ANFIS modeling and validation of a variable speed wind turbine based on actual data

Document Type: Research Paper


1 School of Mechanical and Energy Engineering, Shahid Beheshti University, Tehran, Iran

2 School of Mechanical Engineering, University of Tehran, Tehran, Iran


In this research paper, ANFIS modeling and validation of Vestas 660 kW wind turbine based on actual data obtained from Eoun-Ebn-Ali wind farm in Tabriz, Iran, and FAST is performed. The turbine modeling is performed by deriving the non-linear dynamic equations of different subsystems. Then, the model parameters are identified to match the actual response. ANFIS is an artificial intelligent technique which creates a fuzzy inference system based on input and output information of the model. In this research, the ANFIS algorithm combines neural network and fuzzy logic with 5 layers which utilize different node functions for learning and setting fuzzy inference system parameters. After learning, by assuming constant parameters, a hybrid method is used to update the results. Employing the proposed method, computation time and complexity are remarkably reduced. Results of the proposed method are then compared and validated with the actual data of Eoun-Ebn-Ali wind farm in Tabriz. It is shown and concluded that the proposed model matches favorably well with the actual data and FAST model.


[1] Gustavo M, Enrique M(2011) Modeling and Control design of pitch-controlled variable speed wind turbines. Wind turbines4: 373-402.

[2] Muljadi E, Pierce K,Migliore P (1998)Control Strategy for Variable-Speed, Stall-Regulated Wind Turbines. American Controls Conference. doi: NREL/CP-500-24311

[3] Thomas E, Christoffer S (2009) Fault Diagnosis and Fault-Tolerant Control of Wind Turbine. Aalborg: Denmark. 151-157.

[4] Wright AD, Fingersh LJ (2008) Advanced Control Design for Wind Turbines Part I: Control Design, Implementation, and Initial Tests. NREL, Colorado: 27-37.

[5] Vlastimir DN, Gradimir SI, Predrag MZ, Zarko MC, Ivan TC (2012) Hybrid Soft Computing Control Strategies for Improving the Energy Capture of a Wind Farm. Thermal Science 16: 483-491. doi: 10.2298/TSCI120503185Z

[6] Aamer BA and Xiaodong L (2018) Adaptive neuro-fuzzy algorithm to estimate effective wind speed and optimal rotor speed for variable-speed wind turbine. Neurocomputing 272: 495-504.

[7] Mohit S (2008) Dynamic Models for Wind Turbines and Wind Power Plants. Subcontract Report, University of Texas at Austin.

[8] Watsamon S (2014) Development of a Model for an Offshore Wind Turbine Supported by a Moored Semi-Submersible Platform. MSc Thesis, University of Texas at Austin.

[9] Manyonge AW, Ochieng RM, Onyango FN, Shichikha JM (2012) Mathematical Modelling of Wind Turbine in a Wind Energy Conversion System: Power Coefficient Analysis, Applied Mathematical Sciences 6: 4527 – 4536.

[10] Alejandro R, Alvaro L, Gerardo V, Daniel A, Gustavo A (2009) Modeling of a Variable Speed Wind Turbine with a Permanent Magnet Synchronous Generator, IEEE International Symposium on Industrial Electronics, Seoul, Korea.

[11] Adria JF, Oriol GB, Andreas S, Marc S (2010) Montserrat M. Modeling and control of the doubly fed induction generator wind turbine, Simulation Modelling Practice and Theory 18: 1365-1381. doi:10.1016/j.simpat.2010.05.018.

[12] Sanchez R., Medina A (2014) Wind turbine model simulation: A bond graph approach, Simulation Modelling Practice and Theory 41: 28-45. doi:10.1016/j.simpat.2013.11.001.

[13] Yousif Al (2012) Design and Simulation of Anfis Controller for Virtual-Reality-Built Manipulator. Recent Advances in Theory and Applications, Intech.

[14] Aware MV, Kqthari AG, CHoube SO (2000) Application of Adaptive Neuro Fuzzy Controller (ANFIS) for Voltage Source Inverter Fed Induction Motor Drive. Power Electronics and Motion Control Conference 2: 935-939. doi: 10.1109/IPEMC.2000.884638

[15] Choon YL, Lee J. Multiple (2005) Neuro-Adaptive Control of Robot Manipulators Using Visual Cues. IEEE Transactions on Industrial Electronics 52; 320-326. doi: 10.1109/TIE.2004.841080.

[16] Hui C, Gangquan S, Yanbin Z, Xikui M (2007) A Hybrid Controller of Self-Optimizing Algorithm and ANFIS for Ball Mill Pulverizing System. Proceedings of the IEEE International Conference on Mechatronics and Automation: 3289 – 3294. doi: 10.1109/ICMA.2007.4304089.

[17] Swasti RK, Sidhartha P (2010) ANFIS Approach for TCSC-based Controller Design System Stability Improvement Design for Power. IEEE; 149-154. doi: 10.1109/ICCCCT.2010.5670543.

[18] A. Kusagur, Sh. F. Kodad, S. Ram (2012) Modelling & Simulation of an ANFIS Controller for an AC Drive. World Journal of Modelling and Simulation 8: 36-49.  

[19] Simon SH (2009) Neural Networks and Learning Machines, Pearson Prentice Hall.

[20] Pao L, Johnson E (2009) A Tutorial on the Dynamics and Control of Wind Turbines and Wind Farms. American Control Conference, Hyatt Regency Riverfront: 2076-2089. doi: 10.1109/ACC.2009.5160195.

[21] Jiang H, Li Y (2015)  Cheng Zh. Performances of ideal wind turbine. Renewable Energy 83: 658-662. doi:10.1016/j.renene.2015.05.013.

[22] Pintea A (2011) Christov N, Borne P, Popescu D, Badea A. Optimal control of variable speed wind turbines, Control and Automation: 838-843. doi: 10.1109/MED.2011.5983056.

[23] Rasel SR, Hasnat MD (2011)Modeling of a wind turbine generator using wind speed as a controlled variable, PhD Thesis.

[24] Bianchi FD, Battista HD (2006) Mantz RJ. Wind Turbine Control Systems Principles, Modelling and Gain Scheduling Design .Springer, La Plata, 20.

[25] Jelavic M, Peric N, Petrovic I, Car S, MańĎercic M (2007) Design of a Wind Turbine Pitch Controller for Loads and Fatigue Reduction. European Wind Energy Association.

[26] Burton T, Sharpe D, Jenkins N, Bossanyi E (2001) Wind Energy Handbook. Wile,: West Sussex, 176-177.

[27] Thomas E (2009) Christoffer S. Fault Diagnosis and Fault-Tolerant Control of Wind Turbine. Aalborg, Denmark, 151-157.

[28] Wright AD, Fingersh LJ (2008) Advanced Control Design for Wind Turbines Part I: Control Design, Implementation, and Initial Tests. NREL: Colorado, 27-37.

[29] Guo R, Du J, Wu J, Liu Y (2013) The Pitch Control Algorithm of Wind Turbine Based on Fuzzy Control and PID Control. Energy and Power Engineering 5: 6-10.

[30] Navarro RI (2013) 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