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Multiple-wind-power-plant correlation modeling method based on adaptive multi-variable nonparametric kernel density estimation

A non-parametric kernel density and modeling method technology, applied in the field of multi-wind farm output correlation modeling, which can solve problems such as low local accuracy

Active Publication Date: 2017-08-11
CHINA THREE GORGES UNIV
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Problems solved by technology

The adaptive improvement strategy based on the optimal bandwidth matrix adjustment model can better solve the problem of low local accuracy of the existing multivariable non-parametric kernel density estimation, and further improve the modeling accuracy

Method used

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  • Multiple-wind-power-plant correlation modeling method based on adaptive multi-variable nonparametric kernel density estimation
  • Multiple-wind-power-plant correlation modeling method based on adaptive multi-variable nonparametric kernel density estimation
  • Multiple-wind-power-plant correlation modeling method based on adaptive multi-variable nonparametric kernel density estimation

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Experimental program
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Embodiment

[0120] The present invention takes 4773 sampling sequences of wind power output in the same time period of six wind farms in a certain place in Hubei Province as a calculation example, numbered [1, 4773]. The sampling interval is 10 minutes, and the sampling period is from 19:40 on March 17, 2009 to 23:00 on April 19, 2009. The calculation example simulation is carried out in the Matlab environment. The 3-dimensional and 4-dimensional joint probability density functions were constructed for 2 and 3 wind farms respectively, and compared and analyzed.

[0121] Depend on Figure 1 to Figure 6 It can be seen that in most time periods, the wind power output of the three wind farms 1, 2, and 3 has unstable correlation characteristics, and the positive correlation is strong, that is, the same increase and the same decrease. Among the three wind farms 4, 5, and 6, there is a strong positive correlation between wind farm 4 and wind farm 6, while wind farm 5 only has a strong positive...

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Abstract

The invention discloses a multiple-wind-power-plant correlation modeling method based on adaptive multi-variable nonparametric kernel density estimation, and belongs to the technical field of multidimensional variable correlation research. The method comprises the following steps that: S1: establishing a multi-variable nonparametric kernel density estimation model of a wind power plant; S2: constructing a bandwidth optimization model; and S3: constructing a bandwidth solving method of the multi-variable nonparametric kernel density estimation model of the wind power plant on the basis of ordinal optimization. By use of a method, a modeling process is practical and simple, correlation among a plurality of random variables can be quickly and efficiently modelled. Compared with a traditional parametric estimation method based on a copula function, the method is higher in accuracy and applicability, and a local adaptation problem of a traditional multi-variable nonparametric kernel density estimation method is favorably solved.

Description

technical field [0001] The invention discloses a multi-wind farm output correlation modeling method based on self-adaptive multi-variable non-parametric kernel density estimation, and belongs to the technical field of multi-dimensional variable correlation research. Background technique [0002] With the increasing popularity of wind power energy in my country, large-scale wind power grid integration has become a trend, which brings a lot of uncertainties. Affected by regional and environmental characteristics, the output characteristics of multiple wind farms may have a certain degree of probability correlation. Therefore, in the process of power system operation control, it is necessary to consider the correlation between multiple wind farms and conduct joint probability modeling to improve the operating efficiency, safety and stability of the system. [0003] Because it can be used to characterize the probability correlation characteristics among multiple random variable...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06Q50/06H02J3/38H02J3/00
CPCG06Q50/06G06F30/20H02J3/386H02J3/00H02J2203/20Y02E10/76
Inventor 杨楠叶迪李宏圣黄禹董邦天
Owner CHINA THREE GORGES UNIV
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