Fluctuation feature identification-based wind power combination prediction method

A technology that combines forecasting and wind power, applied in forecasting, character and pattern recognition, computer components, etc., to solve problems such as transient or permanent grid failures, economic losses, etc.

Inactive Publication Date: 2017-07-07
CHINA AGRI UNIV
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although the conventional power prediction method has met the requirements of wind farm operation, when there is a significant instantaneous fluctuation of wind power power, that is, a wind power ramp event, it will cause instantaneous or permanent failure of the power grid and cause huge economic losses.

Method used

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  • Fluctuation feature identification-based wind power combination prediction method
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Embodiment Construction

[0059] The above is only an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0060] Such as figure 1 As shown, a kind of wind power combination prediction method based on the feature recognition of climbing event described in the present invention comprises the following steps:

[0061] Step S1. For the historical wind speed data and historical power data of the wind farm, use the wavelet denoising method to obtain smooth historical wind speed curves and historical power curves, so as to eliminate the interference caused by noise.

[0062] The useful signal usually appears as a low-frequency signal or is relatively stable, while the noise signal usually appears as a high-frequency signal. After the noise-containing original signal is decomposed by the wa...

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Abstract

The invention discloses a fluctuation feature identification-based wind power combination prediction method. The method comprises the following steps of 1, processing historical wind speed and power data of a wind power plant by utilizing a wavelet denoising method to obtain smooth curves; 2, performing fluctuation feature identification and extraction on the obtained curves by adopting a compression algorithm; 3, classifying eigenvalues of fluctuations of wind power obtained in the step 2 by utilizing a fuzzy clustering method; 4, training different fluctuation types by utilizing a statistical method and building a prediction model; 5, performing the steps 1-2 on real-time wind speed test data and real-time power test data of the wind power plant, and extracting eigenvalues of fluctuations of the real-time wind speed and power test data; and 6, classifying the extracted eigenvalues of the fluctuations of the real-time wind speed and power test data by utilizing the prediction model obtained in the step 4, and performing prediction by utilizing the prediction model, thereby obtaining a final combination prediction result finally.

Description

technical field [0001] The invention relates to the field of power system operation and control, in particular to a wind power combination prediction method based on feature recognition of wind farm ramp events. Background technique [0002] With the depletion of non-renewable resources such as coal and oil and the increasingly serious energy dilemma, renewable energy such as wind energy, solar energy, tidal energy and biomass energy has attracted more and more attention worldwide. Wind power is the renewable energy with the most mature technology and the most development value in the renewable energy generation technology. The development of wind power is of great significance to ensure energy security, adjust energy structure, reduce environmental pollution, and achieve sustainable development. [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 increas...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/18G06F17/14G06K9/62G06N99/00G06Q10/04G06Q50/06
CPCG06F17/14G06F17/18G06N20/00G06Q10/04G06Q50/06G06F18/2155
Inventor 叶林腾景竹张慈杭陈超宇
Owner CHINA AGRI UNIV
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