Power Transformer Fault Classification Based on Dissolved Gas Analysis by Implementing Bootstrap and Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{Shintemirov:2009:ieeeSMC-C,

• author = "A. Shintemirov and W. Tang and Q. H. Wu",
• title = "Power Transformer Fault Classification Based on Dissolved Gas Analysis by Implementing Bootstrap and Genetic Programming",
• journal = "IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews",
• year = "2009",
• month = jan,
• volume = "39",
• number = "1",
• pages = "69--79",
• keywords = "genetic algorithms, genetic programming, Bootstrap, dissolved gas analysis (DGA), fault classification, feature extraction, genetic programming, K-nearest neighbour (KNN), neural networks, power transformer, support vector machine (SVM)",
• DOI = "doi:10.1109/TSMCC.2008.2007253",
• ISSN = "1094-6977",
• size = "11 pages",
• 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 used to approximately equalise 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 neighbour (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.",
• notes = "Also known as \cite{4717246}",

}

Genetic Programming entries for Almas Shintemirov W Tang Henry Wu

Citations