Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Adaptive Neuro-fuzzy Algorithm for Pitch Control of Variable-speed Wind Turbine

  • Regular Papers
  • Intelligent Control and Applications
  • Published:
International Journal of Control, Automation and Systems Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

With increasing size of wind turbines (WTs), the power regulation and fatigue loads on WT structures emerge as major problems to wind power industry. Pitch angle is scheduled above the rated wind speed to keep the power captured by variable-speed wind turbine (VSWT) around its rated value and release the fatigue load on WT structure. In this paper, a hybrid intelligent learning based adaptive neuro-fuzzy algorithm is proposed to schedule the pitch angle of 2 Megawatt (MW) VSWT. The artificial neural network (ANN) trains the parameters of fuzzy membership functions (MFs) using least squares estimator method in forward pass and back propagation gradient descent method in backward pass. The simulation is done in MATLAB and results are compared with multilayer perceptron feed-forward neural network (MLPFFNN) and fuzzy logic-based pitch controllers. The results indicate the effectiveness of proposed neuro-fuzzy algorithm which outperforms the other two methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. S. Xiao, H. Geng, and G. Yang, “Non-linear pitch control of wind turbines for tower load reduction,” IET Renewable Power Generation, vol. 8, no.7, pp. 786–794, 2014.

    Article  Google Scholar 

  2. X. Yin, Y. Lin, W. Li, H. Liu, and Y. Gu, “Adaptive sliding mode back-stepping pitch angle control of a variable-displacement pump controlled pitch system for wind turbines,” ISA Transactions, vol. 58, pp. 629–634, 2015.

    Article  Google Scholar 

  3. X. Yin, Y. Lin, W. Li, and Y. Gu, “Integrated pitch control for wind turbine based on a novel pitch control system,” Journal of Renewable and Sustainable Energy, vol. 6, p. 043106, 2014.

    Article  Google Scholar 

  4. A. D. Wright and L. J. Fingersh, “Advanced control design for wind turbines,” National Renewable Energy Lab, pp. 19–27, 2008.

  5. P. F. Odgaard, J. Stoustrup, and M. Kinnaert, “Fault-tolerant control of wind turbines: A benchmark model,” IEEE Transactions on Control Systems Technology, vol. 21, no. 4, pp. 1168–1182, 2013.

    Article  Google Scholar 

  6. K. Stol and M. Balas, “Periodic disturbance accommodating control for blade load mitigation in wind turbines,” Journal of Solar Energy Engineering, vol. 125, no. 4, pp. 379–385, 2003.

    Article  Google Scholar 

  7. R. Saravanakumar and D. Jena, “Validation of an integral sliding mode control for optimal control of a three blade variable speed variable pitch wind turbine,” Electrical Power and Energy Systems, vol. 69, pp. 421–429, 2015.

    Article  Google Scholar 

  8. M. Mahmood, S. Mohsen, and K. P. Niels, “An MPC approach to individual pitch control of wind turbines using uncertain LIDAR measurements,” Proc. of European Control Conference, Zurich, Switzerland, pp. 490–495, 2013.

  9. D. Li, Y. Song, W. Cai, P. Li, and H. R. Karimi, “Wind turbine pitch control and load mitigation using an L1 adaptive approach,” Mathematical Problems in Engineering, vol. 2014, p. 719803, 2014.

    Google Scholar 

  10. S. T. Kandukuri, A. Klausen, H. R. Karimi, and K. G. Robbersmyr, “A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm level health management,” Renewable and Sustainable Energy Reviews, vol. 53, pp. 697–708, 2016.

    Article  Google Scholar 

  11. Y. Si, H. R. Karimi, and H. Gao, “Modelling and parameter analysis of the OC3-hywind floating wind turbine with a tunned mass damper in nacelle,” Journal of Applied Mathematics, vol. 2013, p. 679071, 2013.

    Article  Google Scholar 

  12. Y. Si, H. R. Karimi, and H. Gao, “Modelling and optimization of a passive structural control design for a spar-type floating wind turbine,” Engineering Structures, vol. 69, pp. 168–182, 2014.

    Article  Google Scholar 

  13. T. Bakka and H. R. Karimi, “H∞ static output-feedback control design with constrained information for offshore wind turbine system,” Journal of the Franklin Institute, vol. 350, no. 8, pp. 2244–2260, 2013.

    Article  MathSciNet  MATH  Google Scholar 

  14. T. Bakka, H. R. Karimi, and S. Christiansen, “Linear parameter-varying modelling and control of an offshore wind turbine with constrained information,” IET Control Theory & Applications, vol. 8, no. 1, pp. 22–29, 2014.

