A Review of Fault Diagnosing Methods in Power Transmission Systems

RAZA, Ali, BENRABAH, Abdeldjabar, ALQUTHAMI, Thamer and AKMAL, Muhammad (2020). A Review of Fault Diagnosing Methods in Power Transmission Systems. Applied Sciences, 10 (4), e1312.

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Official URL: https://www.mdpi.com/2076-3417/10/4/1312
Open Access URL: https://www.mdpi.com/2076-3417/10/4/1312 (Published)
Link to published version:: https://doi.org/10.3390/app10041312


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.

Item Type: Article
Additional Information: ** From MDPI via Jisc Publications Router ** Licence for this article: https://creativecommons.org/licenses/by/4.0/ **Journal IDs: eissn 2076-3417 **History: published 14-02-2020; accepted 01-02-2020
Uncontrolled Keywords: AC networks, artificial intelligence (AI), deep learning (DL), fault detection (FD), fault-type classification (FC), fault location (FL), machine learning (ML)
Identification Number: https://doi.org/10.3390/app10041312
Page Range: e1312
SWORD Depositor: Colin Knott
Depositing User: Colin Knott
Date Deposited: 17 Feb 2020 11:36
Last Modified: 18 Mar 2021 01:15
URI: https://shura.shu.ac.uk/id/eprint/25851

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