Wind speed optimisation method of numerical prediction for wind farm based on Kalman filter method

• Published in: Journal of engineering (Stevenage, England) Vol. 2017; no. 13; pp. 1146 - 1149
• Main Authors: Hua, Shenbing, Wang, Shu, Jin, Shuanglong, Feng, Shuanglei, Wang, Bo
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 2017
• Subjects: correlation coefficient; forecasted wind speed; Kalman filter method; Kalman filters; matrix algebra; numerical prediction; optimisation; predictive factor matrix; RMSE; weather research and forecast model; wind farm; wind power plants; wind speed forecast; wind speed optimisation method; WRF model; wind farm; Kalman filter method; RMSE; weather research and forecast model; predictive factor matrix; matrix algebra; wind speed forecast; numerical prediction; WRF model; optimisation; forecasted wind speed; wind speed optimisation method; Kalman filters; wind power plants; correlation coefficient
• ISSN: 2051-3305; 2051-3305
• DOI: 10.1049/joe.2017.0508

In recent years, with the rapid increase of wind turbine install, the volatility and randomness of wind speed restrict the development of wind power industry. The uncertainty of wind speed forecast has the mainly impact on wind power prediction. The subject of this manuscript is to study the impact of wind speed prediction on wind power prediction. Firstly, The authors compare the observed wind speed in one wind farm with the forecasted wind speed using the 3 km × 3 km Weather Research and Forecast (WRF) model. Then the revised is conducted based on Kalman filter theory. In the revised process, the special and temporal impact of observed wind speed on target wind farm is considered and the reasonable predictive factor matrix is built to lower the systematic and random error. The wind speed data at heights of 10, 30, 50, and 70 m are processed using this revised approach, respectively. It turns out that the correlation coefficient between the wind speed forecast and observation has been raised from 0.48 and 0.47 (no revision) to 0.88 and 0.89 (after revision). The RMSE of wind speed has been decreasing from 3.98 and 4.20 to 1.72 and 1.60 m/s. The accuracy of wind speed forecast has been improved.