Abstract
This paper presents an intelligent fault classification approach to power transformer dissolved gas analysis (DGA), dealing with highly versatile or noise-corrupted data. Bootstrap and genetic programming (GP) are implemented to improve the interpretation accuracy for DGA of power transformers. Bootstrap preprocessing is utilized to approximately equalize the sample numbers for different fault classes to improve subsequent fault classification with GP feature extraction. GP is applied to establish classification features for each class based on the collected gas data. The features extracted with GP are then used as the inputs to artificial neural network (ANN), support vector machine (SVM) and K-nearest neighbor (KNN) classifiers for fault classification. The classification accuracies of the combined GP-ANN, GP-SVM, and GP-KNN classifiers are compared with the ones derived from ANN, SVM, and KNN classifiers, respectively. The test results indicate that the developed preprocessing approach can significantly improve the diagnosis accuracies for power transformer fault classification.
| Original language | English |
|---|---|
| Pages (from-to) | 69-79 |
| Number of pages | 11 |
| Journal | IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews |
| Volume | 39 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2009 |
| Externally published | Yes |
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Keywords
- Bootstrap
- Dissolved gas analysis (DGA)
- Fault classification
- Feature extraction
- Genetic programming
- K-nearest neighbor (KNN)
- Neural networks
- Power transformer
- Support vector machine (SVM)
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering
- Computer Science Applications
- Human-Computer Interaction
- Information Systems
- Software
Cite this
Power transformer fault classification based on dissolved gas analysis by implementing bootstrap and genetic programming. / Shintemirov, A.; Tang, W.; Wu, Q. H.
In: IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, Vol. 39, No. 1, 2009, p. 69-79.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Power transformer fault classification based on dissolved gas analysis by implementing bootstrap and genetic programming
AU - Shintemirov, A.
AU - Tang, W.
AU - Wu, Q. H.
PY - 2009
Y1 - 2009
N2 - This paper presents an intelligent fault classification approach to power transformer dissolved gas analysis (DGA), dealing with highly versatile or noise-corrupted data. Bootstrap and genetic programming (GP) are implemented to improve the interpretation accuracy for DGA of power transformers. Bootstrap preprocessing is utilized to approximately equalize the sample numbers for different fault classes to improve subsequent fault classification with GP feature extraction. GP is applied to establish classification features for each class based on the collected gas data. The features extracted with GP are then used as the inputs to artificial neural network (ANN), support vector machine (SVM) and K-nearest neighbor (KNN) classifiers for fault classification. The classification accuracies of the combined GP-ANN, GP-SVM, and GP-KNN classifiers are compared with the ones derived from ANN, SVM, and KNN classifiers, respectively. The test results indicate that the developed preprocessing approach can significantly improve the diagnosis accuracies for power transformer fault classification.
AB - This paper presents an intelligent fault classification approach to power transformer dissolved gas analysis (DGA), dealing with highly versatile or noise-corrupted data. Bootstrap and genetic programming (GP) are implemented to improve the interpretation accuracy for DGA of power transformers. Bootstrap preprocessing is utilized to approximately equalize the sample numbers for different fault classes to improve subsequent fault classification with GP feature extraction. GP is applied to establish classification features for each class based on the collected gas data. The features extracted with GP are then used as the inputs to artificial neural network (ANN), support vector machine (SVM) and K-nearest neighbor (KNN) classifiers for fault classification. The classification accuracies of the combined GP-ANN, GP-SVM, and GP-KNN classifiers are compared with the ones derived from ANN, SVM, and KNN classifiers, respectively. The test results indicate that the developed preprocessing approach can significantly improve the diagnosis accuracies for power transformer fault classification.
KW - Bootstrap
KW - Dissolved gas analysis (DGA)
KW - Fault classification
KW - Feature extraction
KW - Genetic programming
KW - K-nearest neighbor (KNN)
KW - Neural networks
KW - Power transformer
KW - Support vector machine (SVM)
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U2 - 10.1109/TSMCC.2008.2007253
DO - 10.1109/TSMCC.2008.2007253
M3 - Article
VL - 39
SP - 69
EP - 79
JO - IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
JF - IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
SN - 1094-6977
IS - 1
ER -