Prediction of dissolved gas Content in transformer oil based on Genetic Programming and DGA

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@InProceedings{WeiChang:2011:TMEE,
  author =       "Wei Chang and Ning Hao",
  title =        "Prediction of dissolved gas Content in transformer oil
                 based on Genetic Programming and DGA",
  booktitle =    "International Conference on Transportation,
                 Mechanical, and Electrical Engineering (TMEE 2011)",
  year =         "2011",
  month =        "16-18 " # dec,
  pages =        "1133--1136",
  address =      "Changchun, China",
  size =         "4 pages",
  abstract =     "Genetic Programming (GP), which is suitable for
                 prediction, is combined with transformer oil dissolved
                 gas analysis (DGA), and also a method of the prediction
                 of dissolved gas Content in transformer oil based on GP
                 classification algorithm is proposed, so as to
                 predicting the operational status and the latent faults
                 of a power transformer effectively. The comparative
                 results show that GP model can improve the prediction
                 accuracy effectively.",
  keywords =     "genetic algorithms, genetic programming, DGA, GP
                 classification algorithm, dissolved gas content
                 prediction, power transformer, transformer oil
                 dissolved gas analysis, chemical analysis, power
                 transformer insulation, transformer oil",
  DOI =          "doi:10.1109/TMEE.2011.6199404",
  notes =        "Also known as \cite{6199404}",
}

Genetic Programming entries for Wei Chang Ning Hao

Citations