CN105354349A - Wind speed modeling method for large-sized wind power plant in mountainous area - Google Patents
Wind speed modeling method for large-sized wind power plant in mountainous area Download PDFInfo
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Abstract
The invention discloses a wind speed modeling method for a large-sized wind power plant in a mountainous area. The method comprises: 1, sampling historically recorded wind speed of the wind power plant, and calculating parameters c and k of wind speed Weibull distribution of each fan group of the wind power plant; 2, calculating a rank correlation coefficient Rho Rr of each fan group in the wind power plant, and constructing a historical wind speed rank correlation coefficient Rr of the wind power plant; 3, calculating related wind speed normal Copula functions of the wind power plant to obtain a joint probability distribution function, defined in the specification, of modeled and predicted wind speed with correlation, and edge probability distribution functions u1 to un of wind speed of fan groups; and 4, performing equiprobable reverse transformation of the Weibull distribution parameters c and k on the edge probability distribution functions of the wind speed of the fan groups to realize wind speed prediction. According to the method, the technical problem that a characteristic that wind speed in a wind power plant has correlation cannot be truly and effectively reflected, electric power distribution and safety operation of a power system are influenced due to direct influence of the correlation of the wind speed in the wind power plant on output of the wind power plant, and the like in the prior art are solved.
Description
Technical field:
The invention belongs to electric system wind energy turbine set modeling field, particularly relate to a kind of mountain area Large Scale Wind Farm Integration wind speed modeling method.
Background technology:
Wind energy is a kind of green clean energy resource, greatly develops wind-powered electricity generation and is conducive to reducing fossil energy consumption, reducing carbon emission level.Carry out wind speed modeling and forecasting wind speed work, obtain wind power prediction result more accurately, can be analysis wind speed characteristics provides strong help and correlation technique to support on problems such as electricity net safety stable impact, wind electricity digestion, wind energy turbine set Site Selections.
Current wind speed modeling and forecasting method can be divided into physically based deformation model and based on historical data these two kinds.The former generally adopts numerical weather forecast data to carry out forecasting wind speed, and the latter utilizes the historical wind speed of wind energy turbine set to carry out outside forecast.The distribution of wind resource has certain regional characteristic, but mostly do not consider the correlativity of same wind field inner diverse location wind power generating set wind speed in present stage wind speed Modeling Research, but in false wind electric field, the wind speed of each blower fan present position is equal at synchronization.This is for mountain area Large Scale Wind Farm Integration, and this hypothesis obviously can not reflect that the wind speed of wind energy turbine set inside has this feature of correlativity authentic and validly.Because the correlativity of the inner wind speed of wind energy turbine set directly affects output of wind electric field, and then affect the distribution of electric system quantity of electricity and safe operation thereof, therefore to carry out wind energy turbine set Site Selection, wind electricity digestion work, containing the security and stability analysis of wind energy turbine set electric system and economic analysis, just must set up the good wind farm wind velocity correlation models of degree of fitting.
The method being commonly used to set up wind speed correlation models at present mainly contains three kinds.One is the wind series based on autoregressive moving-average model and time-shifting techniques method serial correlation Time Created that periodical " Proceedings of the CSEE " proposes in the 29th volume the 4th phase one literary composition for 2009; Two is the Orthogonal Transformation Methods decomposed based on the Cholesky of linearly dependent coefficient matrix, i.e. the wind field group wind speed modeling and forecasting method based on Orthogonal Transformation Method that adopts of periodical " IEEETransonPowerSystems " the 24th volume the 2nd phase in 2009, " Proceedings of the CSEE " the 32nd volume the 7th phase in 2012 and 2012 the 32nd volume the 13rd phase pertinent literature; Three is the Nataf transform methods considering wind speed linear correlation matrix and standardized normal distribution linear variable displacement correlation matrix relation, and the Nataf converter technique that namely periodical " Automation of Electric Systems " the 37th volume the 6th phase in 2013, " IEEETransonPowerSystems " the 26th volume the 2nd phase in 2011 and " Proceedings of the CSEE " the 33rd volume the 16th phase related article in 2013 adopt sets up the relevant Wind speed model of wind farm group each wind energy turbine set interior.
