A Data-Driven Approach for Monitoring Blade Pitch Faults in Wind Turbines

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@Article{Kusiak:2011:ieeeTSusE,
  author =       "Andrew Kusiak and Anoop Verma",
  title =        "A Data-Driven Approach for Monitoring Blade Pitch
                 Faults in Wind Turbines",
  journal =      "IEEE Transactions on Sustainable Energy",
  year =         "2011",
  month =        jan,
  volume =       "2",
  number =       "1",
  pages =        "87--96",
  abstract =     "A data-mining-based prediction model is built to
                 monitor the performance of a blade pitch. Two blade
                 pitch faults, blade angle asymmetry, and blade angle
                 implausibility were analysed to determine the
                 associations between them and the
                 components/subassemblies of the wind turbine. Five
                 data-mining algorithms have been studied to evaluate
                 the quality of the models for prediction of blade
                 faults. The prediction model derived by the genetic
                 programming algorithm resulted in the best accuracy and
                 was selected to perform prediction at different time
                 stamps.",
  keywords =     "genetic algorithms, genetic programming, blade angle
                 asymmetry, blade angle implausibility, blade pitch
                 faults monitoring, data-mining-based prediction model,
                 genetic programming algorithm, wind turbines, data
                 mining, power engineering computing, wind turbines",
  DOI =          "doi:10.1109/TSTE.2010.2066585",
  ISSN =         "1949-3029",
  notes =        "Also known as \cite{5547006}",
}

Genetic Programming entries for Andrew Kusiak Anoop Verma

Citations