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Machine learning advances in transmission line fault detection: A literature review
Department of Computer, Electronics and Electrical Engineering, College of Engineering and Information Technology, Cavite State University Main-Campus, Indang, Cavite, Philippines.
Review
International Journal of Science and Research Archive, 2024, 12(01), 2880–2887.
Article DOI: 10.30574/ijsra.2024.12.1.1150
Publication history:
Received on 13 May 2024; revised on 24 June 2024; accepted on 27 June 2024
Abstract:
Fault detection in transmission lines plays a role in maintaining the dependability and steadiness of power networks. Traditional methods for identifying faults often struggle to handle the diverse nature of real world fault situations. Machine learning (ML) algorithms offer a data centered approach that can adjust and learn from datasets potentially overcoming the limitations of traditional approaches. This document presents a review of progress in using ML for detecting faults in transmission lines. By drawing insights from a variety of studies we explore how ML algorithms have evolved in fault detection, including techniques like networks, recurrent neural networks featuring Long Short Term Memory and convolutional neural networks. We delve into the spectrum of applications where ML is used for fault detection across fault scenarios and operational settings. Additionally we discuss the obstacles and prospects linked to putting ML based fault detection systems into practice such as challenges with data quality, model interpretability and integration with existing grid monitoring systems. Lastly we outline future research paths focused on pushing forward the boundaries of fault detection, in power transmission systems through approaches and collaborative endeavors involving academia, industry players and policymakers. In general, this review highlights how machine learning has the power to revolutionize fault detection methods enhancing the resilience and dependability of power systems.
Keywords:
Transmission line fault detection; Machine learning; Neural networks; Fault scenarios
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