    Article  MathSciNet  MATH  Google Scholar 

  15. L. Aguilar, P. Melin, and O. Castillo, “Intelligent control of a stepping motor drive using a hybrid neuro-fuzzy ANFIS approach,” Applied Soft Computing, vol. 3, pp. 209–219, 2003.

    Article  Google Scholar 

  16. P. Melin and O. Castillo, “A new method for adaptive control of non-linear plants using type-2 fuzzy logic and neural networks,” International Journal of General Systems, vol. 33, no. 2–3, pp. 289–304, 2004.

    Article  MATH  Google Scholar 

  17. O. Atan, F. Kutlu, and O. Castillo, “Intuitionistic fuzzy sliding controller for uncertain hyperchaotic synchronization,” International Journal of Fuzzy Systems, vol. 22, no. 5, pp. 1430–1443, 2020.

    Article  Google Scholar 

  18. C. Felizardo, C. Oscar, and C.-A. Prometeo, “Design of a control strategy based on type-2 fuzzy logic for omnidirectional mobile robots,” Journal of Multiple-Valued Logic & Soft Computing, vol. 37, no. 1–2, pp. 107–136, 2021.

    MATH  Google Scholar 

  19. E. Ontiveros-Robles, P. Melin, and O. Castillo, “Comparative analysis of noise robustness of type 2 fuzzy logic controllers,” Kybernetika, vol. 54, no. 1, pp. 175–201, 2018.

    MathSciNet  MATH  Google Scholar 

  20. Z. Zhang and J. Dong, “A novel H∞ control for T-S fuzzy systems with membership functions online optimization learning,” IEEE Transactions on Fuzzy Systems, vol. 30, no. 4, pp. 1129–1138, 2022.

    Article  Google Scholar 

  21. Z. Zhang and J. Dong, “Fault-tolerant containment control for IT2 fuzzy networked multiagent systems against denial-of-service attacks and actuator faults,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 4, pp. 2213–2224, 2022.

    Article  Google Scholar 

  22. X. Xin, Y. Tu, V. Stojanovic, H. Wang, K. Shi, S. He, and T. Pan, “Online reinforcement learning multiplayer nonzero sum games of continuous-time Markov jump linear systems,” Applied Mathematics and Computation, vol. 412, p. 126537, 2022.

    Article  MATH  Google Scholar 

  23. X. Zhang, H. Wang, V. Stojanovic, P. Cheng, S. He, X. Luan, and F. Liu, “Asynchronous fault detection for interval type-2 fuzzy nonhomogeneous higher-level Markov jump systems with uncertain transition probabilities,” IEEE Transactions on Fuzzy Systems, vol. 30, no. 7, pp. 2487–2499, 2022.

    Article  Google Scholar 

  24. P. Cheng, H. Wang, V. Stojanovic, S. He, K. Shi, X. Luan, F. Liu, and C. Sun, “Asynchronous fault detection observer for 2-D Markov jump systems,” IEEE Transactions on Cybernetics, pp. 1–12, 2021. DOI: https://doi.org/10.1109/TCYB.2021.3112699

  25. H. Fang, G. Zhu, V. Stojanovic, R. Nie, S. He, X. Luan, and Fei Liu, “Adaptive optimization algorithm for nonlinear Markov jump systems with partial unknown dynamics,” International Journal of Robust and Nonlinear Control, vol. 31, pp. 2126–2140, 2021.

    Article  MathSciNet  Google Scholar 

  26. H. Jafarnejadsani and J. Pieper, “Gain-scheduled L1-optimal control of variable-speed-variable-pitch wind turbines,” IEEE Transactions on Control Systems Technology, vol. 23, no. 1, pp. 372–379, 2015.

    Article  Google Scholar 

  27. H. Jafarnejadsani, J. Pieper, and J. Ehlers, “Adaptive control of a variable-speed variable-pitch wind turbine using radial-basis function neural network,” IEEE Transactions on Control Systems Technology, vol. 21, no.6, pp. 2264–2272, 2013.

    Article  Google Scholar 

  28. T. L. Van, T. H. Nguyen, and D. Lee, “Advanced pitch angle control based on fuzzy logic for variable-speed wind turbine systems,” IEEE Transactions on Energy Conversion, vol. 30, no. 2, pp. 578–587, 2015.

