Open AccessConvolutional Bi-directional Long Short Term Memory Network based Dynamic Fault Diagnosis for Transformer DGA
Author(s) -
Lin Luo,
Xiaodeng Pei,
Shuai Chen
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1914/1/012045
Subject(s) - computer science , convolutional neural network , transformer , artificial intelligence , artificial neural network , pooling , dissolved gas analysis , pattern recognition (psychology) , support vector machine , data mining , machine learning , engineering , transformer oil , voltage , electrical engineering
Dissolved gas analysis (DGA) method is one of the important methods to detect early internal faults in transformers. Aiming at the shortcomings of feature extraction and online modelling in the analysis model based on shallow layer, a transformer DGA online fault diagnosis method combining convolutional neural network and bidirectional long short term memory network is proposed in this paper. By using multi-time characteristic gas data as the input of the diagnostic model, combined with convolution and pooling, the fault sensitive features were extracted, and the time dimension features were extracted by using the recursive neural network with gated structure. Under various evaluation criteria, it is compared with convolutional neural network, bidirectional long short-term memory (Bi-LSTM) network and dynamic support vector machine. The experimental results show that the online transformer diagnosis model proposed in this paper, which considers both time and space characteristics, has higher prediction accuracy.