A Critical Review of Artificial Intelligence-based Fault Detection, Location and Resolution Techniques in Transmission Networks: A Case Study of the Nigerian 330 kV Transmission Network
1 Department of Electrical and Electronic Engineering, Akwa Ibom State University, Nigeria.
2 Department of Electrical/Electronic Engineering, Akwa Ibom State Polytecnic, Nigeria.
Review
Open Access Research Journal of Engineering and Technology, 2026, 10(02), 127-138.
Article DOI: 10.53022/oarjet.2026.10.2.0048
Publication history:
Received on 06 May 2026; revised on 12 June 2026; accepted on 15 June 2026
Abstract:
An electrical power system is a network that generates, transmits, and distributes electricity from power stations to end users via a transmission line. This power system network is characterized by very lengthy transmission lines which often pass through different environmental topography making it possible for the transmission line to experience fault. A fault is an undesired disruption to the power system that disrupts the system network's regular operation. The need to quickly and accurately identify, classify, and locate these faults is very important as failure to do so will cause damage to the end users and losses to the transmission company. This problem has propelled a detailed review of the fault analysis in transmission network by exploring various scientific and engineering powerful simulation tools and reliable scientific methods like artificial intelligence (AI), machine learning (ML), signal processing techniques, and recent techniques used by researchers. The system quality such as voltage, and current are used in the analysis. Traditional and AI method for Fault detection, fault classification, and fault location are also discussed separately to give a better understanding of each method used in the individual fault analysis. This study presents a comprehensive review that guides the understanding of various techniques used in fault detection, classification and location analysis, alongside their results, test systems, and gaps in the literature.
Keywords:
Artificial Intelligence; Fault Detection; Fault Location; Resolution Techniques; Transmission Networks
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Copyright © 2026 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0