Early fault diagnosis of transformer winding based on ReliefF-mRMR leakage magnetic field feature optimization and improved LSSVM
摘要
Aiming at the problem of complex transformer structure and small amount of early winding fault sample data, in order to improve the accuracy of transformer winding fault type diagnosis, this paper proposes an early fault diagnosis model of transformer winding based on ReliefF-mRMR leakage magnetic field feature optimization and POA-LSSVM. Firstly, the consistency of the physical entity of the transformer and the simulation model is verified. The leakage magnetic field information of the early fault of the transformer winding is taken as the fault characteristic state quantity, and the fault characteristics of the leakage magnetic field are optimized and the key features are extracted by the ReliefF and mRMR feature selection algorithms. After optimization, the fault feature is input to the least squares support vector machine (LSSVM) for fault diagnosis, and the LSSVM parameters are optimized by the pelican optimization algorithm (POA). The results show that the POA-LSSVM fault diagnosis model after fault optimization by ReliefF-mRMR algorithm can effectively distinguish different early fault types of transformer windings, and compared with GA-LSSVM algorithm, PSO-LSSVM algorithm and POA-LSSVM algorithm, the fault diagnosis efficiency and classification accuracy are significantly improved. Finally, the dynamic simulation experiment of transformer is carried out to verify the effectiveness of the fault diagnosis model.