Transformer Failure Diagnosis based on VNWOA-MRVM

This paper introduces a new methodology for diagnosing transformer failure to enhance diagnostic accuracy based on the combination of the Von Neumann Whale Optimization Algorithm (VNWOA) and the multi-categorical correlation vector machine (MRVM). Firstly, the whale algorithm is improved by using the principle of Von Neumann topology to increase the converge velocity and the optimization finding veracity of the whale algorithm by constructing a VN topology for each individual whale. Secondly, the VNWOA is applied to the MRVM arithmetic to find the optimal kernel features argument and penalty factor, and a diagnostic model of VNWOA-MRVM is advanced for failure diagnosis of the test data. Finally, the collected DGA data of 276 oil-immersed transformers are classified into exercise data and test data at a rate of 2: 1 for case analysis. After calculation, the accuracy of the proposed method can reach 95.65%, which is improved by 5.95%, 3.56%, 4.76% and 2.37% compared with SVM, PSO-SVM, M-RVM and PSO-RVM methods, respectively.