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PIDD2 Control of Large Wind Turbines’ Pitch Angle

Author

Listed:
  • Xingqi Hu

    (School of Control & Computer Engineering, North China Electric Power University, Beijing 102206, China)

  • Wen Tan

    (School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China)

  • Guolian Hou

    (School of Control & Computer Engineering, North China Electric Power University, Beijing 102206, China)

Abstract

The pitch control system has a profound impact on the development of wind energy, and yet a delay or non-minimum phase can weaken its performance. Thus, there is a strong incentive to enhance pitch control technology in order to counteract the negative effects of unidentified delays and non-minimum phase characteristics. To reduce the complexity of the parameter-tuning process and improve the performance of the system, in this paper, we propose a novel control method for wind turbine pitch angle with time delays. Specifically, the proposed control method is state-space PIDD2, which is based on internal model control (IMC) and the open-loop system step response. Then, considering the tracking, disturbance rejection and measurement noise, the proposed controller is verified through simulations. The simulation results demonstrate that the state-space PIDD2 (SS-PIDD2) can provide a trade-off between robustness, time domain performance and measurement noise attenuation and effectively improve pitch control performance in contrast to series PID and PI control methods.

Suggested Citation

  • Xingqi Hu & Wen Tan & Guolian Hou, 2023. "PIDD2 Control of Large Wind Turbines’ Pitch Angle," Energies, MDPI, vol. 16(13), pages 1-22, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:5096-:d:1184838
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    References listed on IDEAS

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    1. Yun-Su Kim & Il-Yop Chung & Seung-Il Moon, 2015. "Tuning of the PI Controller Parameters of a PMSG Wind Turbine to Improve Control Performance under Various Wind Speeds," Energies, MDPI, vol. 8(2), pages 1-20, February.
    2. Sungsu Park & Yoonsu Nam, 2012. "Two LQRI based Blade Pitch Controls for Wind Turbines," Energies, MDPI, vol. 5(6), pages 1-19, June.
    3. Cross, Philip & Ma, Xiandong, 2014. "Nonlinear system identification for model-based condition monitoring of wind turbines," Renewable Energy, Elsevier, vol. 71(C), pages 166-175.
    4. Wang, Huai-zhi & Li, Gang-qiang & Wang, Gui-bin & Peng, Jian-chun & Jiang, Hui & Liu, Yi-tao, 2017. "Deep learning based ensemble approach for probabilistic wind power forecasting," Applied Energy, Elsevier, vol. 188(C), pages 56-70.
    5. Gao, Richie & Gao, Zhiwei, 2016. "Pitch control for wind turbine systems using optimization, estimation and compensation," Renewable Energy, Elsevier, vol. 91(C), pages 501-515.
    6. Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2017. "Potential impacts of climate change on European wind energy resource under the CMIP5 future climate projections," Renewable Energy, Elsevier, vol. 101(C), pages 29-40.
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    Cited by:

    1. Yingming Liu & Yi Wang & Xiaodong Wang, 2024. "Independent Pitch Adaptive Control of Large Wind Turbines Using State Feedback and Disturbance Accommodating Control," Energies, MDPI, vol. 17(18), pages 1-17, September.

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