首页|Computational intelligence for preventive maintenance of power transformers

Computational intelligence for preventive maintenance of power transformers

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  • NSTL
  • Elsevier
Power transformers are an indispensable equipment in power transmission and distribution systems, and failures or hidden defects in power transformers can cause operational and downtime issues in power supply, resulting in economic and resource losses. Therefore, it is highly desirable to put in place intelligent preventive maintenance measures to diagnose and evaluate the condition of power transformers. Although conventional methods have achieved success in detecting problems associated with power transformers, their adoption rate in practical environments is still far from universal. The advent of Computational Intelligence (CI) models offers useful potential to complement the existing diagnostic practices of power transformers. In this paper, we provide a review on various computational intelligence techniques for fault detection and diagnosis pertaining to preventive maintenance of power transformers. An overview of each representative CI approach is presented to facilitate researchers in selecting an appropriate method for a specific problem at hand. We carry out a broad discussion on numerous concerns and challenges that are missing from the current literature, which, nevertheless, need to be addressed seriously. We identify the research gaps in the literature, and suggest the way forward in research that will in the long run enhance power system reliability by embracing CI approaches into business operations in an effort to realize the Sustainable Development Goal (SDGs) advocated by the United Nation, primarily SDG7: Clean and Affordable Energy and SDG9: Industry, Innovation and Infrastructure. (C) 2021 Elsevier B.V. All rights reserved.

Power transformerComputational IntelligencePreventive maintenanceFault detection and diagnosisPARTICLE-SWARM-OPTIMIZATIONDISSOLVED-GAS ANALYSISFREQUENCY-RESPONSE ANALYSISARTIFICIAL NEURAL-NETWORKEXTREME LEARNING-MACHINEPARTIAL DISCHARGE LOCALIZATIONSUPPORT VECTOR MACHINEFAULT-DIAGNOSISFUZZY-LOGICINSULATING OIL

Wong, Shen Yuong、Ye, Xiaofeng、Guo, Fengkai、Goh, Hui Hwang

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Xiamen Univ Malaysia

Guangxi Univ

2022

10.1016/j.asoc.2021.108129

Applied Soft Computing

Applied Soft Computing

EISCI
ISSN：1568-4946
年,卷(期)：2022.114
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