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Effect investigation of yaw on wind turbine performance based on SCADA data

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  • Dai, Juchuan
  • Yang, Xin
  • Hu, Wei
  • Wen, Li
  • Tan, Yayi

Abstract

Due to the time-varying wind direction, yaw operation of wind turbines is a common state. Under yaw, the aerodynamic behavior of wind turbines is complicated, and also causes complex energy capture performance. To clarify some vague knowledge, a detailed investigation of yaw effect based on SCADA data is carried out. Firstly, a yaw coefficient definition and its specific calculation method are presented. Furthermore, to analyze the energy capture mechanism, the loss factor of energy capture (power coefficient) is subdivided into aerodynamic loss factor and inertia loss factor, which means both the effects of aerodynamic characteristic and mechanical inertia on energy capture are considered. According to this understanding, power coefficient is expanded as a function of four factors. Then, a single-valued processing method is employed to investigate the relationship between wind speed and output power. Subsequently, relationship between yaw coefficient and output power is investigated. Comparative investigations are carried out by two different methods, that is, least square fitting (LSF) method and kernel density estimation (KDE) method. Also, characteristics of power coefficient and rotor torque under yaw are investigated. Effect laws of yaw coefficient on wind turbine power, power coefficient, and rotor torque are obtained. Finally, the relationship between the internal control mechanism and external output of wind turbines is discussed.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:149:y:2018:i:c:p:684-696
    DOI: 10.1016/j.energy.2018.02.059
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    Cited by:

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    17. Long, Huan & Xu, Shaohui & Gu, Wei, 2022. "An abnormal wind turbine data cleaning algorithm based on color space conversion and image feature detection," Applied Energy, Elsevier, vol. 311(C).

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