Research on Transformer Fault Diagnosis with Support Vector Machine Optimized by Slime Mold Algorithm Based on Principal Component Analysis

In view of the low accuracy of transformer fault diagnosis using support vector machine (SVM), This paper proposes a transformer fault diagnosis with support vector machine optimized by slime mold algorithm (SMA) based on principal component analysis (PCA). Firstly, PCA was used to reduce the dimensionality of high-dimensional data features and extract key data features. Secondly, SMA was used to optimize the internal parameters of SVM to improve the diagnosis performance of vector machine. Finally, the extracted key data features were input into the SMA-SVM fault diagnosis model for transformer fault diagnosis. The example analysis shows that the proposed method has high diagnostic accuracy and strong practicability, and provides a way to solve the practical engineering problems of fault diagnosis.