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Wind and Structures
  Volume 30, Number 4, April 2020, pages 393-403
DOI: http://dx.doi.org/10.12989/was.2020.30.4.393
 


Nonlinear Kalman filter bias correction for wind ramp event forecasts at wind turbine height
Jing-Jing Xu, Zi-Niu Xiao and Zhao-Hui Lin

 
Abstract
    One of the growing concerns of the wind energy production is wind ramp events. To improve the wind ramp event forecasts, the nonlinear Kalman filter bias correction method was applied to 24-h wind speed forecasts issued from the WRF model at 70-m height in Zhangbei wind farm, Hebei Province, China for a two-year period. The Kalman filter shows the remarkable ability of improving forecast skill for real-time wind speed forecasts by decreasing RMSE by 32% from 3.26 m s-1 to 2.21 m s-1, reducing BIAS almost to zero, and improving correlation from 0.58 to 0.82. The bias correction improves the forecast skill especially in wind speed intervals sensitive to wind power prediction. The fact shows that the Kalman filter is especially suitable for wind power prediction. Moreover, the bias correction method performs well under abrupt weather transition. As to the overall performance for improving the forecast skill of ramp events, the Kalman filter shows noticeable improvements based on POD and TSS. The bias correction increases the POD score of up-ramps from 0.27 to 0.39 and from 0.26 to 0.38 for down-ramps. After bias correction, the TSS score is significantly promoted from 0.12 to 0.26 for up-ramps and from 0.13 to 0.25 for down-ramps.
 
Key Words
    numerical simulation; wind power prediction; bias correction; nonlinear Kalman filter; WRF model
 
Address
Jing-Jing Xu:International Center for Climate and Environment Science (ICCES), Institute of Atmospheric Physics,Chinese Academy of Science, Beijing 100029, China
Zi-Niu Xiao:State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG),
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Zhao-Hui Lin:International Center for Climate and Environment Science (ICCES), Institute of Atmospheric Physics,Chinese Academy of Science, Beijing 100029, China

 

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