Wind speed probability distribution modeling method and system

A technology of probability distribution and probability distribution function, applied in the field of wind farms, can solve problems such as large amount of calculation and complex expressions, and achieve the effect of satisfying data requirements, simplifying calculation and enhancing adaptability

Inactive Publication Date: 2017-02-15
SOUTHWEST PETROLEUM UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, in order to reduce the error in non-parametric kernel density estimation, the value of the general sample size n is selected to be relatively large, resulting in complex expressions and a large amount of calculation.

Method used

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  • Wind speed probability distribution modeling method and system
  • Wind speed probability distribution modeling method and system
  • Wind speed probability distribution modeling method and system

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Experimental program
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Effect test

Embodiment 1

[0035] combined below figure 1 , using the Weibull distribution probability density function as the first parameter probability density function of the wind speed to describe the method for modeling the wind speed probability distribution disclosed in the first embodiment of the present invention in detail.

[0036] Step 101: Obtain the probability density function of the first parameter of the wind speed

[0037] According to the historically measured wind speed samples, the probability density function of Weibull distribution of wind speed can be obtained. Specifically, the two-parameter Weibull distribution can be used to calculate the historical measured wind speed 1 ,v 2 ,…,v n Statistical description. For example, the maximum likelihood estimation method can be used to estimate the shape parameter k and scale parameter c of the Weibull distribution of wind speed, and obtain the probability density function f(v) of the Weibull distribution of wind speed:

[0038]

...

Embodiment 2

[0056] The following is a detailed description of an embodiment using nonparametric kernel density estimation as the probability distribution model of wind speed.

[0057] Suppose the historical measured wind speed sample X 1 ,X 2 ,…,X n is n data samples of random variable X, and the real probability density function of random variable X is f(x), then its nonparametric kernel density estimation function f h (x) is:

[0058]

[0059] Where: h is the bandwidth, n is the sample size, and K( ) is the kernel function. When n→∞, f h (x) converges to f(x).

[0060] Specifically, the kernel function may be a uniform kernel function K(u)=1 / 2-1≤u≤1, a triangular kernel function K(u)=1-|u|-1≤u≤1, a Gaussian kernel function Wait.

[0061] In a preferred embodiment, the present invention selects a Gaussian function as the kernel function, and its expression is as follows:

[0062]

[0063] In practical applications, the choice of bandwidth h determines f h (x) accuracy, an...

Embodiment 3

[0077] The following takes 4 wind farms W 1 , W 2 , W 3 and W 4 For example, the method for modeling the wind speed probability distribution disclosed in the third embodiment of the present invention will be described in detail based on the historical measured wind speed data of one year.

[0078] For each wind farm, the maximum likelihood estimation method is used to estimate the shape parameter k and scale parameter c of the Weibull distribution of wind speed, as shown in Table 1:

[0079] Table 1

[0080] wind farm k c W 1

10.72 2.42 W 2

8.26 2.31 W 3

8.43 2.32 W 4

11.29 2.16

[0081] Then, the Weibull distribution probability density function f(v) of wind speed can be obtained.

[0082] Using historical measured wind speed samples, the Weibull distribution model of wind speed was calculated by χ 2 test, get χ 2 Test statistic χ 2 , and the critical value χ corresponding to 3 degrees of freedom and a significance l...

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Abstract

The invention discloses a wind speed probability distribution modeling method and system. The difference of wind speeds in different areas can be reflected and the adaptability of a wind speed random distribution model can be enhanced. The method comprises the steps of obtaining a first parameter probability density function of a wind speed according to a historical actually measured wind speed sample; setting m wind speed grouping intervals, and performing x<2> checking and K-S checking on the first parameter probability density function of the wind speed by utilizing the historical actually measured wind speed sample; when a statistical quantity x<2> is less than a critical value x<2>m1(1-alpha) and a statistical quantity Dn is less than a critical value D(n, alpha), obtaining a probability distribution function of the wind speed according to the first parameter probability density function of the wind speed to describe the probability distribution model of the wind speed; and when the statistical quantity x<2> is greater than or equal to the critical value x<2>m1(1-alpha) and the statistical quantity Dn is greater than or equal to the critical value D(n, alpha), obtaining the probability distribution function of the wind speed according to a nonparametric kernel density estimation function to describe the probability distribution model of the wind speed.

Description

technical field [0001] The invention relates to the technical field of wind farms, and in particular, to a method and system for modeling wind speed probability distribution. Background technique [0002] Due to the volatility and randomness of wind, the wind speed probability distribution model is generally used to describe the distribution characteristics of wind speed under different meteorological and topographic conditions. The accuracy of the wind speed probability distribution model directly determines the feasibility of the wind farm in the early stage, affects the economy of the wind farm in the operational stage, and the stable operation of the grid-connected wind farm. Therefore, it is of great significance to study the probability distribution model of wind speed under different meteorological and terrain conditions for the rational utilization of wind resources. Affected by geographical factors (such as latitude, topography, landform) and regional climate facto...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
CPCG06F30/20
Inventor 李茜张鹏翔张安安王嘉糯禹海张力丹李维任劲舟袁豪陈豪李炎庆
Owner SOUTHWEST PETROLEUM UNIV
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