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Wind farm operation is characterized by large scale, fast and disorderly, the construction of grid structure is not complete, the fast adjustment of power supply in the power grid does not match to others, the objective laws of electric power system and uncontrollable and intermittent of wind power, and adjustable power supply capacity constraints in power grid, all of them cause the consumptive ability of wind power grid, which leads to more and more abandoned wind. This paper studies the tower neural network method, according to different height wind speed and wind direction data of historical wind tower measuring, combining with the fan power of historical observation data of wind farm, and building the neural network model, then the sample data will be input to the neural network model which has built to get theoretical power fan and abandoned wind power. By comparing the wind tower method, neural network method, model machine method with area integral method calculate abandoned wind power data, evaluate the effect that based on abandoned wind power tower evaluation model of neural network method in low wind speed has a good reference value, relatively close to the measured wind speed.

Wind power prediction accuracy is important for assessing the security and economy when wind power is connected to the grid, and wind speed is the key factor. This article presents a future four hours prediction scheme that combined chaos phase space reconstruction with NWP method. Historical wind speed data are reconstructed as phase space vectors, which are used as the first input part of prediction model, and the NWP data at the prediction time as the second input part. Wind speed at the height of turbine hub is derived from neural network model output. To test the approach, the data from a wind farm are used for this study. The prediction results are presented and compared separately to the chaos neural network model, NWP ANN model and persistence model. The results show that the method presented in this paper has higher prediction precision.

For direct drive synchronous wind power generation system, the converter of direct drive motor is connected to the stator side of permanent magnet synchronous generator side, through controlling the converter, the maximum wind energy can be captured. Main circuit design of the direct-drive grid-converter is studied. Firstly, power grid side filter is designed and calculated, LCL filter is used instead of traditional L type filter, in this way, the value of the inductance、 the volume and cost are reduced effectively, and its inhibitory effect on high frequency signal is more obvious. Not only meet the vector triangle, but also grid connection harmonic current is restrained. Secondly, the filter of motor side is also designed, about the problem of voltage reflection for long cable drive motor PWM converter on the motor side, the solution is to reduce the output value of the du/dt and increase the pulse rise time, at the same time the reflected voltage of the motor side is also reduced. Finally ,Brake unit is researched, at the instant of the power grid voltage dips is detected, chopper control is started to IGBT of brake unit, making the machine side power in addition to the net from the side of the part, the rest are consumed in the brake unit, to ensure that the equipment can grid-connected operated. The last but one, the pre-charging circuit is included.

Now many kinds of technologies applied to wind-power generation are in the ascendant, among which the technology of variable speed and constant frequency (VSCF) is getting increasing recognition for its characteristic of high-efficiency and practicability. In this paper, it brought forward the method of emulating wind turbine by squirrel-cage induction motor to solve the prime motor issue for the study of wind power generating system in lab. The reliable and integrated laboratory experimental platform of wind power generation system was constructed in this paper. The result of the experiments all validated the validity and feasibility of the model of VSCF wind power generation system platform in the lab.