Fault diagnosis of the transformer based on QPSO-SVM

Abstract In view of the shortcomings of traditional dissolved gas analysis technology low diagnostic veracity and low intelligence, this paper proposes to use QPSO to optimize the nuclear argument in the support vector machine (SVM), and on this basis, dissolved gas analysis (DGA) technology is used to diagnosis transformer faults. Firstly, the transformer data is preprocessed by DGA technology, and the processed data is used as the input amount of fault characteristics. Secondly, for the optimization of core parameters in SVM, the QPSO algorithm is combined with fault data for training and acquisition. Finally, five kinds of feature inputs are added to the model for training, and the trained multi-classification correlation vector machine is used to diagnose the test data. After case studies and comparative experimental analysis, the diagnostic accuracy of this method is as high as 94.74%, and relatively with SVM, PSO-SVM, and RVM methods, the accuracy is increased by 5.11%, 2.12%, and 2.12%, respectively.