Enhancing Transformer Fault Identification Through PCA-SVM Approach

As a key component of the power system, the health of the transformer directly affects the stability and security of the entire power grid. Therefore, accurate identification and classification of transformer fault types is important for the prevention of large-scale power outages. This study aims to improve the accuracy and efficiency of transformer fault diagnosis by combining principal component analysis (PCA) and support vector machine (SVM). Firstly, thermal imaging maps of transformers under multiple types of fault states are collected using infrared thermography, and after image preprocessing, dimensionality reduction is carried out using the PCA method to extract the most representative feature vectors as inputs, reducing computational complexity and eliminating noise interference. A multiclassification SVM-based fault classification model is constructed, which employs a radial basis function (RBF) kernel to improve the nonlinear processing capability of the classifier. Results of the study show that the feature input optimized by PCA can significantly improve the performance of the SVM classifier. The experimental results show that the accuracy of the method on the transformer fault dataset reaches more than 95%, which is significantly better than the traditional single classification method. The method not only can effectively improve the accuracy of fault detection, but also has good generalization ability and fast processing speed, providing a reliable technical means for transformer condition monitoring and fault warning.