Reliability Analysis of CVT Online Monitoring Device Based on Bayesian Network

Xu Chen, Haomiao Zhang, Chao Zhang, Yinzhe Xu, Yu Yan, Yuntao Zhao (), Xuhui Chen and Rui Ren
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Xu Chen: State Grid Ningxia Marketing Service Center (State Grid Ningxia Metrology Center), Yinchuan 750001, China
Haomiao Zhang: State Grid Ningxia Marketing Service Center (State Grid Ningxia Metrology Center), Yinchuan 750001, China
Chao Zhang: State Grid Ningxia Marketing Service Center (State Grid Ningxia Metrology Center), Yinchuan 750001, China
Yinzhe Xu: State Grid Ningxia Marketing Service Center (State Grid Ningxia Metrology Center), Yinchuan 750001, China
Yu Yan: State Grid Ningxia Marketing Service Center (State Grid Ningxia Metrology Center), Yinchuan 750001, China
Yuntao Zhao: School of Artificial Intelligence and Automation, Wuhan University of Science and Technology, Wuhan 430041, China
Xuhui Chen: School of Artificial Intelligence and Automation, Wuhan University of Science and Technology, Wuhan 430041, China
Rui Ren: School of Artificial Intelligence and Automation, Wuhan University of Science and Technology, Wuhan 430041, China

Energies, 2025, vol. 18, issue 18, 1-12

Abstract: To address the challenges of equipment reliability assessment in the context of intelligent power systems, especially the shortcomings of traditional methods in dealing with multi-factor coupling and uncertain fault inference of CVT (capacitive voltage transformer) online monitoring devices, this study proposes a reliability analysis method based on Bayesian networks (BNs). The research aims to evaluate the reliability of CVT online monitoring devices, identify key risk factors, and optimize maintenance strategies. Firstly, a Bayesian network reliability model is constructed for the CVT online monitoring device, defining key influencing factors such as environmental factors and component quality as network nodes, and establishing conditional probability dependency relationships between nodes. Subsequently, the MATLAB R2021b simulation platform was used to simulate the system’s operating status under different combinations and scenarios. The experimental results indicate that the combination of high-temperature and high-humidity environments has the most significant impact on reliability; among the component factors, the failure of the data acquisition and processing unit has the greatest impact on system reliability; wiring process issues pose a greater threat to reliability than mechanical fixing issues; and regular maintenance can significantly improve system reliability. This method validates the effectiveness of Bayesian networks in dynamic reliability analysis of CVT online monitoring devices, which can accurately locate high-risk factors and support maintenance decision optimization.

Keywords: Bayesian network; online monitoring device; reliability analysis; voltage transformer (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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