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

Dissolved Gas Analysis(DGA)is an important method for oil-immersed transformer fault diagnosis.However,collecting labelled DGA data is difficult because the determi-nation 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 pro-posed 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.