Sensing Incipient Faults in Power Transformers Using Bi-Directional Long Short-Term Memory Network
Suchandan K Das, Ashish Paramane, Soumya Chatterjee, U. Mohan Rao
Abstract
Dissolved gas analysis (DGA) is a standard technique for detecting incipient faults in oil-immersed power transformers. However, fault sensing accuracy depends on feature selection and the machine learning (ML) algorithm used for fault classification. To overcome these two issues, 50 features were extracted from a DGA dataset of 2242 samples obtained from local power utilities. Two state-of-the-art deep learning algorithms, i.e., long-short-term memory (LSTM) and bi-directional long-short-term memory (bi-LSTM), were used to classify different types of faults and normal conditions. In addition, the proposed method was further verified on IEC TC 10 database. Investigations revealed that both LSTM and bi-LSTM performed better than conventional ML classifiers.