Wind power station power projection method based on error statistics modification

Abstract

The invention discloses a wind power station power projection method based on error statistics modification. The procedures adopted by the invention is that firstly, data pretreatment is performed, the projection and measured data and the statistical data of the wind power station are divided into a plurality of sample sets according to different terrain heights; secondly, the sample sets are used for the training of a wind power projection model so as to form a 24-hour output short-term forecast model of the wind power station, the error distribution condition of the power can be projected through analyzing the projected power value and measured power value so as to obtain the expected value of the power projection error of the wind power station; thirdly, the power projection value of the wind power station can be obtained according to the numerical weather prediction data, weather forecast data and the trained model; and finally, the final power modification value of the wind power station can be obtained through modifying the power projection value according to the projection error statistical calculation result of the wind power station.

Description

technical field [0001] The application belongs to the technical field of electric power systems, and in particular relates to a method for correcting wind farm power prediction errors. Background technique [0002] Large-scale grid-connected wind farms bring a series of new problems to the power system, one of which is the impact on the operation and dispatch of the power system. Wind power forecasting is of great significance to the power balance and economic dispatch of the power system. According to the different predicted physical quantities, the current research on wind farm output power prediction can be divided into two categories, one is the prediction of wind speed, and then the power output of the wind farm is obtained according to the power curve of the wind turbine or wind farm; the second category is Directly predict the output power of wind farms. According to the different mathematical models used, it can be divided into continuous prediction method, ARMA mo...

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Problems solved by technology

From the point of view of modeling, there are essential differences between different time scales. For the prediction of 0-3h, because the change is mainly determined by the persistence of atmospheric conditions, better prediction results can be obtained by using the time series model; and For long-term predictions, regardless of the fact that numerical weather prediction data cannot reflect the nature of atmospheric motion, it is difficult to obtain better prediction results. Therefore, the output power prediction of wind farms studied now takes numerical weather prediction data as a set of important inputs. data

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