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Predictive model of yaw error in a wind turbine

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  • Ouyang, Tinghui
  • Kusiak, Andrew
  • He, Yusen

Abstract

The yaw position of a wind turbine is adjusted in response to the changing wind direction for maximum energy extraction. A data-mining approach is proposed to predict wind direction. To accommodate the full range of yaw motion, the wind direction data is transformed into two time series (sine value and cosine values). Parameters of the time series are selected for predictive modeling. Four data-mining algorithms are applied to build prediction models. Industrial data is used to develop, validate, and test the proposed models. Computational experience with data representing four seasons and four sampling frequencies is reported in this paper.

Suggested Citation

  • Ouyang, Tinghui & Kusiak, Andrew & He, Yusen, 2017. "Predictive model of yaw error in a wind turbine," Energy, Elsevier, vol. 123(C), pages 119-130.
    • Handle: RePEc:eee:energy:v:123:y:2017:i:c:p:119-130
    DOI: 10.1016/j.energy.2017.01.150
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Dai, Juchuan & Yang, Xin & Hu, Wei & Wen, Li & Tan, Yayi, 2018. "Effect investigation of yaw on wind turbine performance based on SCADA data," Energy, Elsevier, vol. 149(C), pages 684-696.
    2. Tang, Zhenhao & Zhao, Gengnan & Ouyang, Tinghui, 2021. "Two-phase deep learning model for short-term wind direction forecasting," Renewable Energy, Elsevier, vol. 173(C), pages 1005-1016.
    3. Song, Dongran & Fan, Xinyu & Yang, Jian & Liu, Anfeng & Chen, Sifan & Joo, Young Hoon, 2018. "Power extraction efficiency optimization of horizontal-axis wind turbines through optimizing control parameters of yaw control systems using an intelligent method," Applied Energy, Elsevier, vol. 224(C), pages 267-279.
    4. Jing, Bo & Qian, Zheng & Pei, Yan & Zhang, Lizhong & Yang, Tingyi, 2020. "Improving wind turbine efficiency through detection and calibration of yaw misalignment," Renewable Energy, Elsevier, vol. 160(C), pages 1217-1227.
    5. Marugán, Alberto Pliego & Márquez, Fausto Pedro García & Perez, Jesus María Pinar & Ruiz-Hernández, Diego, 2018. "A survey of artificial neural network in wind energy systems," Applied Energy, Elsevier, vol. 228(C), pages 1822-1836.
    6. Cheng, Youliang & Xue, Zhanpu & Jiang, Tuo & Wang, Wenyang & Wang, Yuekun, 2018. "Numerical simulation on dynamic response of flexible multi-body tower blade coupling in large wind turbine," Energy, Elsevier, vol. 152(C), pages 601-612.
    7. Guo, Peng & Chen, Si & Chu, Jingchun & Infield, David, 2020. "Wind direction fluctuation analysis for wind turbines," Renewable Energy, Elsevier, vol. 162(C), pages 1026-1035.
    8. Han, Qinkai & Ma, Sai & Wang, Tianyang & Chu, Fulei, 2019. "Kernel density estimation model for wind speed probability distribution with applicability to wind energy assessment in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    9. Song, Dongran & Li, Ziqun & Wang, Lei & Jin, Fangjun & Huang, Chaoneng & Xia, E. & Rizk-Allah, Rizk M. & Yang, Jian & Su, Mei & Joo, Young Hoon, 2022. "Energy capture efficiency enhancement of wind turbines via stochastic model predictive yaw control based on intelligent scenarios generation," Applied Energy, Elsevier, vol. 312(C).
    10. Yan Pei & Zheng Qian & Bo Jing & Dahai Kang & Lizhong Zhang, 2018. "Data-Driven Method for Wind Turbine Yaw Angle Sensor Zero-Point Shifting Fault Detection," Energies, MDPI, vol. 11(3), pages 1-14, March.
    11. Jesús Rascón & Wildor Gosgot Angeles & Manuel Oliva-Cruz & Miguel Ángel Barrena Gurbillón, 2022. "Wind Characteristics and Wind Energy Potential in Andean Towns in Northern Peru between 2016 and 2020: A Case Study of the City of Chachapoyas," Sustainability, MDPI, vol. 14(10), pages 1-11, May.

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