CN113312836A - Short-term wind speed prediction method - Google Patents

Short-term wind speed prediction method Download PDF

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CN113312836A
CN113312836A CN202110506041.8A CN202110506041A CN113312836A CN 113312836 A CN113312836 A CN 113312836A CN 202110506041 A CN202110506041 A CN 202110506041A CN 113312836 A CN113312836 A CN 113312836A
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郑亚锋
高宇峰
但伟
屠学伟
王春雨
魏振华
桑士杰
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State Nuclear Electric Power Planning Design and Research Institute Co Ltd
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Abstract

The invention discloses a short-term wind speed prediction method, which comprises the following steps: carrying out scene clustering on the wind turbine generator by using a K-medoids clustering algorithm to obtain a plurality of equivalent fans; constructing an LSTM model, and training a plurality of equivalent fans through the LSTM model to obtain an initial predicted value of the wind speed; establishing a functional relation among a plurality of equivalent fans by using a hybrid Copula function, and solving parameters in the hybrid Copula function by using a genetic algorithm; solving a residual value of the wind speed through the wind speed edge distribution values of the equivalent fans and a mixed Copula function; obtaining an accurate predicted value of the wind speed according to the initial predicted value and the residual value of the wind speed; the fans in the wind power plant are classified by using a K-medoids clustering algorithm to obtain equivalent fans, the randomness of the wind speed is reduced, the equivalent fans are modeled by using a mixed Copula function and then combined with a long-term network and a short-term network, and the precision of wind speed prediction is improved.

