Transformer Fault Diagnosis Using Support Vector Machines

Transformer fault diagnosis based on Support Vector Machines (SVM) is a technical solution that utilizes machine learning methods to identify potential transformer faults. This approach establishes classification models by analyzing dissolved gas data in transformer oil (such as H2, CH4, C2H4), effectively distinguishing between normal operating conditions and fault types (like partial discharge, overheating).

The core methodology involves extracting gas concentration ratios as feature vectors and using Support Vector Machines to construct optimal classification hyperplanes in high-dimensional space. SVM's ability to handle nonlinear data through kernel functions (such as RBF kernel) makes it particularly suitable for complex fault patterns involving multiple gas parameters. Compared to traditional threshold-based methods, this approach can integrate multi-dimensional features, significantly improving diagnostic accuracy. Code implementation typically involves feature normalization and kernel parameter optimization using grid search or cross-validation techniques.

In practical applications, attention must be paid to sample imbalance issues, which can be addressed using SMOTE oversampling algorithms or class weight adjustment techniques. The key advantage of this technology lies in its strong generalization capability with small sample sizes, making it suitable for high-reliability fault early warning requirements in power systems. Future developments could integrate deep learning architectures to further optimize feature extraction processes through automated feature engineering.