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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

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