A Review of Fault Diagnosing Methods in Power Transmission Systems
Source abstract
Abstract
Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Power Systems Fault Detection · Islanding Detection in Power Systems · Power Transformer Diagnostics and Insulation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical Engineering, the University of Lahore, Lahore 54000, Pakistan
Department of Electrical Engineering, Ecole Militaire Polytechnique, Algiers 16111, Algeria
Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Department of Engineering & Mathematics, Sheffield Hallam University, Sheffield S1 1WB, UK
References (100+)
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