The wind series that the above-mentioned method setting up wind speed correlation models all supposes to have correlativity all meets the distribution of same parameter, such as, suppose all to meet identical Weibull distribution parameters, and all supposes that in same wind energy turbine set, wind speed is equal everywhere.Wind speed for actual wind energy turbine set often presents different distributions, even if modeling thinks that distribution pattern is identical, distribution parameter is also different, thus needs to adopt a kind of good method to set up the relevant Wind speed model in wind energy turbine set inside of different distributions Type and distribution parameter.
Summary of the invention:
The technical problem to be solved in the present invention: a kind of mountain area Large Scale Wind Farm Integration wind speed modeling method is provided, with solve prior art to mountain area Large Scale Wind Farm Integration wind speed modeling and forecasting exist can not be authentic and valid reflect that the wind speed of wind energy turbine set inside has this feature of correlativity.Because the correlativity of the inner wind speed of wind energy turbine set directly affects output of wind electric field, and then affect the technical matterss such as the distribution of electric system quantity of electricity and safe operation thereof.
Technical solution of the present invention:
A kind of mountain area Large Scale Wind Farm Integration wind speed modeling method, it comprises:
Step 1, to wind energy turbine set historical record wind-speed sample, and calculate parameter c and the k of each draught fan group wind speed Weibull distribution of wind energy turbine set;
The rank correlation coefficient of each draught fan group in step 2, calculating wind energy turbine set
and form the historical wind speed rank correlation matrix R of this wind energy turbine set
r;
The relevant wind speed normal state Copula function of step 3, calculating wind energy turbine set, and then obtain the joint probability distribution function of the modeling and forecasting wind speed with correlativity
and the marginal probability distribution function u of each draught fan group wind speed
1..., u
n;
Step 4, the equiprobability inverse transformation obeying Weibull distribution parameters c and k is done to the marginal probability distribution function of draught fan group wind speed, realize forecasting wind speed.
The parameter c of each draught fan group wind speed Weibull distribution of the calculating wind energy turbine set described in step 1 and the computing formula of k are:
In formula, v is wind speed, c and k is respectively scale parameter and form parameter.
The rank correlation coefficient of each draught fan group in calculating wind energy turbine set described in step 2
computing formula be:
In formula:
be respectively order X
r, Y
raverage,
for the Spearman rank correlation coefficient of random vector X, Y.
The computing formula of the relevant wind speed normal state Copula function of the calculating wind energy turbine set described in step 3 is:
In formula: R
rfor the correlation matrix of n unit normally distributed variable;
for correlation matrix is R
rn unit standardized normal distribution distribution function;
i=1,2 ..., n is the inverse function of Standard Normal Distribution.
The equiprobability inverse transformation formula that the marginal probability distribution function to draught fan group wind speed described in step 4 makes to obey Weibull distribution parameters c and k is:
In formula: u
ifor vectorial U
iin element,
for marginal probability distribution function.
Beneficial effect of the present invention:
The present invention is directed to mountain area Large Scale Wind Farm Integration blower fan quantity many, have a very wide distribution, the blower fan wind speed being distributed in each hilltop often has certain correlativity feature, consider that mountain area Large Scale Wind Farm Integration inner blower is distributed in different positions, the blower fan wind speed of each position is different, blower fan is turned to several group of planes, adopt the correlativity Wind speed model setting up each group of planes based on Multivariate Normal Copula function and rank correlation theory, adopt Spearman rank correlation matrix can describe the correlation properties of wind speed between the inner unit of wind energy turbine set preferably, compare to press close to the probabilistic statistical characteristics of historical wind speed by the wind speed of Multivariate Normal Copula function modelling prediction, embody high accuracy of the present invention.Can be widely applied in the wind speed Modeling Calculation of mountain area Large Scale Wind Farm Integration, to improve the precision of this type of forecasting wind speed further, thus provide certain technical support for wind energy turbine set Site Selection, electricity net safety stable and economical operation; The invention solves prior art to mountain area Large Scale Wind Farm Integration wind speed modeling and forecasting wind speed exist can not be authentic and valid reflect that the wind speed of wind energy turbine set inside has this feature of correlativity, because the correlativity of the inner wind speed of wind energy turbine set directly affects output of wind electric field, and then affect the technical matterss such as the distribution of electric system quantity of electricity and safe operation thereof.
