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Recent Advances in Electrical & Electronic Engineering

Editor-in-Chief

ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

Review Article

A Review of Bayesian Networks Applications for Electrical Systems

Author(s): Zerrouki Hamza*

Volume 15, Issue 2, 2022

Published on: 24 March, 2022

Page: [93 - 103] Pages: 11

DOI: 10.2174/2352096515666220303161713

Price: $65

Abstract

The development in the field of electrical energy has been growing increasingly due to the need for this energy in daily life. The reliability and safety of electrical power systems and equipment represent complex problems that are difficult to solve by conventional methods such as Fuzzy Logic and Artificial Neural Networks. Bayesian network is recently used to overcome some limitations in conventional methods. This paper represents a bibliographic review about the use of Bayesian networks in the field of electric systems. This paper seeks to answer the following questions: (i) What are the areas of interest? (ii) What are the most active countries in this field?? (iii) Who are the most participating authors in this field? (iv) which year witnessed the largest number of publications? (v) What is the most widespread field related to this research? (vi) What is the most used system in terms of application? This field witnesses a slight increase in the number of publications in the last two decades (1999-2021), with a note of a sharp increase in publishing in the last two years. It is observed that reliability assessment and fault diagnosis are the most common fields. Furthermore, it is found that China and USA are the active countries in this field. Electric Power and Energy Systems Journal and IEEE Transactions on Power Systems Journal are the lead source documents, and most of the documents used electric power systems as an application. This paper will help researchers to know the versability features of BN and to identify the gaps in the use of BN in electric domains.

Keywords: Bayesian network, electric domains, electrical power systems, reliability assessment, fault diagnosis, annual load variation.

Graphical Abstract

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