    Article  Google Scholar 

  29. B. Han, L. Zhou, F. Yang, and Z. Xiang, “Individual pitch controller based on fuzzy logic control for wind turbine load mitigation,” IET Renewable Power Generation, vol. 10, no. 5, pp. 687–693, 2016.

    Article  Google Scholar 

  30. A. Rezvani, M. Izadbakhsh, and M. Gandomkar, “Enhancement of microgrid dynamic responses under fault conditions using artificial neural network for fast changes of photovoltaic radiation and FLC for wind turbine,” Energy Systems, vol. 6, no.4, pp. 551–584, 2015.

    Article  Google Scholar 

  31. A. Rezvani, M. Izadbakhsh, and M. Gandomkar, “Microgrid dynamic responses enhancement using artificial neural network-genetic algorithm for photovoltaic system and fuzzy controller for high wind speeds,” International Journal of Numerical Modeling: Electronic Networks, Devices and Fields, vol. 29, no. 2, pp. 309–332, 2016.

    Article  Google Scholar 

  32. A. Rezvani, A. Esmaeily, H. Etaati, and M. Mohammadinodoushan, “Intelligent hybrid power generation system using new hybrid fuzzy-neural for photovoltaic system and RBFNSM for wind turbine in the grid connected mode,” Frontiers in Energy, vol. 13, no. 1, pp. 131–148, 2019.

    Article  Google Scholar 

  33. D. Wu, G. S. Nariman, S. Q. Mohammed, Z. Shao, A. Rezvani, and S. Mohajeryami, “Modeling and simulation of novel dynamic control strategy for PV-wind hybrid power system using FGS-PID and RBFNSM methods,” Soft Computing, vol. 24, pp. 8403–8425, 2020.

    Article  Google Scholar 

  34. S. Abdelmalek, A. T. Azar, and D. Dib, “A novel actuator fault-tolerant control strategy of DFIG-based wind turbines using Takagi-Sugeno multiple models,” International Journal of Control, Automation, and Systems, vol. 16, no. 3, pp. 1415–1424, 2018.

    Article  Google Scholar 

  35. X. Zhang and K. Jin, “Proportional plus derivative state feedback control of Takagi-Sugeno fuzzy singular fractional order systems,” International Journal of Control, Automation, and Systems, vol. 19, pp. 3823–3829, 2021.

    Article  Google Scholar 

  36. D. Song, J. Yang, M. Dong, and Y. H. Joo, “Kalman filter-based wind speed estimation for wind turbine control,” International Journal of Control, Automation, and Systems, vol. 15, no. 3, pp. 1089–1096, 2017.

    Article  Google Scholar 

  37. Y. Sun, S. Yan, B. Cai, Y. Wu, and Z. Zhang, “MPPT adaptive controller of DC-based DFIG in resistances uncertainty,” International Journal of Control, Automation, and Systems, vol. 19, no. 8, pp. 2734–2746, 2021.

    Article  Google Scholar 

  38. D. Yang, Y. C. Kang, J.-W. Park, Y. I. Lee, and S.-H. Song, “Power smoothing of a variable-speed wind turbine generator,” International Journal of Control, Automation, and Systems, vol. 19, no. 1, pp. 11–19, 2021.

    Article  Google Scholar 

  39. A. B. Asghar and X. Liu, “Estimation of wind turbine power coefficient by adaptive neuro-fuzzy methodology,” Neurocomputing, vol. 238, pp. 227–233, 2017.

    Article  Google Scholar 

  40. A. B. Asghar and X. Liu, “Estimation of wind speed probability distribution and wind energy potential using adaptive neuro-fuzzy methodology,” Neurocomputing, vol. 287, pp. 58–67, 2018.

    Article  Google Scholar 

  41. A. B. Asghar and X. Liu, “Online estimation of wind turbine tip speed ratio by adaptive neuro-fuzzy algorithm,” International Journal of Advanced Computer Science and Applications, vol. 9, no. 3, pp. 28–33, 2018.

    Google Scholar 

  42. N. T. Truong, T.-I. Seo, and S. D. Nguyen, “Bearing fault online identification based on ANFIS,” International Journal of Control, Automation, and Systems, vol. 19, no. 4, pp. 1703–1714, 2021.

    Article  Google Scholar 

  43. F. Kamil, T. S. Hong, W. Khaksar, N. Zulkifli, and S. A. Ahmad, “An ANFIS-based optimized fuzzy-multilayer decision approach for a mobile robotic system in ever-changing environment,” International Journal of Control, Automation, and Systems, vol. 17, no. 1, pp. 253–266, 2019.

