(1. School of Electrical Engineering, Southeast University, Nanjing 210096, China; 2. State Key Laboratory of Operation and Control of Renewable Energy and Storage Systems(China Electric Power Research Institute), Nanjing 210003, China; 3. NARI Group Corporation(State Grid Electric Power Research Institute), Nanjing 211106, China; 4. State Key Laboratory of Smart Grid Protection and Control, Nanjing 211106, China; 5. Shenhua Group Corporation Limited, Beijing 100011, China; 6. Electric Power Research Institute of State Grid Gansu Electric Power Corporation, Lanzhou 730050, China; 7. Wind Power Technology Center of State Grid Gansu Electric Power Corporation, Lanzhou 730050, China)
Spatial correlation of wind speed is helpful to improving its prediction quality, especially when there are sudden changes of wind speed. A new method for ultra-short term wind speed prediction based on the idea of “offline modeling by classification, and online feature matching for model selection” is proposed. By analyzing time series among historical data, the time segments having spatial correlation in different wind farms are identified. The time segments of wind speed in the current time window are divided into sample subsets with different evolution patterns according to the features of time series and other external conditions. Prediction models and corresponding parameters for different patterns are optimized offline based on their sample subsets, respectively. While for online application, prediction models and the corresponding parameters are selected by feature matching, according to evolution patterns and other external conditions in the current time window. Finally, a case study using actual historical data is presented to validate the effectiveness of the proposed method. This work is supported by the State Key Program of National Natural Science Foundation of China(No. 61533010), NSFC-NRCT(Sino-Thai)Cooperation Research Project(No. 51561145011)and State Grid Corporation of China.
CHEN Ning, XUE Yusheng, DING Jie,et al.Ultra-short Term Wind Speed Prediction Using Spatial Correlation[J].Automation of Electric Power Systems,2017,41(12):124-130.DOI:10.7500/AEPS20170109004Copy