Transformer Fault Diagnosis Based On Deep Brief Sparse Autoencoder

Dissolved gas analysis (DGA) is an effective way to diagnose the internal faults of transformer. This paper proposes a deep belief sparse autoencoder (DBSAE), which can be performed on DGA data to detect the transformer fault. The proposed method is constructed by two layers of sparse autoencoder and one layer of back propagation network. DBSAE adopts the unsupervised training to collect the features of DGA raw data, and the back propagation network with the supervised training could realize the transformer faults diagnosis. The experiments are performed on a real DGA dataset. The experimental results verify the effectiveness of the proposed method, which can extract features from the original data, and achieve a superior correct differentiation rate which reached 95.4% in average accuracy on transformer fault diagnosis.