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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
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    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.
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    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.
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    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.
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    Cited by:

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