An adaptive neuro-fuzzy inference system approach for prediction of power factor in wind turbines

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dc.contributor.authorAta R.
dc.date.accessioned2024-07-22T08:21:44Z
dc.date.available2024-07-22T08:21:44Z
dc.date.issued2009
dc.description.abstractThis paper introduces an adaptive neuro-fuzzy inference system (ANFIS) model for predicting the power factor of a wind turbine. This model based on the parameters involved for NACA 4415 and LS- 1 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 power factor was taken as output variable. After a successful learning and training process the proposed model produced reasonable mean errors. The results on a testing data indicate that the ANFIS model is found to be more successful than the ANN approach in estimating the power factor.
dc.identifier.issn13030914
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/18760
dc.language.isoEnglish
dc.subjectElectric power factor
dc.subjectEngines
dc.subjectFuzzy inference
dc.subjectFuzzy systems
dc.subjectWell pumps
dc.subjectWind turbines
dc.subjectAdaptive neuro-fuzzy inference system
dc.subjectANFIS
dc.subjectANFIS model
dc.subjectBlade numbers
dc.subjectEnergy
dc.subjectInput variables
dc.subjectLearning and training
dc.subjectMean errors
dc.subjectModel development
dc.subjectModel-based
dc.subjectOutput variables
dc.subjectPower factor
dc.subjectPower factors
dc.subjectSchmitz
dc.subjectTesting data
dc.subjectWind power
dc.titleAn adaptive neuro-fuzzy inference system approach for prediction of power factor in wind turbines
dc.typeArticle

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