Modeling and Neuro-fuzzy Controller Design of a Wind Turbine in Full-load Region Based on Operational Data

Document Type : Research Article

Authors

1 PhD Student in Shahid Beheshti University

2 Department of Mechanical and Energy engineering, Shahid Beheshti University

3 Department of Mechanical engineering, University of Tehran

Abstract

In this paper, dynamic modeling of a Vestas 660 kW wind turbine and its validation are performed based on operational data extracted from Eoun-Ebn-Ali wind farm in Tabriz, Iran. The operational data show that the turbine under study, with a classical PI controller, encounters high fluctuations when controlling the output power at its rated value. The turbine modeling is performed by deriving the non-linear dynamic equations of different subsystems. Then, the model parameters are identified such that the model response matches the actual response. In order to validate the proposed model, inputs to the actual wind turbine (wind speed, pitch angle and generator torque) are fed to the model in MATLAB as well as FAST tool, and the output powers are compared. In order to improve the control performance and alleviate fluctuations in the full-load region, considering the nonlinear and complex behavior of the system, a neuro-fuzzy controller is designed and simulated to control the pitch angle. In this controller, neural network is used to adjust the membership functions of the fuzzy controller. Simulation results of the designed neuro-fuzzy controller indicate the improved performance of the closed-loop system compared to the actual and simulated results from the classical PI controller.

