A Literature Survey on Fault Identification and Classification System Using Machine Learning

Abstract

This paper provides a study of different methodologies for classifying faults in transmission lines. Effective positioning of the relay medium in the network will detect any interruption in the power system. The key issue in the transmission system is usually fault detection. If the fault happens during the power swing, then the fault diagnosis is a tedious one. Early detection of faults is important to ensure the performance of the distributed system. Although there are many solutions for fault detection, it is still a challenge to manage the complexity and uncertainty of system observations to make an accurate detection. For a reliable and high speed protective relay followed by digital distance safety, fault classification is more important. It is a summary of a brief introduction of transmission line faults and a review of the scope of various old approaches in this area. In this report, we present a review study on the detection of faults using machine learning techniques. It presents a brief overview of all the common and hybrid methods that exist. Also, the need for innovative fault classification techniques is discussed in this paper.