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References (32)
-
(2007)
Wind Turbine Control Systems: Principles, Modeling and Gain Scheduling (1st edn) -
Junsong Wang, N. Tse, Zhiwei Gao (2011)
Synthesis on PI-based pitch controller of large wind turbines generatorEnergy Conversion and Management, 52
-
Liu Zhongpeng, Huo Feng, Xiao Shuowen, Zhang Xuesong, Zhu Shilong, Ji Guorui, D. Haitao, Feng Jian (2016)
Individual pitch control of wind turbine based on RBF neural network2016 35th Chinese Control Conference (CCC)
-
(2011)
A variable speed wind generator maximum power tracking based on adaptive neuro-fuzzy -
M. Soliman, O. Malik, D. Westwick (2011)
Multiple model multiple-input multiple-output predictive control for variable speed variable pitch wind energy conversion systemsIet Renewable Power Generation, 5
-
Yishuang Qi, Qingjin Meng (2012)
The Application of Fuzzy PID Control in Pitch Wind TurbineEnergy Procedia, 16
-
Satyabrata Sahoo, B. Subudhi, G. Panda (2016)
Pitch Angle Control for Variable Speed Wind Turbine Using Fuzzy Logic2016 International Conference on Information Technology (ICIT)
-
M. Mohandes, S. Rehman, S. Rahman (2011)
Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS)Applied Energy, 88
-
B. Subudhi, Pedda Ogeti (2016)
Non-linear autoregressive moving average with exogenous input model-based adaptive control of a wind energy conversion systemThe Journal of Engineering, 2016
-
C. Jauch, T. Cronin, P. Sørensen, B. Jensen (2007)
A fuzzy logic pitch angle controller for power system stabilizationWind Energy, 10
-
Halis Altun, T. Yalcinoz (2008)
Implementing soft computing techniques to solve economic dispatch problem in power systemsExpert Syst. Appl., 35
-
(2005)
Neural network based sensorless maximum wind energy captured with compensated power coef -
A. Eltamaly, Hassan Farh (2013)
Maximum power extraction from wind energy system based on fuzzy logic controlElectric Power Systems Research, 97
-
(2008)
Optimal control of wind energy systems (1st edn) -
B. Boukhezzar, L. Lupu, H. Siguerdidjane, M. Hand (2007)
Multivariable control strategy for variable speed, variable pitch wind turbinesRenewable Energy, 32
-
T. Takagi, M. Sugeno (1985)
Fuzzy identification of systems and its applications to modeling and controlIEEE Transactions on Systems, Man, and Cybernetics, SMC-15
-
(2007)
Wind Turbine Control Systems: Principles, Modeling and Gain Scheduling -
A. Yilmaz, Z. Özer (2009)
Pitch angle control in wind turbines above the rated wind speed by multi-layer perceptron and radial basis function neural networksExpert Syst. Appl., 36
-
Muhammad Mughal, Li Guojie (2015)
Review of Pitch Control for Variable Speed Wind Turbine2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom)
-
M. Jazaeri, H. Chitsaz (2011)
A New Efficient Scheme for Frequency Control in an Isolated Power System with a Wind GeneratorElectric Power Components and Systems, 40
-
J. Jang (1993)
ANFIS: adaptive-network-based fuzzy inference systemIEEE Trans. Syst. Man Cybern., 23
-
F. Bianchi, H. Battista, R. Mantz (2006)
Wind turbine control systems -
A. Hansen, P. Sørensen, F. Blaabjerg, John Becho (2002)
Dynamic Modelling of Wind Farm Grid InteractionWind Engineering, 26
-
M. Lotfy, T. Senjyu, M. Farahat, A. Abdel-Gawad, A. Yona (2017)
Enhancement of a Small Power System Performance Using Multi-Objective OptimizationIEEE Access, 5
-
Marouane Azzaoui, H. Mahmoudi (2016)
Fuzzy-PI control of a doubly fed induction generator-based wind power systemInt. J. Autom. Control., 11
-
Hui Li, K. Shi, P. McLaren (2004)
Neural-network-based sensorless maximum wind energy capture with compensated power coefficientIEEE Transactions on Industry Applications, 41
-
E. Muljadi, C. Butterfield (1999)
Pitch-controlled variable-speed wind turbine generationConference Record of the 1999 IEEE Industry Applications Conference. Thirty-Forth IAS Annual Meeting (Cat. No.99CH36370), 1
-
H. Kargar, A. Nateghi, Aram Khodamoradi (2012)
A New Approach for Wind Speed Behavior PredictionInternational Journal of Renewable Energy Research, 1
-
J. Laks, L. Pao (2009)
Optimal Control of Wind Energy Systems: Towards a Global Approach (Munteanu, I. et al.; 2008) [Bookshelf]IEEE Control Systems, 29
-
(2002)
Dynamic modeling of wind farm grid connection -
A. Meharrar, M. Tioursi, Mustapha Hatti, A. Stambouli (2011)
A variable speed wind generator maximum power tracking based on adaptative neuro-fuzzy inference systemExpert Syst. Appl., 38
-
Zhanshan Wang, Z. Shen, Chao Cai, Kaili Jia (2014)
Adaptive control of wind turbine generator system based on RBF-PID neural network2014 International Joint Conference on Neural Networks (IJCNN)
- Publisher
- SAGE
- Copyright
- © The Author(s) 2019
- ISSN
- 0309-524X
- eISSN
- 2048-402X
- DOI
- 10.1177/0309524X19849825
- Publisher site
- See Article on Publisher Site
Abstract
This article presents a multiple adaptive neuro-fuzzy inference system-based control scheme for operation of the wind energy conversion system above the rated wind speed. By controlling the pitch angle and generator torque concurrently, the generator power and speed fluctuation can be reduced and also turbine blade stress can be minimized. The proposed neuro-fuzzy-based adaptive controller is composed of both the Takagi–Sugeno fuzzy inference system and neural network. First, a step change in wind speed and then a simulated wind speed are considered in the proposed adaptive control design. A MATLAB/Simulink model of the wind turbine system is prepared, and simulations are carried out by applying the proportional integral, fuzzy-proportional integral and the proposed adaptive controller. From the obtained results, the effectiveness of the proposed adaptive controller approach is confirmed.
Journal
Wind Engineering – SAGE
Published: Apr 1, 2020