A Fault Diagnosis Method for Power Transformer Using Canonical Variate Analysis and Support Vector Machine

Abstract

The transformer is one of the most important units in the power grid. Due to the potential failures and costs of the power system, it is necessary to pay attention to the fault diagnosis of power transformers. This paper proposes a fault diagnosis method based on Canonical Variate Analysis and Support Vector Machine (CVA-SVM). As a system identification method, CVA is widely used for fault detection because of its ability to identify multivariate state space models using experimental data. The support vector machine is a new machine learning method and is a powerful tool for solving problems with nonlinear and non-Gaussian distributed data. Dissolved gas analysis (DGA) has shown great potential for detecting faults in power transformers. For fault diagnosis based on DGA, a CVA model is first constructed for the process variables to generate a series of feature vectors, and then the fault types are classified using SVM. A real power transformer process is employed to verify the effectiveness of the proposed method.