风电场风速规律分析及风电功率预报方法研究

doi:10.12118/j.issn.1000-6060.2020.05.11

Abstract

Abstract: Based on the observation data of one wind tower and thirty three wind turbines of a wind farm in Shaan’xi Province from 1st January 2016 to 31st December 2018，wind speed variation in the wind farm was analyzed，forecast models for wind power business were established by using simple and multiple linear regression methods with introducing the optimal training period. The optimal training period was defined as a sliding cycle，which was the N days before the forecast date in the training samples，for n=4，5…，365 days，the N was decided by the minimum absolute error of the forecast，and then the wind power forecast model was established with the optimal training period day by day. The results showed that the wind speed and its difference at various heights had a clear diurnal cycle characteristics from the land surface to height of 80 meters，which exhibited that the maximum value took place in the night time, while the minimum value took place in the day time. The wind speed difference between heights of 80 meters and 10 meters always maintained the maximum in all levels，then the second maximum was between heights of 50 meters and 10 meters，but the wind speed difference was not completely proportional with the height difference between them. For example, the variation in wind speed between height of 30 meters and 10 meters was bigger than that between 30 meters and 80 meters. Wind speed was the most important factor affecting wind power，and wind power had a significant positive correlation with wind speed at various heights. The correlation between wind speed and wind power during the 09:00 and 17:00 in every day was significantly better than the other period of time. If the observation samples were divided into two groups according to whether the wind speed was greater than 5 m·s^-1，then regression relationship between wind speed and wind power would be better，and the wind power forecast model would be significantly improved. Based on diurnal variation of correlation coefficient between wind power output and wind speed，as well as considering wind speed scale and the other factors，taking the wind speed at the height of the fan hub (80 m) as the forecast factor，then the wind power forecast equation that made forecast error smaller and optimal training period shorter was established by the simple linear regression method with the optimal training period. As a result，the wind power forecast equation could be applied to business with getting the wind speed prediction at Fan hub height，which was not a common forecast product of Numerical weather prediction model. The wind speed forecast at other height，which will be used for wind power forecast，should be revised to the height of the fan hub firstly．

Key words: wind farm, wind power prediction, linear regression method, optimal training period

WANG Dan, GAO Hong-yan, YANG Yan-chao, LI Bo, ZHANG Li. Study on wind speed variation and wind power prediction in wind farm[J].Arid Land Geography, 2020, 43(5): 1261-1269.

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Study on wind speed variation and wind power prediction in wind farm

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