Transformer Fault Synthetic Diagnosis Method Based on Fusion of Multi-Neural Networks and Evidence Theory in Cloud Computing

Abstract

Abstract As the core equipment of power system, the failure of transformer will result in paralysis of the whole system. Therefore, timely diagnosis of transformer fault types is the most important task to ensure the stable operation of power system. Through the cloud computing method, the neural network is combined with the advantages of evidence theory, by comparing the chromatographic data of the transformer and the electrical test data. A comprehensive diagnosis method of transformer fault based on the fusion of multi neural network and evidence theory based on cloud computing is proposed. The fault diagnosis performance of the transformer is measured by the inspection of the fault transformer. The results show that compared with the traditional single data comparison, this method can improve the reliability and accuracy of diagnosis by comparing many kinds of data.