Genetic programming feature extraction with bootstrap for dissolved gas analysis of power transformers

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Shintemirov:2009:PES,
  author =       "A. Shintemirov and W. H. Tang and Q. H. Wu and 
                 J. Fitch",
  title =        "Genetic programming feature extraction with bootstrap
                 for dissolved gas analysis of power transformers",
  booktitle =    "IEEE Power Energy Society General Meeting, PES '09",
  year =         "2009",
  month =        jul,
  pages =        "1--6",
  keywords =     "genetic algorithms, genetic programming, K-nearest
                 neighbor classifiers, artificial neural network,
                 dissolved gas analysis, genetic programming feature
                 extraction, power transformer fault classification,
                 support vector machine, fault diagnosis, feature
                 extraction, neural nets, power engineering computing,
                 power transformers, support vector machines",
  DOI =          "doi:10.1109/PES.2009.5275606",
  ISSN =         "1944-9925",
  abstract =     "This paper discusses a feature extraction technique
                 with genetic programming (GP) and bootstrap to improve
                 interpretation accuracy of dissolved gas analysis (DGA)
                 fault classification in power transformers, dealing
                 with highly versatile or noise corrupted data. Initial
                 DGA data are preprocessed with bootstrap to equalize
                 the sample numbers for different fault classes, thus
                 improving subsequent extraction of classification
                 features with GP for each fault class. 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 test results indicate that
                 the proposed preprocessing approach can significantly
                 improve the accuracy of power transformer fault
                 classification based on DGA data.",
  notes =        "Also known as \cite{5275606}",
}

Genetic Programming entries for Almas Shintemirov Wenhu Tang Henry Wu J Fitch

Citations