An adaptive neuro-fuzzy inference system approach for prediction of tip speed ratio in wind turbines

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dc.contributor.authorAta R.
dc.contributor.authorKocyigit Y.
dc.date.accessioned2024-07-22T08:20:45Z
dc.date.available2024-07-22T08:20:45Z
dc.date.issued2010
dc.description.abstractThis paper introduces an adaptive neuro-fuzzy inference system (ANFIS) model to predict the tip speed ratio (TSR) and the power factor of a wind turbine. This model is based on the parameters for LS-1 and NACA4415 profile types with 3 and 4 blades. In model development, profile type, blade number, Schmitz coefficient, end loss, profile type loss, and blade number loss were taken as input variables, while the TSR and power factor were taken as output variables. After a successful learning and training process, the proposed model produced reasonable mean errors. The results indicate that the errors of ANFIS models in predicting TSR and power factor are less than those of the ANN method. © 2010 Elsevier Ltd. All rights reserved.
dc.identifier.DOI-ID10.1016/j.eswa.2010.02.068
dc.identifier.issn09574174
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/18303
dc.language.isoEnglish
dc.subjectElectric power factor
dc.subjectErrors
dc.subjectForecasting
dc.subjectFuzzy inference
dc.subjectFuzzy neural networks
dc.subjectSpeed
dc.subjectWell pumps
dc.subjectWind power
dc.subjectWind turbines
dc.subjectAdaptive neuro-fuzzy inference system
dc.subjectANFIS model
dc.subjectArtificial neural-networks (ANN)
dc.subjectBlade numbers
dc.subjectInput variables
dc.subjectLearning and training
dc.subjectMean errors
dc.subjectModel development
dc.subjectOutput variables
dc.subjectPower factors
dc.subjectSchmitz
dc.subjectTip speed ratio
dc.subjectFuzzy systems
dc.titleAn adaptive neuro-fuzzy inference system approach for prediction of tip speed ratio in wind turbines
dc.typeArticle

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