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Wind power plant signal feature extraction and de-noising data optimization method

A technology for signal characteristics and data optimization, applied in the field of data processing, can solve problems such as the inability to describe the characteristics of wind power plant signal data, and achieve the effects of good commercial development prospects, easy implementation, and convenient commercial development

Pending Publication Date: 2022-04-29
STATE GRID LIAONING ELECTRIC POWER RES INST +2
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] In traditional data processing, there is still a problem that only the empirical mode is decomposed. When the data is not pure white noise, the empirical mode will overlap and cannot accurately describe the characteristics of the wind power plant signal data.

Method used

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  • Wind power plant signal feature extraction and de-noising data optimization method
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  • Wind power plant signal feature extraction and de-noising data optimization method

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Embodiment 1

[0069] The present invention provides an embodiment, which is a method for wind farm signal feature extraction and denoising optimization data. Such as figure 1 As shown, it is an overall flow chart of the method for extracting wind farm signal features and denoising and optimizing data according to the present invention. The analytical signal is obtained by establishing the Hilbert-Huang transform model of the white noise clustering empirical method after obtaining the measured signal of the wind farm. Then the analytical signal is analyzed and sampled to obtain the stable value of the signal through the Gaussian filter adaptive method. From figure 1 It can be seen from the process flow in the present invention that the method of the present invention is divided into two major steps, one is to extract the characteristics of the measured signal parameters, and the other is to remove noise from the measured signal, and the first is based on the Hilbert method of white noise c...

Embodiment 2

[0122] Based on the same inventive concept, an embodiment of the present invention also provides a computer storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the wind farm signal described in Embodiment 1 is realized. Steps in the method of feature extraction and denoising optimization data.

[0123] Those skilled in the art should understand that the embodiments of the present application may be provided as methods, systems, or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

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Abstract

The invention belongs to the technical field of data processing, and particularly relates to a wind power plant signal feature extraction and de-noising data optimization method. Comprising the steps of 1, obtaining wind power plant measurement signal parameters; 2, EEMD parameters in the measurement signals are obtained; step 3, establishing a Hilbert-Huang transform model of a white noise clustering empirical method according to the measured signal parameters; 4, analyzing to obtain a signal, and determining the length of a filtering window; step 5, sampling the number m of points for the analysis signal; step 6, setting the number of iterations K; 7, calculating derivative values and an average value of each point of the analysis signal sampling points; step 8, calculating and analyzing the weight of each point in the signal filtering window to de-noise each signal point; step 9, performing iteration for K times; and step 10, obtaining an accurate wind power plant analysis signal characteristic value. The method can effectively and accurately perform signal feature extraction and de-noising optimization of the wind power plant, is easy to implement, and has a wide market application prospect.

Description

technical field [0001] The invention belongs to the technical field of data processing, and in particular relates to a method for wind farm signal feature extraction and denoising optimization data, in particular to a Hilbert-Huang transform (HHT) based on white noise clustering empirical method (EEMD) The feature extraction of the measurement data and then the adaptive denoising method through Gaussian filtering. Background technique [0002] The current power system development mainly focuses on the development of non-polluting renewable new energy. The main ways to use new energy to generate electricity are: wind power, solar power, tidal power, fuel cells and other technologies, as well as advanced energy storage technologies such as supercapacitors and battery packs that cooperate with these technologies. However, new energy power generation has obvious advantages: less demanding location conditions, flexible installation locations, and dispersible distribution points,...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06F17/14
CPCG06F17/14G06F2218/02G06F2218/04G06F2218/08
Inventor 李家珏孙俊杰张晓珩杨宏宇钱小毅叶鹏刘宛菘
Owner STATE GRID LIAONING ELECTRIC POWER RES INST
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