Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 1
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Application of genetic programming for multicategory patternclassification
IEEE Transactions on Evolutionary Computation
Feature generation using genetic programming with application to fault classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Association rule mining-based dissolved gas analysis for fault diagnosis of power transformers
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An adaptive knowledge evolution strategy for finding near-optimal solutions of specific problems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Data mining for oil-insulated power transformers: an advanced literature survey
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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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.