is widely employed in all aspects of daily life. In this paper, an ML-based system for fault classification and fault detection has been proposed. For that, in order to create normal and fault data (voltage/ current) of four different types of faults, Matlab Simulink was used to simulate the DFIG-WT linked to the grid via the transmission line. The dataset obtained by Matlab is utilized to test and train the models as well as to calculate and analyze the performance of four algorithms by the use of recall, precision, and accuracy metrics. The high-performing classifier is determined by comparing the results of the confusion matrix for machine learning methods.