Accompanying drawing illustrates:
Fig. 1 is Multivariate Normal Copula functional transformation process schematic of the present invention;
Fig. 2 is embodiment of the present invention blower fan power producing characteristics curve synoptic diagram;
Fig. 3 is the rank correlation matrix schematic diagram of historical record wind speed between the embodiment of the present invention 8 draught fan group;
Fig. 4 is the relevant wind speed rank correlation matrix of embodiment of the present invention modeling and forecasting;
Fig. 5 is the cumulative probability distribution curve schematic diagram of embodiment of the present invention modeling and forecasting wind speed and historical record wind speed;
Fig. 6 is embodiment of the present invention probability density curve schematic diagram.
Embodiment:
A kind of mountain area Large Scale Wind Farm Integration wind speed modeling method, it comprises:
Step 1, to wind energy turbine set historical record wind-speed sample, and calculate parameter c and the k of each draught fan group wind speed Weibull distribution of wind energy turbine set;
The rank correlation coefficient of each draught fan group in step 2, calculating wind energy turbine set
and form the historical wind speed rank correlation matrix R of this wind energy turbine set
r;
The relevant wind speed normal state Copula function of step 3, calculating wind energy turbine set, and then obtain the joint probability distribution function of the modeling and forecasting wind speed with correlativity
and the marginal probability distribution function u of each draught fan group wind speed
1..., u
n;
Step 4, the equiprobability inverse transformation obeying Weibull distribution parameters c and k is done to the marginal probability distribution function of draught fan group wind speed.Finally obtain modeling and forecasting wind speed.
Set up mountain area Large Scale Wind Farm Integration be correlated with Wind speed model time, need the historical record wind speed gathering wind energy turbine set, and historical record wind speed is carried out to the Fitting Calculation of distribution character, obtain the satisfied distribution pattern in this wind-powered electricity generation place and correlation parameter thereof, to obtain the cumulative distribution function of wind speed, this function needs when adopting Multivariate Normal Copula function modelling to use.
In the wind speed modeling of wind energy turbine set, Weibull distribution is usually used for characterizing the distribution of wind speed, as shown in the formula
In formula, v is wind speed, c and k is respectively scale parameter and form parameter, the annual mean wind speed size in the described area of c reflection.The parameter c of each draught fan group wind speed Weibull distribution of the calculating wind energy turbine set therefore described in step 1 and the computing formula of k adopt formula (1) to calculate.
Because the Wind turbines of mountain area Large Scale Wind Farm Integration is often distributed in the different hilltop, thus at the wind speed of time and spatially wind energy turbine set each blower fan inner, there is certain correlativity.The method of tolerance wind speed stochastic variable correlativity has a variety of, and linear correlation degree is one of conventional index, and it is defined as:
In formula,
represent stochastic variable X
1, X
2between linear correlation degree, cov () represents and asks the covariance of stochastic variable, and var () represents and asks variance.
But, linear correlation degree only reflects the linear dependence between stochastic variable, if carry out the identical linear transformation of monotonicity to stochastic variable, then its linear correlation degree is constant, but if dull nonlinear transformation were carried out to it, then its linear correlation degree will change.The deviation occurred to measure correlativity in order to avoid adopting linear correlation when wind speed of being correlated with to wind field inside makes nonlinear transformation, the correlativity that the present invention will adopt the method for Spearman rank correlation coefficient to calculate the inner wind speed of wind energy turbine set.
The Spearman rank correlation coefficient of random vector X, Y
be defined as follows, be provided with stochastic variable (x
j, y
j| j=1,2 ..., be m) random vector X, the element in Y, sorts to the element in X, Y, obtains element (x in random vector
j, y
j| j=1,2 ..., order m)
then:
In formula
be respectively order X
r, Y
raverage.If carry out the identical conversion of monotonicity to X, Y, the Spearman rank correlation coefficient of X and Y
remain unchanged, therefore the present invention calculates the rank correlation coefficient of each draught fan group in wind energy turbine set
formula (3) is adopted to calculate,
The present invention will adopt this argument of case verification, proof procedure detailed in Example 1.