Keywords

dor 20.1001.1.25882953.2019.51.2.7.8

Main Subjects

References

1] Hoda M, Danyal B. Wind turbine control using T-S systems with nonlinear consequent parts. Energy 2019; 172: 922-931. DOI: 10.1016/j.energy.2019.01.133
[2] Vlastimir DN, Gradimir SI, Predrag MZ, Zarko MC. Ivan TC, Hybrid Soft Computing Control Strategies for Improving the Energy Capture of a Wind Farm. Thermal Science 2012; 16: 483-491.
      DOI: 10.2298/TSCI120503185Z
[3] Kerim K, Numan C. Artificial Neural Networks for Controlling Wind–PV Power Systems. Renewable and Sustainable Energy Reviews2014; 29: 804–827.
      DOI:10.1016/j.rser.2013.08.070
[4] Yaakoubi A.E, Amhaimar L, Attari K, Harrak M.H, Halaoui M.E, Asselman A. Non-linear and intelligent maximum power point tracking strategies for small size wind turbines: Performance analysis and comparison. Energy Reports 2019; 5: 545-554.
[5] Civelek Z.  Optimization of fuzzy logic (Takagi-Sugeno) blade pitch angle controller in wind turbines by genetic algorithm. Engineering Science and Technology, an International Journal 2019.
     DOI: 10.1016/j.jestch.2019.04.010.
[6] Romański L, Bieniek J, Komarnicki P, Dębowski Marcin, Detyna J. Estimation of operational parameters of the counter-rotating wind turbine with artificial neural networks. Archives of Civil and Mechanical Engineering 2017: 17: 1019-1028
[7] Bououden S, Chadli M, Filali S, A. Hajjaji El. Fuzzy model based multivariable predictive control of a variable speed wind turbine: LMI approach, Renewable Energy 2012; 37: 434-439.
[8] Kabira E.M, Abdellah E.H, Abdellatif K. Analysis of a RBF Neural Network Based Controller for Pitch Angle of Variable-Speed Wind Turbines, Procedia Engineering 2017; 181: 552-559.
[9] Abdolhamed H, Reza K. Wind Turbine Control Using PI Pitch Angle Controller, Brescia: Italy, 2012; 241-246.
[10] Mansour Sh, Reza Sh, Ali NY. An optimal fuzzy PI controller to capture the maximum power for variable speed wind turbines. Neural Computing and Applications 2013; 23: 1359-1368.
      DOI: 10.1007/s00521
[11] Rudion K, Orths A, Styczynski Z. Modelling of variable speed wind turbines with pitch control. Securing   Critical Infrastructures, Grenoble, 2004.
[12] Irving PG, Jaspreet SD. Pitch controller for wind turbine load mitigation through consideration of yaw misalignment. Mechatronics 2015; 32:  44-58.
      DOI:10.1016/j.mechatronics.2015.10.003
[13] Yaxing R, Liuying L, Joseph B, Lin J. Nonlinear PI control for variable pitch wind turbine. Control  Engineering Practice 2016; 50: 84-94.
      DOI:10.1016/j.conengprac.2016.02.004
[14] Rui G, Jinsong D, Jinghui W, Yiyang L. The Pitch Control Algorithm of Wind Turbine Based on Fuzzy Control and PID Control. Energy and Power Engineering 2013; 3: 6-10.
      DOI:10.4236/epe.2013.53B002
[15] Ahmed L, Abdel LE. Wind-turbine Collective Pitch Control via a Fuzzy Predictive Algorithm. Renewable Energy 2016; 87: 298-306.
      DOI:10.1016/j.renene.2015.10.030
[16] Hamed H, Aghil YK. Wind Turbine Integrated Control during Full Load Operation. The 10th international Energy Conference (IEC), 2014.
[17] Sachin G, Mukul G, Sulata B. Power Regulation of a Wind Turbine Using Adaptive Fuzzy- PID Pitch Angle Controller. International Journal of Recent Technology and Engineering 2013; 2: 2277-3878.
[18] Iman P, Reza Sh, Mansour Sh, RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm. ISA Transactions 2012; 51: 641-648.
     DOI:10.1016/j.isatra.2012.06.001
[19] Yishuang Q, Qingjin M, The Application of Fuzzy PID Control in Pitch Wind Turbine.  Energy Procedia 2012;  16:1635-1641.
      DOI:10.1016/j.egypro.2012.01.254
[20] Yousif Al, Design and Simulation of Anfis Controller for Virtual-Reality-Built Manipulator. Recent Advances in Theory and Applications, Intech, 2012.
[21] Assareh E, Biglari M. A novel approach to capture the maximum power from variable speed wind turbines using PI controller, RBF neural network and GSA evolutionary algorithm. Renewable and Sustainable Energy Reviews 2015; 51: 1023–1037.
[22] Fernando J.L, Godpromesse K, Francoise L.L, A novel online training neural network-based algorithm for wind speed estimation and adaptive control of PMSG wind turbine system for maximum power extraction, Renewable Energy2016; 86: 38-48.
 [23] Ahmed M, Abderrezak G, Hocine B, Adel M. New neural network and fuzzy logic controllers to monitor maximum power for wind energy conversion system, Energy 2016; 137-146.
[24] Simon SH. Neural Networks and Learning Machines, Pearson Prentice Hall, 2009.
[25] Jiang H, Li Y,  Cheng Zh. Performances of ideal wind turbine. Renewable Energy 2015; 83: 658-662.
      DOI:10.1016/j.renene.2015.05.013
[26] Pintea A, Christov N, Borne P, Popescu D, Badea A. Optimal control of variable speed wind turbines, Control and Automation 2011; 838-843.
      DOI: 10.1109/MED.2011.5983056
[27]FazlollahiV, Taghizadeh M, Shirazi F.A.  ANFIS Modeling and Validation of a Variable Speed Wind Turbine Based on Actual Data, Energy Equipment and Systems 2019.
         DOI:  10.22059/ees.2019.96991.1196
[28] Bianchi FD, Battista HD, Mantz RJ. Wind Turbine Control Systems Principles, Modelling and Gain Scheduling Design .Springer: La Plata, 2006; 20.
[29] Aamer B.A, Xiaodong L. Estimation of Wind Turbine Power Coefficient by Adaptive Neuro-fuzzy Methodology. Neurocomputing 2017; 238: 227-233.
[30] Burton T, Sharpe D, Jenkins N, Bossanyi E. Wind Energy Handbook. Wiley: West Sussex, 2001; 176-177.
[31] Sandhya T, Chandan SK. Control and operation of Opti-slip induction generator in wind farms. Computer, Communication and Electrical Technology 2011; 450-454.
      DOI: 10.1109/ICCCET.2011.5762517
[32] Tiwari AR, Shewale AJ, Gagangras AR, Lokhande NM. Comparison of various Wind Turbine Generators. Multidiscplinary Journal of Research in Engineering and Technalogy 2014; 1: 129-135.
[33] Guo R, Du J, Wu J, Liu Y. The Pitch Control Algorithm of Wind Turbine Based on Fuzzy Control and PID Control. Energy and Power Engineering 2013; 5: 6-10.
[34] Navarro RI. Study of a Neural Network-based System for Stability Augmentation of an Airplane, Annex1, Introduction to Neural Networks and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Technical Report, Catalunya, 2013.
[35] Golnary F, Moradi H. Dynamic modelling and design of various robust sliding mode controls for the wind turbine with estimation of wind speed, Applied Mathematical Modelling 2019; 65, 566-585.
[36] Aamer B.A, Xiaodong L. Adaptive neuro-fuzzy algorithm to estimate effective wind speed and optimal rotor speed for variable-speed wind turbine, Neurocomputing 2018; 272: 495-504.
[37] Majid M, Mojtaba K, Rupp C, DavidTing, Mehrdad S. Application of imputation techniques and Adaptive Neuro-Fuzzy Inference System to predict wind turbine power production, Energy; 2017; 138: 394-404.
[38] Srisaeng P, Baxter G.S, Wild G. An adaptive neuro-fuzzy inference system for forecasting Australia’s
domestic low cost carrier passenger demand. Aviation 2015; 19: 150–163.
 
AUT Journal of Modeling and Simulation
Volume 51, Issue 2
Summer and Autumn 2019
Pages 139-152

Files

Share

How to cite

Statistics