A novel two‐stage Dissolved Gas Analysis fault diagnosis system based semi‐supervised learning

Abstract

Dissolved Gas Analysis (DGA) is an important method for oil-immersed transformer fault diagnosis. However, collecting labelled DGA data is difficult because the determination of the transformer fault is time-consuming and expensive in the transformer substation, but DGA data without labels is easier to obtain. Therefore, the paper proposed a semi-supervised two-stage diagnostic system based DGA by using less labelled samples. The two-stage system includes a novel semi-supervised feature selection based Genetic Algorithm (GA) and Support Vector Machine (SVM) model (SSL-FS-GASVM) for selecting optimal features and a novel semi-supervised transformer fault diagnosis model based improved Artificial Fish Swarm Algorithm (AFSA) and SVM (SSL-IAFSA-SVM) for optimising the SVM parameter. Finally, the performances of SSL-FS-GASVM and SSL-IAFSA-SVM models are tested and compared with traditional supervised diagnostic models combined with other optimisation methods, respectively. The results show that the proposed two-stage system works in optimising features and parameters and has strong robustness in solving small sample classification problems.