Research On Transformer Condition Based Maintenance And Fault Diagnosis Based On RCM

The construction of new power systems has brought profound changes to the power supply structure and power grid structure,posing new challenges to the reliability of power equipment,including the core equipment transformers in the power grid.The operating status of transformers directly affects the safety and stability of power grid operation.Therefore,in the new power system,transformers need to have higher reliability and safety to meet the requirements of grid operation.So transformer condition maintenance and fault diagnosis is an extremely important project.In order to analyze the fault mode of transformers more systematically and intuitively,and improve the reliability of transformers has become an increasingly important issue for the power grid.On the basis of the research on the typical fault knowledge of power transformers,this paper combines RCM theory and Bayesian network and other related theories to express the transformer fault knowledge in a knowledge-based way,and then carries out the research on the key technologies of transformer condition based maintenance and fault diagnosis centered on reliability.Firstly,aiming at the correlation between the structure and faults of transformer equipment,and facilitating a more systematic analysis of transformer equipment,a transformer fault diagnosis analysis based on RCM theory is proposed.Obtain transformer fault knowledge comprehensively using FTA and FMEA analysis methods;On this basis,the main faults of transformers are systematically analyzed using RCM theory.During the RCM process,FTA and FMEA are commonly used to determine which failure modes may cause equipment failures,and what preventive measures should be taken to reduce or eliminate potential faults;Structured representation of transformer equipment and fault knowledge based on ontology theory improves knowledge management efficiency.Through cases,a fault knowledge base covering six groups of components was established with a 500kV transformer as the object.Secondly,based on the high accuracy and real-time characteristics of Bayesian networks,a transformer fault diagnosis method based on Bayesian networks is constructed.RCM is used to analyze the status parameters and fault modes in the collected historical operation data of transformers,discretize them,and determine Bayesian network nodes.Based on Bayesian theorem,a fault diagnosis model based on Bayesian network is constructed;Combining the fault features extracted based on RCM and the established fault knowledge ontology,the probability of transformer fault occurrence is analyzed and calculated using Bayesian network model;The effectiveness of the model is verified through a transformer case,and the cause of the fault is determined.Finally,based on the theoretical research work of transformer fault diagnosis,a prototype system for transformer fault diagnosis was designed and developed,providing support for the subsequent engineering applications of transformer condition detection and fault diagnosis systems.