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

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@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