An Information Granulated Based SVM Approach for Anomaly Detection of Main Transformers in Nuclear Power Plants

Abstract

The main transformer is critical equipment for economically generating electricity in nuclear power plants (NPPs). Dissolved gas analysis (DGA) is an effective means of monitoring the transformer condition, and its parameters can reflect the transformer operating condition. This study introduces a framework for main transformer predictive-based maintenance management. A condition prediction method based on the online support vector machine (SVM) regression model is proposed, with the input data being preprocessed using the information granulation method, and the parameters of the model are optimized using the particle swarm optimization (PSO) algorithm. Using DGA data from the NPP data acquisition system, two experiments are designed to verify the trend tracing and prediction envelope ability of main transformers installed in NPPs with different operating ages of the proposed model. Finally, how to use this framework to benefit the maintenance plan of the main transformer is summarized.