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

Abstract

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.