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A Comparative Study of Optimal Individual Pitch Control Methods

Author

Listed:
  • Abhinandan Routray

    (School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea)

  • Nitin Sivakumar

    (School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea)

  • Sung-ho Hur

    (School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea)

  • Deok-je Bang

    (Electric Machine and Drives Research Center, Korea Electrotechnology Research Institute (KERI), Changwon 51543, Republic of Korea)

Abstract

Wind turbines are subjected to asymmetric loads and fatigue with subsequent increases in their dimension and capacity, leading to a reduction in their lifetime. To address this problem, the individual pitch control (IPC) technique is quite familiar in the control of wind turbines. IPC is used to reduce the tilt and yaw moments, simultaneously alleviating the turbine blade-root bending moments (BRBMs). This study discusses the performance of model predictive control (MPC), H-infinity ( H ∞ ), and proportional and integral (PI)-based IPC strategies integrated with collective pitch control. The performance of the reported controllers has been validated using the National Renewable Energy Laboratory (NREL) 5 MW full nonlinear reference wind turbine. Simulation studies are conducted at varying wind speeds and turbulent intensities as per international electrotechnical commission (IEC) norms. Comparative results in the time and frequency domains indicate that the H ∞ based IPC achieves enhanced control performance in terms of reduction in BRBMs and damage equivalent load compared to MPC and PI-based control strategies.

Suggested Citation

  • Abhinandan Routray & Nitin Sivakumar & Sung-ho Hur & Deok-je Bang, 2023. "A Comparative Study of Optimal Individual Pitch Control Methods," Sustainability, MDPI, vol. 15(14), pages 1-25, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:10933-:d:1192445
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    References listed on IDEAS

    as
    1. Sungsu Park & Yoonsu Nam, 2012. "Two LQRI based Blade Pitch Controls for Wind Turbines," Energies, MDPI, vol. 5(6), pages 1-19, June.
    2. Abhinandan Routray & Yiza Srikanth Reddy & Sung-ho Hur, 2023. "Predictive Control of a Wind Turbine Based on Neural Network-Based Wind Speed Estimation," Sustainability, MDPI, vol. 15(12), pages 1-22, June.
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    Citations

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    Cited by:

    1. Chae-Wook Lim, 2024. "A Study of a Gain-Scheduled Individual Pitch Controller for an NREL 5 MW Wind Turbine," Energies, MDPI, vol. 17(1), pages 1-12, January.

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