    Article  Google Scholar 

  44. J. F. Manwell, J. G. McGowan, and A. L. Rogers, Wind Energy Explained-Theory, Design and Application, John Wiley & Sons Ltd, 2002.

  45. A. B. Asghar and X. Liu, “Adaptive neuro-fuzzy algorithm to estimate effective wind speed and optimal rotor speed for variable-speed wind turbine,” Neurocomputing, vol. 272, pp. 495–504. 2018.

    Article  Google Scholar 

  46. T. Al-Shemmeri, Wind Turbines, Ventus Publishing Aps, 2010.

  47. M. H. Hansen, A. D. Hansen, T. J. Larsen, S. Øye, P. Sørensen, and P. Fuglsang, “Control design for a pitch-regulated, variable speed wind turbine,” Denmark. Forsknings center Risoe. Risoe-R, no. 1500 (EN), 2005.

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan

    Aamer Bilal Asghar

  2. Khazina Naveed is with the Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan

    Khazina Naveed

  3. State Key Laboratory for Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Gang Xiong

  4. Cloud Computing Center, Chinese Academy of Sciences, Beijing, China

    Gang Xiong

  5. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China

    Yong Wang

Authors
  1. Aamer Bilal Asghar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Khazina Naveed
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gang Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yong Wang.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work was supported, in part, by the National Natural Science Foundation of China under Grants 61872365, 61773381, U1909218, U1909204, U19B2029 and Chinese Guangdong’s S&T project (2019B1515120030, 2020B0909050001).

Aamer Bilal Asghar received his Ph.D. degree in control theory and control engineering from the School of Control Science and Engineering, Dalian University of Technology, Dalian, China in 2018, an M.S. degree in electrical engineering from Government College University Lahore, Pakistan in 2014, and a B.S. degree in electronic engineering from The Islamia University of Bahawalpur, Pakistan in 2008. He has 13 years of experience in Industry and Academia. Currently, he is serving as an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) at COMSATS University Islamabad (CUI), Lahore campus, Pakistan. He is the HEC approved Ph.D. supervisor and the in-charge of Instrumentation and Measurement LAB at ECE dept. CUI, Lahore campus. He is the author and co-author of several SCI Impact Factor (IF) International Journal papers. His research interest areas include artificial intelligence, intelligent control, fuzzy logic control, artificial neural networks, ANFIS, genetic algorithm machine learning, and renewable energy technologies.

Khazina Naveed is doing an M.S. degree in computer science from COMSATS University Islamabad, Lahore campus, Pakistan. She received her B.S. degree in bioinformatics from the Department of Biosciences, COMSATS University Islamabad, Sahiwal, Pakistan, in 2019. Her research interest areas include artificial intelligence, machine learning, data mining, computer programming, and bioinformatics.

Gang Xiong received his B.Eng. and M.Eng. degrees from the Department of Precision Instrument, Xi’an University of Science and Technology, Xi’an, China, in 1991 and 1994, respectively, and a Ph.D. degree in control science and engineering from Shanghai Jiao Tong University, Shanghai, China, in 1996. From 1996 to 1998, he was a Postdoctoral and Associate Scientist with the Institute of Industrial Process Control, Zhejiang University. He is the Deputy Director of Beijing Engineering Research Center for Intelligent Systems and Technology, and Deputy Director of Cloud Computing Center, Chinese Academy of Sciences (CAS). His research interests include parallel control and management, modeling and optimization of complex systems, cloud computing and big data, intelligent manufacturing, and intelligent transportation systems.

Yong Wang received his B.S. degree from the Department of Mathematics, Shandong University, Jinan, China, in 1998, an M.Eng. degree from the Department of Computer Science and Engineering, China University of Petroleum, Beijing, China, in 2004, and a Ph.D. degree in pattern recognition & intelligent systems from the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, in 2008. From 2008 to 2010, he was a Post doctor in the College of Artificial Intelligence, University of Chinese Academy of Sciences. In 2010, he started his present position as a Research Scientist in the College of Artificial Intelligence, University of Chinese Academy of Sciences. He is the author and co-author of over 30 refereed journal and conference proceeding papers. His research interests include pattern recognition and data mining, and modeling and optimization of complex systems.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Asghar, A.B., Naveed, K., Xiong, G. et al. Adaptive Neuro-fuzzy Algorithm for Pitch Control of Variable-speed Wind Turbine. Int. J. Control Autom. Syst. 20, 3788–3798 (2022). https://doi.org/10.1007/s12555-021-0675-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-021-0675-y

Keywords

Search

Navigation