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A robust gain scheduling method for a PI collective pitch controller of multi-MW onshore wind turbines

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  • Hawari, Qusay
  • Kim, Taeseong
  • Ward, Christopher
  • Fleming, James

Abstract

This work proposes a robust tuning method for full-load pitch control by deriving new design formulae for collective Proportional Integral (PI) pitch controllers. The paper investigates the frequency domain characteristics of a full state linearized turbine model extracted from the aeroelastic simulation tool HawcStab2, and suggests a reduced order model that captures the low-frequency behaviour and may be used to derive PI tuning formulae that account for collective blade flap modes. The proposed controllers are then compared to traditional PI controllers based on a single degree of freedom (1-DOF) model of the drive-train using linearized NREL5MW and DTU10MW models. The 10 MW model is investigated in more detail through non-linear simulations in HAWC2 using wind step and turbulent wind conditions. The proposed design formulae show robust results, giving more consistent gain and phase margins than 1-DOF designs throughout the above rated wind speed region, and may be used to increase controller bandwidth while maintaining acceptable stability margins, achieving 49% and 63% reductions in standard deviation of the output power for the DTU10MW model in turbulent conditions. Statistical analysis for both controllers was also performed to investigate fatigue loading on the main shaft caused by the pitch actuation.

Suggested Citation

  • Hawari, Qusay & Kim, Taeseong & Ward, Christopher & Fleming, James, 2022. "A robust gain scheduling method for a PI collective pitch controller of multi-MW onshore wind turbines," Renewable Energy, Elsevier, vol. 192(C), pages 443-455.
    • Handle: RePEc:eee:renene:v:192:y:2022:i:c:p:443-455
    DOI: 10.1016/j.renene.2022.04.117
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    References listed on IDEAS

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    1. Yuan, Yuan & Chen, Xu & Tang, J., 2020. "Multivariable robust blade pitch control design to reject periodic loads on wind turbines," Renewable Energy, Elsevier, vol. 146(C), pages 329-341.
    2. Lasheen, Ahmed & Elshafei, Abdel Latif, 2016. "Wind-turbine collective-pitch control via a fuzzy predictive algorithm," Renewable Energy, Elsevier, vol. 87(P1), pages 298-306.
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    4. Mokhtari, Yacine & Rekioua, Djamila, 2018. "High performance of Maximum Power Point Tracking Using Ant Colony algorithm in wind turbine," Renewable Energy, Elsevier, vol. 126(C), pages 1055-1063.
    5. Ossmann, Daniel & Seiler, Peter & Milliren, Christopher & Danker, Alan, 2021. "Field testing of multi-variable individual pitch control on a utility-scale wind turbine," Renewable Energy, Elsevier, vol. 170(C), pages 1245-1256.
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    Cited by:

    1. Yang, Lin & Liao, Kangping & Ma, Qingwei & Ma, Gang & Sun, Hanbing, 2023. "Investigation of wake characteristics of floating offshore wind turbine with control strategy using actuator curve embedding method," Renewable Energy, Elsevier, vol. 218(C).
    2. Hawari, Qusay & Kim, Taeseong & Ward, Christopher & Fleming, James, 2023. "LQG control for hydrodynamic compensation on large floating wind turbines," Renewable Energy, Elsevier, vol. 205(C), pages 1-9.
    3. Dongmyoung Kim & Taesu Jeon & Insu Paek & Wirachai Roynarin & Boonyang Plangklang & Bayasgalan Dugarjav, 2023. "A Study on the Improved Power Control Algorithm for a 100 kW Wind Turbine," Energies, MDPI, vol. 16(2), pages 1-15, January.

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