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Aiming at the local wind speed prediction of each turbine in the wind farm, a wind speed prediction method based on feature analysis of wind speed behavior coupling the time characteristics and spatial feature is proposed, and a three-dimensional convolutional neural network (3D-CNN) multi-output wind speed prediction model with behavioral feature learning is established. Though the overall depth learning of the spatiotemporal behavior of wind speed with the temporal and spatial characteristics information being uncoupled, the function of future wind velocity and history spatiotemporal behavior features of each turbine location was established and furthermore, realized the multi-step wind speed prediction of each turbine location of the whole wind farm. The feasibilities and effectiveness of the proposed model are verified by the obtained SCADA (Supervisory Control and Data Acquisition) data from a wind farm in Hebei Province, China. When the prediction step size is 7 days, comparison results showed that the mean absolute error (MAE) and root mean squared error (RMSE) of the proposed model are 7.51 % and 0.70 %, respectively, which can conduct the wind speed prediction use spatiotemporal feature information effectively. Meanwhile, performances of the proposed model and previous existing prediction models were also compared with its superior been illuminated.

Caused by the expansion construction of large-scale wind farm, wind turbines of different rotor-diameters always exist in typical area with abundant wind resources. This paper investigates the wake characteristics of turbines with different rotor-diameters and their wake interactions as well as performance through LiDAR measurement and SCADA data. The experiment wind farm is located in North China with a flat terrain and three different types of wind turbines are installed. Results show that the wake recovery is related to the rotor diameter, the larger of the turbine, the slower of its wake recovery. The wake of downwind turbine is aggravated by the upstream turbine’s wake, reflected in broader wake width, larger velocity deficit and depth. Meanwhile, the upstream wind turbine’s wake that close to the downstream wind turbine can also be affected, reflected in the velocity deficit and the wake width suddenly increases when the wake approaching the downwind turbine. The power loss caused by wake effect increased sharply when wind speed decreases and the wake of a 1.5 MW wind turbine makes a great power loss even after a long distance, thus, the wake effect of large wind turbine should be paid more attentions.