Reliability Analysis of CVT Online Monitoring Device Based on Bayesian Network

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.