Wind Speed Prediction of Wind Turbine Based on the Internet of Machines and Spatial Correlation Weight
Shen Xiaojun1, Zhou Chongcheng1,2, Fu Xuejiao1
1. College of Electronic and Information Engineering Tongji University Shanghai 200092 China; 2. Jiading Power Supply Company State Grid Shanghai Electric Power Company Shanghai 201800 China
Abstract：Real-time wind speed prediction can effectively improve the control performance and power generation of wind turbine, and realize the efficient use of wind energy. On the basis of analyzing the temporal-spatial propagation characteristics of wind speed in wind farms, a real-time prediction framework based on Internet of machines and the weight of spatial correlation is proposed in this paper. The prediction model and process is established, a Kalman filter algorithm is developed and comparison study is carried out with the persistent model and traditional spatial correlation prediction method. Case study result shows that on small time scale, the proposed method has better prediction accuracy than the persistent model, and the fault tolerance and robustness are superior to the traditional spatial correlation method. The feasibility and effectiveness of the proposed method is verified, and the prediction framework is capable of multi-time scale prediction such as minute level and ultra-short term wind speed forecasting. The research results can provide new insights for accurate real-time wind speed perception of wind turbines.
沈小军, 周冲成, 付雪娇. 基于机联网-空间相关性权重的风电机组风速预测研究[J]. 电工技术学报, 2021, 36(9): 1782-1790.
Shen Xiaojun, Zhou Chongcheng, Fu Xuejiao. Wind Speed Prediction of Wind Turbine Based on the Internet of Machines and Spatial Correlation Weight. Transactions of China Electrotechnical Society, 2021, 36(9): 1782-1790.
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2021, Vol. 36 
