IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i24p4712-d296342.html
   My bibliography  Save this article

Fault-Tolerant Neuro Adaptive Constrained Control of Wind Turbines for Power Regulation with Uncertain Wind Speed Variation

Author

Listed:
  • Hamed Habibi

    (Faculty of Science and Engineering, School of Civil and Mechanical Engineering, Curtin University, Perth 6102, Australia)

  • Hamed Rahimi Nohooji

    (Center for Research in Mechatronics, Institute of Mechanics, Materials, and Civil Engineering, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium)

  • Ian Howard

    (Faculty of Science and Engineering, School of Civil and Mechanical Engineering, Curtin University, Perth 6102, Australia)

  • Silvio Simani

    (Department of Engineering, University of Ferrara, 44100 Ferrara, Italy)

Abstract

This paper presents a novel adaptive fault-tolerant neural-based control design for wind turbines with an unknown dynamic and unknown wind speed. By utilizing the barrier Lyapunov function in the analysis of the Lyapunov direct method, the constrained behavior of the system is provided in which the rotor speed, its variation, and generated power remain in the desired bounds. In addition, input saturation is also considered in terms of smooth pitch actuator bounding. Furthermore, by utilizing a Nussbaum-type function in designing the control algorithm, the unpredictable wind speed variation is captured without requiring accurate wind speed measurement, observation, or estimation. Moreover, with the proposed adaptive analytic algorithms, together with the use of radial basis function neural networks, a robust, adaptive, and fault-tolerant control scheme is developed without the need for precise information about the wind turbine model nor the pitch actuator faults. Additionally, the computational cost of the resultant control law is reduced by utilizing a dynamic surface control technique. The effectiveness of the developed design is verified using theoretical analysis tools and illustrated by numerical simulations on a high-fidelity wind turbine benchmark model with different fault scenarios. Comparison of the achieved results to the ones that can be obtained via an available industrial controller shows the advantages of the proposed scheme.

