Power Transformer Fault Diagnosis Method Based on Machine Learning

Abstract

Transformer is one of the most important power equipment in power systems. The normal operation of transformers is of great importance for the safety and stability of power grids. Therefore, transformer fault monitoring and diagnosis are very important to ensure the stability of power system. This paper summarizes the existing methods for transformer diagnosis. The traditional methods have some apparent disadvantages and limitations. These methods deal with static data and cannot be mapped to the objects at any time. This may cause a untimely detection and a big error. Therefore, a data-driven transformer fault diagnosis method is introduced to solve these problems. The paper summarizes the applications of expert learning, artificial neural network, support vector machine, deep learning and other machine learning methods in transformer fault diagnosis with the advantages and disadvantages of each method analyzed. And This paper summarizes the contribution of machine learning in transformer fault diagnosis. Finally, the paper summarizes and prospects the development of transformer fault diagnosis methods.