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

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