Combined wind power prediction method based on wind speed fluctuation characteristic extraction

Abstract

The invention discloses a combined wind power prediction method based on wind speed fluctuation characteristic extraction. The combined wind power prediction method includes the following steps that wind speed data acquired by training samples are normalized; time windows are established for the normalized wind speeds, and multifractal spectrum analysis is performed in the time windows; the widths omega of singular index alpha value taking intervals of the time windows and symmetry parameters S of peak value differences Deltaf (alpha) and f (alpha) of a singular spectrum function f (alpha) are analyzed and compared. The wind speeds are classified according to the parameters [omega, Delta f (alpha), S], and the sizes of the time windows are further adjusted. Divided categories are sequentially trained by using an extreme learning machine, a support vector machine and an optimization regression power curve method, average monthly precision comparison is conducted on produced prediction results, one of the methods is selected to serve as an optimum single algorithm for the categories, and trained models are obtained. Same classification and modeling are conducted on test samples, corresponding optimum single algorithms are selected for different models for respective prediction, and finally final prediction results are obtained through combination.

Description

technical field [0001] The invention relates to a wind power combination prediction method based on wind speed fluctuation feature extraction, which belongs to the field of power system operation and control. Background technique [0002] With the gradual increase of the penetration rate of wind power in the entire power system of our country, the problems of voltage control, active power dispatching and system stability caused by its volatility, intermittent and randomness have become more and more prominent. Accurate wind power forecasting can not only reduce the System reserve capacity and energy storage reduce system operating costs, and at the same time help to reduce the impact of wind power access on the grid and improve grid operation reliability. [0003] The intermittent nature of wind energy in nature determines that wind power has strong fluctuations. As the number and installed capacity of wind farms continue to increase, once wind power is integrated into the g...

AI Technical Summary

Problems solved by technology

Therefore, the prediction accuracy of a single prediction model under different wind speed conditions needs to be improved

[0006] In addition, when the wind speed changes drastically, the frequency and intensity of the wind speed fluctuations in this time period will also increase

As time goes by, only building a model for historical data in a specific period of time cannot satisfy the dynamic characteristics of wind speed fluctuations on all time series

Moreover, the size of the time window has a crucial impact on the extraction and classification of data features. Too large and too small windows are not conducive to feature analysis.

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