Review on Artificial Intelligence-Based Fault Location Methods in Power Distribution Networks

artificial intelligence; fault location; power distribution networks; Data gathering systems; Fault localization; Fuzzy-Logic; Input datas; Localization method; Location method; Neural-networks; Power distribution network; Reinforcement learnings; Systematic Review; Renewable Energy, Sustainability and the Environment; Fuel Technology; Engineering (miscellaneous); Energy Engineering and Power Technology; Energy (miscellaneous); Control and Optimization; Electrical and Electronic Engineering; Building and Construction

Abstract :

[en] This paper provides a comprehensive and systematic review of fault localization methods based on artificial intelligence (AI) in power distribution networks described in the literature. The review is organized into several sections that cover different aspects of the methods proposed. It first discusses the advantages and disadvantages of various techniques used, including neural networks, fuzzy logic, and reinforcement learning. The paper then compares the types of input and output data generated by these algorithms. The review also analyzes the data-gathering systems, including the sensors and measurement equipment used to collect data for fault diagnosis. In addition, it discusses fault type and DG considerations, which, together with the data-gathering systems, determine the applicability range of the methods. Finally, the paper concludes with a discussion of future trends and research gaps in the field of AI-based fault location methods. Highlighting the advantages, limitations, and requirements of current AI-based methods, this review can serve the researchers working in the field of fault location in power systems to select the most appropriate method based on their distribution system and requirements, and to identify the key areas for future research.

Disciplines :

Electrical & electronics engineering

Author, co-author :

Rezapour, Hamed ; Centre of Excellence for Power System Automation and Operation, School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran

Jamali, Sadegh; Centre of Excellence for Power System Automation and Operation, School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran

Bahmanyar, Alireza ; Université de Liège - ULiège > Département d'électricité, électronique et informatique (Institut Montefiore) > Smart grids

Language :

English

Title :

Review on Artificial Intelligence-Based Fault Location Methods in Power Distribution Networks

Publication date :

2023

Journal title :

Energies

ISSN :

1996-1073

Publisher :

Multidisciplinary Digital Publishing Institute (MDPI), Switzerland

Volume :

Issue :

Pages :

4636

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://www.mdpi.com/1996-1073/16/12/4636/pdf

Available on ORBi :

since 08 March 2024

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