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

被引：64
|
作者：
Ata, R. [2 ]
Kocyigit, Y. [1 ]
机构：
[1] Celal Bayar Univ, Dept Elect & Elect Engn, Muradiye, Manisa, Turkey
[2] Celal Bayar Univ, Dept Elect, TR-45700 Kirkagac, Manisa, Turkey
来源：
EXPERT SYSTEMS WITH APPLICATIONS | 2010年 / 37卷 / 07期
关键词：
Wind turbines; Tip speed ratio; Adaptive neuro-fuzzy inference system (ANFIS); Artificial neural-networks (ANN); Prediction;
D O I：
10.1016/j.eswa.2010.02.068
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This 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. (C) 2010 Elsevier Ltd. All rights reserved.
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页码：5454 / 5460
页数：7
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