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Combining LIDAR and LADRC for intelligent pitch control of wind turbines

Author

Listed:
  • Jia, Chengzhen
  • Wang, Lingmei
  • Meng, Enlong
  • Chen, Liming
  • Liu, Yushan
  • Jia, Wenqiang
  • Bao, Yutao
  • Liu, Zhenguo

Abstract

At present, most of the pitch control methods are based on PI controller, the pitch control system has poor disturbance resistance, and the research of variable parameter feedforward based on Light detection and ranging (LIDAR) and the Linear Active Disturbance Rejection controller (LADRC) composite control is rarely studied to reduce the blade root load, so this paper conceives a hybrid intelligent and adaptive pitch control approach to reduce a wind turbine generator speed fluctuation and its blade root load. Specifically, we combine the Radial Basis Neural Network and Finite Impulse Response filter (RBFNNFIR) based on LIDAR wind measurement. We then use a variable bandwidth of LADRC controller. Overall the approach enables and facilitates self-adaption and self-adjustment. We use Matlab s-function to call the multi-freedom mathematical wind turbine model based on FAST code, the composite intelligent control algorithm is established in Simulink. Initial results from the statistical analysis of the experiments under different turbulent wind conditions shows that the hybrid intelligent pitch control approach can reduce the generator speed fluctuation by about 40.8%, and the blade root max value of load moment by about 13.1%, compared with the baseline values of the traditional variable gain PI control algorithm.

Suggested Citation

  • Jia, Chengzhen & Wang, Lingmei & Meng, Enlong & Chen, Liming & Liu, Yushan & Jia, Wenqiang & Bao, Yutao & Liu, Zhenguo, 2021. "Combining LIDAR and LADRC for intelligent pitch control of wind turbines," Renewable Energy, Elsevier, vol. 169(C), pages 1091-1105.
    • Handle: RePEc:eee:renene:v:169:y:2021:i:c:p:1091-1105
    DOI: 10.1016/j.renene.2021.01.065
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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. Yuan, Yuan & Tang, J., 2017. "Adaptive pitch control of wind turbine for load mitigation under structural uncertainties," Renewable Energy, Elsevier, vol. 105(C), pages 483-494.
    3. Wu, Zhenlong & Li, Donghai & Xue, Yali & Chen, YangQuan, 2019. "Gain scheduling design based on active disturbance rejection control for thermal power plant under full operating conditions," Energy, Elsevier, vol. 185(C), pages 744-762.
    4. Abdelbaky, Mohamed Abdelkarim & Liu, Xiangjie & Jiang, Di, 2020. "Design and implementation of partial offline fuzzy model-predictive pitch controller for large-scale wind-turbines," Renewable Energy, Elsevier, vol. 145(C), pages 981-996.
    5. Lasheen, Ahmed & Saad, Mohamed S. & Emara, Hassan M. & Elshafei, Abdel Latif, 2017. "Continuous-time tube-based explicit model predictive control for collective pitching of wind turbines," Energy, Elsevier, vol. 118(C), pages 1222-1233.
    6. 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.
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    Cited by:

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    2. Golnary, Farshad & Tse, K.T., 2021. "Novel sensorless fault-tolerant pitch control of a horizontal axis wind turbine with a new hybrid approach for effective wind velocity estimation," Renewable Energy, Elsevier, vol. 179(C), pages 1291-1315.
    3. Hou, Guolian & Huang, Ting & Zheng, Fumeng & Gong, Linjuan & Huang, Congzhi & Zhang, Jianhua, 2023. "Application of multi-agent EADRC in flexible operation of combined heat and power plant considering carbon emission and economy," Energy, Elsevier, vol. 263(PB).
    4. Chen, Hang & Wei, Shanbi & Yang, Wei & Liu, Shanchao, 2023. "Input wind speed forecasting for wind turbines based on spatio-temporal correlation," Renewable Energy, Elsevier, vol. 216(C).
    5. Amira Elkodama & Amr Ismaiel & A. Abdellatif & S. Shaaban & Shigeo Yoshida & Mostafa A. Rushdi, 2023. "Control Methods for Horizontal Axis Wind Turbines (HAWT): State-of-the-Art Review," Energies, MDPI, vol. 16(17), pages 1-32, September.
    6. Tang, Shengming & Li, Tiantian & Guo, Yun & Zhu, Rong & Qu, Hongya, 2022. "Correction of various environmental influences on Doppler wind lidar based on multiple linear regression model," Renewable Energy, Elsevier, vol. 184(C), pages 933-947.
    7. Tang, Shengming & Guo, Yun & Wang, Xu & Zhu, Rong & Tang, Jie & Zhang, Shuai, 2023. "Evaluation and impact factors of Doppler wind lidar during Super Typhoon Lekima (2019)," Renewable Energy, Elsevier, vol. 205(C), pages 305-316.

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