Suggested Citation

  • Hamed Habibi & Hamed Rahimi Nohooji & Ian Howard & Silvio Simani, 2019. "Fault-Tolerant Neuro Adaptive Constrained Control of Wind Turbines for Power Regulation with Uncertain Wind Speed Variation," Energies, MDPI, vol. 12(24), pages 1-33, December.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:24:p:4712-:d:296342
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/24/4712/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/24/4712/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jena, Debashisha & Rajendran, Saravanakumar, 2015. "A review of estimation of effective wind speed based control of wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 1046-1062.
    2. Jaramillo-Lopez, Fernando & Kenne, Godpromesse & Lamnabhi-Lagarrigue, Francoise, 2016. "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 Energy, Elsevier, vol. 86(C), pages 38-48.
    3. Guodong You & Tao Xu & Honglin Su & Xiaoxin Hou & Xue Wang & Chengxin Fang & Jisheng Li, 2019. "Fault-Tolerant Control of Doubly-Fed Wind Turbine Generation Systems under Sensor Fault Conditions," Energies, MDPI, vol. 12(17), pages 1-14, August.
    4. Song, Dongran & Yang, Jian & Cai, Zili & Dong, Mi & Su, Mei & Wang, Yinghua, 2017. "Wind estimation with a non-standard extended Kalman filter and its application on maximum power extraction for variable speed wind turbines," Applied Energy, Elsevier, vol. 190(C), pages 670-685.
    5. Habibi, Hamed & Howard, Ian & Simani, Silvio, 2019. "Reliability improvement of wind turbine power generation using model-based fault detection and fault tolerant control: A review," Renewable Energy, Elsevier, vol. 135(C), pages 877-896.
    6. Tiwari, Ramji & Babu, N. Ramesh, 2016. "Recent developments of control strategies for wind energy conversion system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 268-285.
    7. Urs Giger & Patrick Kühne & Horst Schulte, 2017. "Fault Tolerant and Optimal Control of Wind Turbines with Distributed High-Speed Generators," Energies, MDPI, vol. 10(2), pages 1-13, January.
    8. Seyed Mojtaba Tabatabaeipour & Peter F. Odgaard & Thomas Bak & Jakob Stoustrup, 2012. "Fault Detection of Wind Turbines with Uncertain Parameters: A Set-Membership Approach," Energies, MDPI, vol. 5(7), pages 1-25, July.
    9. Silvio Simani, 2015. "Overview of Modelling and Advanced Control Strategies for Wind Turbine Systems," Energies, MDPI, vol. 8(12), pages 1-24, November.
    10. Silvio Simani & Paolo Castaldi & Saverio Farsoni, 2017. "Data–Driven Fault Diagnosis of a Wind Farm Benchmark Model," Energies, MDPI, vol. 10(7), pages 1-26, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ralf Stetter, 2020. "Approaches for Modelling the Physical Behavior of Technical Systems on the Example of Wind Turbines," Energies, MDPI, vol. 13(8), pages 1-27, April.
    2. Shrabani Sahu & Sasmita Behera, 2022. "A review on modern control applications in wind energy conversion system," Energy & Environment, , vol. 33(2), pages 223-262, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Habibi, Hamed & Howard, Ian & Simani, Silvio, 2019. "Reliability improvement of wind turbine power generation using model-based fault detection and fault tolerant control: A review," Renewable Energy, Elsevier, vol. 135(C), pages 877-896.
    2. Shrabani Sahu & Sasmita Behera, 2022. "A review on modern control applications in wind energy conversion system," Energy & Environment, , vol. 33(2), pages 223-262, March.
    3. Papini, Guglielmo & Faedo, Nicolás & Mattiazzo, Giuliana, 2024. "Fault diagnosis and fault-tolerant control in wave energy: A perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    4. Adrian Gambier, 2021. "Pitch Control of Three Bladed Large Wind Energy Converters—A Review," Energies, MDPI, vol. 14(23), pages 1-24, December.
    5. Youssef, Abdel-Raheem & Mousa, Hossam H.H. & Mohamed, Essam E.M., 2020. "Development of self-adaptive P&O MPPT algorithm for wind generation systems with concentrated search area," Renewable Energy, Elsevier, vol. 154(C), pages 875-893.
    6. Karabacak, Murat, 2019. "A new perturb and observe based higher order sliding mode MPPT control of wind turbines eliminating the rotor inertial effect," Renewable Energy, Elsevier, vol. 133(C), pages 807-827.
    7. Song, Dongran & Yang, Jian & Su, Mei & Liu, Anfeng & Cai, Zili & Liu, Yao & Joo, Young Hoon, 2017. "A novel wind speed estimator-integrated pitch control method for wind turbines with global-power regulation," Energy, Elsevier, vol. 138(C), pages 816-830.
    8. Song, Dongran & Yang, Jian & Dong, Mi & Joo, Young Hoon, 2017. "Model predictive control with finite control set for variable-speed wind turbines," Energy, Elsevier, vol. 126(C), pages 564-572.
    9. Song, Dongran & Yang, Jian & Cai, Zili & Dong, Mi & Su, Mei & Wang, Yinghua, 2017. "Wind estimation with a non-standard extended Kalman filter and its application on maximum power extraction for variable speed wind turbines," Applied Energy, Elsevier, vol. 190(C), pages 670-685.
    10. Barambones, Oscar & Cortajarena, Jose A. & Calvo, Isidro & Gonzalez de Durana, Jose M. & Alkorta, Patxi & Karami-Mollaee, A., 2019. "Variable speed wind turbine control scheme using a robust wind torque estimation," Renewable Energy, Elsevier, vol. 133(C), pages 354-366.
    11. Afef Fekih & Hamed Habibi & Silvio Simani, 2022. "Fault Diagnosis and Fault Tolerant Control of Wind Turbines: An Overview," Energies, MDPI, vol. 15(19), pages 1-21, September.
    12. Song, Dongran & Fan, Xinyu & Yang, Jian & Liu, Anfeng & Chen, Sifan & Joo, Young Hoon, 2018. "Power extraction efficiency optimization of horizontal-axis wind turbines through optimizing control parameters of yaw control systems using an intelligent method," Applied Energy, Elsevier, vol. 224(C), pages 267-279.
    13. Jin, Yuqing & Ju, Ping & Rehtanz, Christian & Wu, Feng & Pan, Xueping, 2018. "Equivalent modeling of wind energy conversion considering overall effect of pitch angle controllers in wind farm," Applied Energy, Elsevier, vol. 222(C), pages 485-496.
    14. Sales-Setién, Ester & Peñarrocha-Alós, Ignacio, 2020. "Robust estimation and diagnosis of wind turbine pitch misalignments at a wind farm level," Renewable Energy, Elsevier, vol. 146(C), pages 1746-1765.
    15. Camila Correa-Jullian & Sergio Cofre-Martel & Gabriel San Martin & Enrique Lopez Droguett & Gustavo de Novaes Pires Leite & Alexandre Costa, 2022. "Exploring Quantum Machine Learning and Feature Reduction Techniques for Wind Turbine Pitch Fault Detection," Energies, MDPI, vol. 15(8), pages 1-29, April.
    16. Pan, Lin & Xiong, Yong & Zhu, Ze & Wang, Leichong, 2022. "Research on variable pitch control strategy of direct-driven offshore wind turbine using KELM wind speed soft sensor," Renewable Energy, Elsevier, vol. 184(C), pages 1002-1017.
    17. Xiao, Qing & Zhou, Shaowu, 2018. "Probabilistic power flow computation considering correlated wind speeds," Applied Energy, Elsevier, vol. 231(C), pages 677-685.
    18. Miguel A. Rodríguez-López & Luis M. López-González & Luis M. López-Ochoa & Jesús Las-Heras-Casas, 2018. "Methodology for Detecting Malfunctions and Evaluating the Maintenance Effectiveness in Wind Turbine Generator Bearings Using Generic versus Specific Models from SCADA Data," Energies, MDPI, vol. 11(4), pages 1-22, March.
    19. Bofeng Xu & Yue Yuan & Haoming Liu & Peng Jiang & Ziqi Gao & Xiang Shen & Xin Cai, 2020. "A Pitch Angle Controller Based on Novel Fuzzy-PI Control for Wind Turbine Load Reduction," Energies, MDPI, vol. 13(22), pages 1-16, November.
    20. Adrian Gambier & Yul Yunazwin Nazaruddin, 2022. "Modelling the Wind Turbine by Using the Tip-Speed Ratio for Estimation and Control," Energies, MDPI, vol. 15(24), pages 1-18, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:24:p:4712-:d:296342. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.