Paper Info
Title
Adaptive neuro-fuzzy algorithm to estimate effective wind speed and optimal rotor speed for variable-speed wind turbine.
Abstract
The precise measurement of effective wind speed is a crucial task and has huge impact on wind turbine output power, safety and control performance. In this study, a hybrid intelligent learning based adaptive neuro-fuzzy inference system (ANFIS) is proposed for online estimation of effective wind speed from instantaneous values of wind turbine tip speed ratio (TSR), rotor speed and mechanical power. The artificial neural network (ANN) adjusts the parameters of fuzzy membership functions (MFs) using hybrid optimization method. The estimated value of effective wind speed is further utilized to design the optimal rotor speed estimator for maximum power point tracking (MPPT) of variable-speed wind turbine (VSWT). Both estimators are implemented in MATLAB and their performance is investigated for national renewable energy laboratory (NREL) offshore 5 MW baseline wind turbine. The simulation results show the effectiveness of proposed method. The proposed scheme is computationally intelligent, easy to implement and more reliable for fast estimation of effective wind speed and optimal rotor speed.
Year
DOI
Venue
2018
10.1016/j.neucom.2017.07.022
Neurocomputing
Keywords
Field
DocType
Wind turbine,Tip speed ratio,Rotor speed,Mechanical power,Power coefficient,ANFIS
Neuro-fuzzy,Tip-speed ratio,Wind speed,Control theory,Maximum power point tracking,Variable speed wind turbine,Turbine,Adaptive neuro fuzzy inference system,Power optimizer,Mathematics
Journal
Volume
ISSN
Citations 
272
0925-2312
5
PageRank 
References 
Authors
0.49
12
2
Name
Order
Citations
PageRank
Aamer Bilal Asghar191.70
Xiaodong Liu249228.50