A Transformer Hot Spot Fault Diagnosis Method Combining Ultrasonic Sensing Technology and PSO-SVM Algorithm

Equipment failures often occur in the internal insulation of power transformers, mainly due to insulation aging, poor operating conditions, and unqualified manufacturing quality of power transformers. The insulating ability of the outer layer oil paper of the transformer winding is crucial, and the key factor affecting oil paper insulation is the hot spot fault of the transformer winding. Therefore, hot spot fault detection of transformers in operation is of great significance. At present, the existing hot spot temperature measurement methods at home and abroad are difficult to apply to transformers that have already been put into operation. Therefore, this paper proposed a transformer hot spot fault diagnosis method based on the combination of ultrasonic sensing technology and PSO-SVM algorithm. The three-dimensional model of the transformer was built using COMSOL finite element simulation software to simulate the dynamic response of ultrasonic signals received by sensors under different hot spot types, and then feature parameters were extracted based on the simulation data. Finally, the particle swarm optimization (PSO) and support vector machine (SVM) algorithm were combined to achieve the transformer hot spot temperature retrieval and fault location. The results indicated that the PSO-SVM model could effectively identify hot spot faults and achieve non-destructive online monitoring of internal hot spots in transformers, providing a new approach for the field of transformer hot spot fault diagnosis.