Version 1
: Received: 9 May 2024 / Approved: 10 May 2024 / Online: 10 May 2024 (11:31:09 CEST)
Version 2
: Received: 10 May 2024 / Approved: 11 May 2024 / Online: 12 May 2024 (07:46:22 CEST)
How to cite:
Daang, J. A. M.; Omas-as, A. M.; Arboleda, E. R. Advancements in Fault Detection Techniques for Transmission Lines: A Literature Review. Preprints2024, 2024050662. https://doi.org/10.20944/preprints202405.0662.v2
Daang, J. A. M.; Omas-as, A. M.; Arboleda, E. R. Advancements in Fault Detection Techniques for Transmission Lines: A Literature Review. Preprints 2024, 2024050662. https://doi.org/10.20944/preprints202405.0662.v2
Daang, J. A. M.; Omas-as, A. M.; Arboleda, E. R. Advancements in Fault Detection Techniques for Transmission Lines: A Literature Review. Preprints2024, 2024050662. https://doi.org/10.20944/preprints202405.0662.v2
APA Style
Daang, J. A. M., Omas-as, A. M., & Arboleda, E. R. (2024). Advancements in Fault Detection Techniques for Transmission Lines: A Literature Review. Preprints. https://doi.org/10.20944/preprints202405.0662.v2
Chicago/Turabian Style
Daang, J. A. M., Andrey M. Omas-as and Edwin R. Arboleda. 2024 "Advancements in Fault Detection Techniques for Transmission Lines: A Literature Review" Preprints. https://doi.org/10.20944/preprints202405.0662.v2
Abstract
This literature review delves into the realm of fault detection techniques for transmission lines, aiming to provide a comprehensive overview of recent advancements and key trends in the field. Employing a structured approach, the review synthesizes a plethora of research spanning from 2019 to 2024, sourced from diverse databases including IEEE Xplore, ScienceDirect, ResearchGate, Scopus, Litmaps, and Google Scholar. The methodology encompasses a systematic literature search protocol, stringent inclusion and exclusion criteria, meticulous data extraction, and a multi-dimensional analysis framework. The literature review uncovers a spectrum of fault detection methodologies, ranging from traditional signal processing techniques like Discrete Wavelet Transform and phase angle-based methods to cutting-edge deep learning algorithms such as Capsule Networks and Convolutional Neural Networks. Insights gleaned from the review underscore the critical importance of fault detection in maintaining the reliability, safety, and efficiency of power grids, highlighting its role as a frontline defense against widespread outages and equipment damage. Key findings from the review shed light on the efficacy of different fault detection approaches, showcasing their strengths and limitations across diverse system conditions. Furthermore, the review identifies common trends and challenges, including the need for real-world validation, scalability, adaptability to various network configurations, and cybersecurity considerations. This literature provides valuable insights and recommendations for future research endeavors in fault detection for transmission lines. By embracing advancements in both traditional and emerging techniques, researchers can continue to enhance the resilience and dependability of power transmission systems, ensuring their ability to withstand evolving challenges and safeguard critical infrastructure.
Keywords
Transmission line fault detection; Machine learning; Deep learning; Fault classification; Grid resilience
Subject
Engineering, Telecommunications
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
