Transformer Fault Diagnosis Based on QPSO-MRVM and Multi-information Fusion

To address the situation of small transformer fault sample data and low accuracy, In this paper, quantum particle swarm optimization (QPSO) and multi-classification correlation vector machine (M-RVM) are combined with the fusion of multi-feature input information for transformer fault diagnosis. First, to compensate for the relatively single fault feature input, the data of the DGA technique, IEC tri-ratio method, and Doernenburg ratio method are feature preprocessed. Second, the QPSO algorithm is combined with the fault data for training to obtain the kernel parameters in M-RVM. Finally, ten feature inputs are added to the model for training, and the trained multi-classification correlation vector machine is used to perform fault diagnosis on the test data. After example and comparative experimental analysis, the diagnostic accuracy of the method is as high as 94.85%, which is 8.24%, 4.57%, 4.57%, and 3.15% higher than that of SVM, PSO-SVM, M-RVM, and PSO-RVM, respectively.