A Methodology to Diagnose Transformer Faults Based on Principal Components Analysis and Artificial Neural Network
- Resource Type
- Conference
- Authors
- Du, Yu; Wang, Zhiwu; Feng, Guangming; Rao, Shaowei; Zou, Guoping; Yang, Shiyou
- Source
- 2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2) Energy Internet and Energy System Integration (EI2), 2022 IEEE 6th Conference on. :1186-1189 Nov, 2022
- Subject
- Power, Energy and Industry Applications
Training
Artificial neural networks
System integration
Dissolved gas analysis
Feature extraction
Transformers
Data mining
transformer
fault diagnosis
dissolved gas analysis
artificial neural network
principal components analysis
- Language
A methodology, combining the principal components analysis (PCA) and the artificial neural network (ANN), to diagnose transformer faults based on dissolved gas analysis (DGA) data is proposed. The main features extracted from DGA data by the PCA are used as the input of the ANN to train and test the classifier. The proposed methodology is tested with practical transformer fault data. The test results show that a higher diagnosis accuracy can be achieved by using the main features extracted by the PCA as compared to that by the original features or the optimal feature combinations obtained by feature selection methods.