For wind power generating set, wind speed is input variable, meritorious the exerting oneself of blower fan is output variable, is converted to the process of process that blower fan exerts oneself a normally nonlinear transformation, engineering usually adopts piecewise nonlinear function carry out the meritorious of matching blower fan and exert oneself by wind speed:
In formula, v
wi, v
wofor incision, the cut-out wind speed of blower fan, v
rwind rating, P
rit is the nominal output of blower fan; M is wind speed-power coefficient.
Visible, if adopt traditional linear correlation matrix to measure the correlativity of wind speed, the degree of correlation of exerting oneself causing blower fan changes, thus the wind speed correlativity of wind energy turbine set inside, mountain area can not be reflected objectively, therefore the present invention adopts Spearman rank correlation coefficient to measure the wind speed correlativity of Large Scale Wind Farm Integration inside, mountain area.
Copula function defines
It is a marginal distribution function and a Copula function that a n is tieed up joint distribution function decomposition n by nineteen fifty-nine Sklar.Copula function can be used for describing the correlativity of variable, and it is random vector X
1, X
2..., X
njoint distribution function F (x
1, x
2..., x
n) with respective marginal distribution function
link together, i.e. function C (u
1, u
2..., u
n), make:
Suppose random vector U
1, U
2..., U
nobey being uniformly distributed between [0,1], then random vector X
1, X
2..., X
nmarginal distribution function
can be designated as:
Wherein, u
ifor vectorial U
iin element.Edge probability distribution function (cumulative distribution function)
do equiprobability inverse transformation:
Then formula (5) can be written as:
Multivariate Normal Copula function
N unit normal state Copula distribution function is:
Wherein, R
rfor the correlation matrix (diagonal element is the symmetric positive definite matrix of 1 entirely) of n unit normally distributed variable;
for correlation matrix is R
rn unit standardized normal distribution distribution function;
i=1,2 ..., n is the inverse function of Standard Normal Distribution.
Three sample space transformation relations involved in the present invention as shown in Figure 1, V in figure
srepresent wind speed sample, W
-1the inverse function that () is Weibull Function, N is the stochastic variable of polynary standardized normal distribution.
The present invention adopts formula (9) to calculate the relevant wind speed normal state Copula function of wind energy turbine set.
Formula (7) is adopted to do to obey the equiprobability inverse transformation of Weibull distribution parameters c and k to the marginal probability distribution function of draught fan group wind speed.
Understand technical solution of the present invention more clearly for the ease of those skilled in the art, below in conjunction with embodiment, the present invention is further described.
Embodiment one: checking Spearman rank correlation does not change the correlativity of variable
Suppose there is wind speed random series v
s1, v
s2, sampling time interval is 1min, sampling T.T. 10min; Blower fan power producing characteristics curve is shown in Fig. 2, adopts piecewise fitting function formula (4), and getting incision wind speed is 3m/s, and cut-out wind speed is 25m/s, and wind rating is 14m/s, and wind speed-power coefficient is 3; Wind series is brought into blower fan power producing characteristics fitting function to obtain blower fan and to exert oneself P
w1, P
w2, this process is actual is done dull nonlinear transformation to wind speed random series.
Table 1 lists wind series v
s1, v
s2and order R1, R2, blower fan is exerted oneself P
w1, P
w2and order r1, r2, in his-and-hers watches 1, wind series tries to achieve linear correlation degree is 0.8726, and blower fan linear correlation degree of exerting oneself is 0.8598; Trying to achieve rank correlation degree to wind series is 0.8182, and blower fan rank correlation degree of exerting oneself also is 0.8182, and visible, the rank correlation degree that wind speed and blower fan are exerted oneself is equal.When demonstrating employing linear correlation matrix measures wind speed correlativity thus, make dull nonlinear transformation to wind speed, correlativity changes, and when adopting Spearman rank correlation matrix to measure, does not change the correlativity of wind speed.
