Research on Transformer Condition Maintenance and Fault Diagnosis Based on RCM and Bayesian Networks

The operational status of transformers directly impacts the safety and stability of power grid operations. In modern power systems, transformers must exhibit higher reliability and safety to meet grid requirements. Therefore, transformer status maintenance and fault diagnosis are critical engineering tasks. This paper builds on research into common transformer faults and integrates theories such as Reliability-Centered Maintenance (RCM) and Bayesian networks, to formalize transformer fault knowledge. It then explores key technologies for RCM-based transformer maintenance and fault diagnosis. First, it analyzes the relationship between transformer structure and faults using RCM principles for fault diagnosis. Next, using Bayes' theorem, it develops a fault diagnosis model grounded in Bayesian networks. By incorporating fault characteristics extracted using RCM and an established fault knowledge framework, the Bayesian network model is used to calculate the probability of transformer faults. A case study on a specific transformer confirms the validity of the model and identifies the causes of faults.