Operation and maintenance strategy of traction transformer based on CBR and RBR

Traction transformer is vulnerable to high voltage, high current, mechanical stress and other environmental factors, which may cause heating, discharging, poor insulation and other faults. In order to formulate a reasonable operation and maintenance strategy to improve the level of fault treatment of traction transformer in operation, a decision-making method based on RBR(Rule-Based Reasoning) and CBR(Case-Based Reasoning) for operation and maintenance of traction transformer is proposed. Firstly, all the key parameters reflecting the state of traction transformer are acquired by RBR, and the initial maintenance scheme is obtained according to the knowledge of rule base. Then case retrieval algorithm is designed to match the similar case in the existing condition-based maintenance case base, and the maintenance strategy can be extracted. Finally, case retrieval results are modified and reused according to the preliminary scheme of RBR, and the optimal operation and maintenance strategy is synthesized to guide the maintenance work. 60 cases are collected to verify the accuracy of the fusion decision-making model, the results show that the average decision-making accuracy can reach 81.67%. Through experiments, it can be judged that the increase of the number of source cases is positively correlated with the decision-making accuracy. © 2020, Electric Power Automation Equipment Editorial Department. All right reserved.