Transformer Fault Diagnosis Method Based on Multidimensional Feature Fusion and Self-Adaptive Synthetic Over-Sampling Using a Least Squares Support Vector Machine Optimized by Experience Exchange Strategy

As critical equipment in power systems, the reliable operation of power transformers is directly linked to the overall safety of the power grid. Traditional fault diagnosis methods based on dissolved gas analysis generally rely on a single gas feature, which inevitably causes misjudgment and suffers from inadequate accuracy. This paper proposes a transformer fault diagnosis model that integrates multidimensional features with intelligent algorithms. The model adopts the volume fractions of five key gases from dissolved gas analysis as the fundamental features and further introduces the three-ratio coding features derived from these five gases. The two categories of features are jointly constructed into a multidimensional input vector. In the method design, firstly, the self-adaptive synthetic over-sampling (SASYNO) method is adopted to address the sample imbalance problem; secondly, the least squares support vector machine (LSSVM) is optimized using the experience exchange strategy (EES), and the classification performance of the model is improved. Experimental results demonstrate that the diagnostic model built on multidimensional features significantly outperforms traditional methods in accuracy, thereby providing an effective new approach for transformer fault diagnosis.