Wind power prediction system for wind farm based on auto regressive statistical model and physical model

作者：Wu Bingheng^*; Song Mengxuan; Chen Kai; He Zhongyang; Zhang Xing

来源：Journal of Renewable and Sustainable Energy, 2014, 6(1): 013101.

DOI：10.1063/1.4861063

Extracting energy from renewable sources such as wind energy is widely investigated in the past decades to mitigate the global energy crisis and environmental pollution. For a wind farm that converts wind energy into electricity power, a real-time prediction system of the output power is significant. In this paper, a prediction system is developed with a method of combining statistical model and physical model. In this system, the inlet condition of the wind farm is forecasted by the auto regressive model. The flow field is computed by the Reynolds average Navier-stokes simulation in the computational fluid dynamics model. The wake flow is calculated by the particle model, which can be used over complex terrain. Taking also the terrain condition, the property of turbines and wake flow model into account, the output power of the wind farm can be further predicted. The proposed prediction system is tested by the data from Wattle Point Wind Farm in Australia. Through the data post-processing, the error of the mean daily output power is less than 5%. The proposed system is effective for power output prediction of wind farm.

• 出版日期2014-1
• 单位清华大学