The order that table 1 wind speed and blower fan are exerted oneself
Embodiment two: adopt polynary Copula function to set up leek level ground, Guizhou wind energy turbine set and to be correlated with Wind speed model
Leek level ground, Guizhou Province wind energy turbine set is a mountain area Large Scale Wind Farm Integration in Guizhou Province, and this wind energy turbine set distance county town, Hezhang is about 45km, is about 55km, is about 150km apart from city, Bijie Prefecture apart from Lupanshui City city.Wind energy turbine set site district is the irregular strip of the southeast-north-westward, and thing is about 16km, and the wide about 11km in north and south, area is about 40km
2.The present invention, according to the wind turbine historical wind speed measured data of monthly typical case's day between leek level ground wind energy turbine set in July ,-2013 in August, 2012, adopts Multivariate Normal Copula function to set up the relevant Wind speed model of this wind energy turbine set.This wind energy turbine set is totally 56 Fans, single-machine capacity 1.5MW, total installed capacity 84MW, and this modeling according to this wind electric field blower numbering 8 groups, often will be organized draught fan group and comprise 7 Fans.
As Fig. 3 shows the rank correlation matrix figure of historical record wind speed between leek level ground wind energy turbine set 8 draught fan group, diagonal line subgraph shows the Weibull distribution that each draught fan group wind speed obeys, and (diagonal element one of rank correlation matrix is decided to be 1, the wind speed and the rank correlation degree of self that represent each draught fan group are 1), off-diagonal element represents the wind speed rank correlation degree between every two group of planes, such as the first row secondary series represents that draught fan group 1 is 0.81 with the rank correlation of draught fan group 2, visible, the wind speed of the inner each draught fan group of wind field has certain correlativity.
When adopting Multivariate Normal Copula function to set up leek level ground wind energy turbine set correlation model, getting wind speed modeling sample is 10000, obtain the relevant wind speed rank correlation matrix of institute's modeling and forecasting as shown in Figure 4, comparison diagram 3 can be found out, the relevant Wind speed model of institute's modeling is substantially equal with the rank correlation matrix of this wind energy turbine set historical record wind speed.
Set up to check Multivariate Normal Copula function the validity that mountain area Large Scale Wind Farm Integration faciation closes Wind speed model, the present invention compared for accumulated probability distribution curve and the probability density curve of wind energy turbine set historical record wind speed and modeling and forecasting wind speed.
Fig. 5 shows the accumulated probability distribution curve of modeling and forecasting wind speed and historical record wind speed, and Fig. 6 is probability density curve.Obviously, the cumulative probability distribution of historical record wind speed overlaps substantially with the cumulative probability distribution curve of prediction modeling wind speed, shows the high accuracy of adopted Multivariate Normal Copula function modelling.In wind speed probability density curve, historical record wind speed profile comparatively prediction of wind speed probability curve slightly " precipitous ", being actually what get due to historical wind speed is typical day wind speed of every month, relatively " concentrates " so wind speed profile can seem.Comprehensive Correlation historical record wind speed and modeling and forecasting wind speed rank correlation matrix and probability statistics curve, can find out that the method that invention adopts has higher fitting and prediction precision.
Modeling analysis result shows, Spearman rank correlation matrix can describe the correlation properties of wind speed between the inner unit of wind energy turbine set preferably, compare to press close to the probabilistic statistical characteristics of historical wind speed by the wind speed of Multivariate Normal Copula function modelling prediction, embody the high accuracy of the method.And then this method can be considered to promote the use of in the wind speed Modeling Calculation of the regional mountain area such as Guizhou Province, Yunnan Province Large Scale Wind Farm Integration, to improve the precision of this type of forecasting wind speed further, thus provide certain technical support for wind energy turbine set Site Selection, electricity net safety stable and economical operation.
Claims (5)
1. a mountain area Large Scale Wind Farm Integration wind speed modeling method, it comprises:
Step 1, to wind energy turbine set historical record wind-speed sample, and calculate parameter c and the k of each draught fan group wind speed Weibull distribution of wind energy turbine set;
The rank correlation coefficient of each draught fan group in step 2, calculating wind energy turbine set
and form the historical wind speed rank correlation matrix R of this wind energy turbine set
r;
The relevant wind speed normal state Copula function of step 3, calculating wind energy turbine set, and then obtain the joint probability distribution function of the modeling and forecasting wind speed with correlativity
and the marginal probability distribution function u of each draught fan group wind speed
1..., u
n;
Step 4, the equiprobability inverse transformation obeying Weibull distribution parameters c and k is done to the marginal probability distribution function of draught fan group wind speed, realize forecasting wind speed.
