Paper Title: State-of-the-art techniques and algorithms for swift and precise fault detection and protection in transmission lines
Authors: Siphesihle Sibonelo Xulu, Bongumsa Mendu, Bessie Baakanyang Monchusi
Corresponding Author: Bongumsa Mendu (mendubongumsa@gmail.com), South Africa
Abstract
Transmission lines are crucial for power systems, enabling bulk power transfer from generation sites to load centers. They face challenges such as faults, losses, and delays, necessitating effective management and maintenance strategies. The aim of this paper is to conduct a systematic literature review focusing on techniques and algorithms for swift and precise fault detection and protection in transmission lines. The methodology included a collection of relevant papers, a filtering process, eligibility identification, synthesizing, and trend analysis. This process was facilitated using the Scopus database and VOSviewer software. Results of this survey revealed some key noticeable aspects (among others) across the studies, which included the utilization of diverse signal processing and machine learning techniques to analyze voltage and current signals for identifying faults. This work will contribute by reviewing recent advances in signal processing, analyzing methods to enhance fault detection speed and accuracy, exploring the use of machine learning and neural networks in fault detection models, investigating advanced relay technologies and protection schemes, evaluating statistical techniques for fault isolation, and examines indexing techniques and evolutionary programming tools for precise fault identification, while also proposing future research directions.
Keywords
Fault detection, Transmission lines protection, Three-phase, Techniques and algorithms
Cite:
Xulu, S. S. ., Mendu, B. ., & Monchusi, B. B. . (2025). State-of-the-art techniques and algorithms for swift and precise fault detection and protection in transmission lines. Future Technology, 4(1), 12–22. Retrieved from https://fupubco.com/futech/article/view/234