2. a kind of mountain area according to claim 1 Large Scale Wind Farm Integration wind speed modeling method, is characterized in that: the parameter c of each draught fan group wind speed Weibull distribution of the calculating wind energy turbine set described in step 1 and the computing formula of k are:
In formula, v is wind speed, c and k is respectively scale parameter and form parameter.
3. a kind of mountain area according to claim 1 Large Scale Wind Farm Integration wind speed modeling method, is characterized in that: the rank correlation coefficient of each draught fan group in the calculating wind energy turbine set described in step 2
computing formula be:
In formula:
be respectively order X
r, Y
raverage,
for the Spearman rank correlation coefficient of random vector X, Y.
4. a kind of mountain area according to claim 1 Large Scale Wind Farm Integration wind speed modeling method, is characterized in that: the computing formula of the relevant wind speed normal state Copula function of the calculating wind energy turbine set described in step 3 is:
In formula: R
rfor the correlation matrix of n unit normally distributed variable;
for correlation matrix is R
rn unit standardized normal distribution distribution function;
i=1,2 ..., n is the inverse function of Standard Normal Distribution.
5. a kind of mountain area according to claim 1 Large Scale Wind Farm Integration wind speed modeling method, is characterized in that: the equiprobability inverse transformation formula that the marginal probability distribution function to draught fan group wind speed described in step 4 makes to obey Weibull distribution parameters c and k is:
In formula: u
ifor vectorial U
iin element,
for marginal probability distribution function.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106067073A (en) * | 2016-05-30 | 2016-11-02 | 都城绿色能源有限公司 | A kind of wind power forecasting method based on wind-resources correlation analysis |
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103020423A (en) * | 2012-11-21 | 2013-04-03 | 华中科技大学 | Copula-function-based method for acquiring relevant characteristic of wind power plant capacity |
| CN104331572A (en) * | 2014-11-17 | 2015-02-04 | 南京工程学院 | Wind power plant reliability modeling method considering correlation between air speed and fault of wind turbine generator |
| CN104834793A (en) * | 2015-05-26 | 2015-08-12 | 河海大学 | Simulation generation method for wind speed data of multiple wind power farms |
-
2015
- 2015-09-17 CN CN201510598636.5A patent/CN105354349A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103020423A (en) * | 2012-11-21 | 2013-04-03 | 华中科技大学 | Copula-function-based method for acquiring relevant characteristic of wind power plant capacity |
| CN104331572A (en) * | 2014-11-17 | 2015-02-04 | 南京工程学院 | Wind power plant reliability modeling method considering correlation between air speed and fault of wind turbine generator |
| CN104834793A (en) * | 2015-05-26 | 2015-08-12 | 河海大学 | Simulation generation method for wind speed data of multiple wind power farms |
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| CN108805331A (en) * | 2018-05-04 | 2018-11-13 | 北京国电通网络技术有限公司 | A kind of electricity demand forecasting method |
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| CN111401596A (en) * | 2019-01-03 | 2020-07-10 | 新疆金风科技股份有限公司 | Wind speed index generation method and device |
| CN111159900A (en) * | 2019-12-30 | 2020-05-15 | 国核电力规划设计研究院有限公司 | Method and device for predicting wind speed of fan |
| CN111159900B (en) * | 2019-12-30 | 2024-02-09 | 国核电力规划设计研究院有限公司 | Method and device for predicting wind speed of fan |
| CN113312739A (en) * | 2020-02-27 | 2021-08-27 | 南京理工大学 | Correlation wind speed generation method based on mixed semi-cloud model |
| CN113312739B (en) * | 2020-02-27 | 2023-03-31 | 南京理工大学 | Correlation wind speed generation method based on mixed semi-cloud model |
| CN112217199A (en) * | 2020-09-14 | 2021-01-12 | 广西大学 | Wind power plant wind speed probability distribution fitting method |
| CN112217199B (en) * | 2020-09-14 | 2023-10-20 | 广西大学 | Wind power plant wind speed probability distribution fitting method |
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