Description

Short-term wind speed prediction method
Technical Field
The invention belongs to the technical field of wind power plant power generation, and particularly relates to a short-term wind speed prediction method.
Background
The wind energy resource storage is huge, and no pollution is caused to the environment, so that the wind power generation is greatly advocated. Accurate wind speed prediction is very important to the scheduling and planning of a power system and is a very critical link in wind power generation.
The traditional wind speed prediction mostly adopts one representative wind motor data to predict the whole wind power plant, and at present, a method combining machine learning is mostly adopted. Due to the influence of the position of the fan and the wake flow, the difference of the operation data of each wind turbine in the wind power plant is large, and the accuracy is not high because only the group data of one wind turbine is used for prediction.
Disclosure of Invention
In order to solve the problems, the invention provides a short-term wind speed prediction method which can obtain a relatively accurate wind speed prediction value.
A short term wind speed prediction method comprising the steps of:
carrying out scene clustering on the wind turbine generator by using a K-medoids clustering algorithm to obtain a plurality of equivalent fans;
constructing an LSTM model, and training a plurality of equivalent fans through the LSTM model to obtain an initial predicted value of the wind speed;
establishing a functional relation among a plurality of equivalent fans by using a hybrid Copula function, and solving parameters in the hybrid Copula function by using a genetic algorithm;
solving a residual value of the wind speed through the wind speed edge distribution values of the equivalent fans and a mixed Copula function;
and obtaining an accurate predicted value of the wind speed through the initial predicted value and the residual value of the wind speed.
Further, carrying out scene clustering on the wind turbine generator by using a K-medoids clustering algorithm to obtain a plurality of equivalent fans, which specifically comprises the following steps:
acquiring historical wind speed data of each wind turbine generator from a wind power plant;
acquiring historical data of wind speed, power, generator rotating speed and blade angle of a region from a meteorological station, analyzing and processing the data, and determining model input and output variables;
carrying out scene clustering on the wind turbine generator by adopting K-medoids clustering, respectively taking the wind speed and the power of the wind turbine generator as horizontal and vertical coordinate values, constructing a rectangular coordinate system, and establishing a criterion function Fs, wherein the method specifically comprises the following steps:
Figure BDA0003058427910000021
in the formula, j represents the number of the fan variables in each class, n represents the total number of the fan variables in each class, and xi、yiAs a coordinate of the center point, xj、yjCoordinates in class except the central point;
solving a criterion function Fs, and determining k wind turbine generator clusters;
and determining each type of wind turbine generator as an equivalent fan.
Further, solving a criterion function Fs to determine k wind turbine clusters, specifically as follows:
s11, randomly selecting k points from the n samples as central points medoids;
s12, distributing the rest points to the current best cluster according to the principle of being nearest to the center;
s13, sequentially calculating the value of the criterion function Fs when the points are medoids for all other points except the medoids in each class, traversing all possibilities, and selecting the point corresponding to the minimum criterion function Fs as a new medoid;
s14, repeating S12 and S13 until all medoids are not changed;
and S15, finally determining k wind turbine generator clusters through continuous iterative operation.
Further, the method for determining the equivalent fan specifically comprises the following steps:
the power of the equivalent fan is the sum of the powers of the clustered wind turbine generators;
the wind speed of the equivalent fan is the average value of the wind speeds of the clustered wind generation sets;
the generator rotating speed of the equivalent fan is the average value of the generator rotating speeds of the clustered wind turbine generators;
and the blade angle of the equivalent fan is the average value of the blade angles of the clustered wind turbine generators.
Further, an LSTM model is constructed, a plurality of equivalent fans are trained through the LSTM model, and an initial predicted value of the wind speed is obtained, wherein the initial predicted value is as follows:
and (3) the calculated wind speed, power, generator rotating speed and blade angle of the equivalent fan are arranged into a new data set, the new data set is divided into a training set and a testing set, and the training set is input into an LSTM model for training to obtain a model parameter and a preliminary predicted value of the wind speed.
Further, the data ratio of the training set to the test set was 8: 2.
Further, a function relation among a plurality of equivalent fans is established by using a hybrid Copula function, and parameters in the hybrid Copula function are solved by using a genetic algorithm, which is specifically as follows:
let x be1,x2,…xnN random wind speed variables, the edge distribution of the n random wind speed variables is respectively f1(x1),f2(x2)，…,fn(xn) The combined distribution is f (x)1,x2,…xn) Let there be a function C, such that:
f(x1,x2，…xn)＝C(f1(x1),f2(x2),…,fn(xn)) (2)
let u1＝f1(x1),u2＝f2(x2),…,un＝fn(xn) Wherein u is1,u2…unFor the edge distribution of wind speed, a mathematical expression of the Copula function is obtained: c (u)1,u2,…un)；
Establishing a mixed Copula function, wherein the concrete form C is as follows:
C(u,v；θ)＝ω1Cg(u,v；θ1)+ω2Cc(u，v；θ2)+ω3Cf(u,v；θ3) (3)
ω1+ω2+ω3＝1，ω1,ω2,ω3≥0 (4)
wherein u is the wind speed edge distribution value of the first equivalent fan, v is the wind speed edge distribution value of the second equivalent fan, omega1,ω2,ω3As a weight parameter of the model, θ1，θ2，θ3Is a dependent parameter of the model;
solving parameter omega of mixed Copula in the formula (3) by adopting genetic algorithm1、ω2、ω3、θ1、θ2、θ3。
Further, solving the parameter omega of the mixed Copula in the formula (3) by adopting a genetic algorithm1、ω2、ω3、θ1、θ2、θ3The method comprises the following steps:
s21, calculating edge distribution values u, v of the wind speeds of the first equivalent fan and the second equivalent fan by a Gaussian kernel density estimation method;
s22, determining a penalty factor gamma of a fitness function of a Genetic Algorithm (GA), and establishing a fitness function f by combining constraint conditions in a formula (4)sThe method comprises the following steps:
Figure BDA0003058427910000031
wherein, Ce(u, v) is an empirical Copula function, ui,viThe ith value represents the edge distribution of the first and second equivalent fans, i represents the number of the edge distribution of the fan, Ce(u, v) are specifically as follows:
Ce(u,v)＝P{U<u,V<v} (6)
in the formula, U and V are edge distribution of historical wind speed;
s23, determining the maximum number G of genetic iterationsmAnd a crossing rate pcThe maximum number of genetic iterations represents the number of iterations, the crossover rate pcIs the probability of individual variation in the population, GmSet to 200-500, pcSet to 0.9-0.97;
s24, solving omega by combining the formulas (4), (5) and (6)1、ω2、ω3、θ1、θ2、θ3。
Further, the residual value of the wind speed is obtained through the wind speed edge distribution values of the equivalent fans and the mixed Copula function, and the method specifically comprises the following steps:
s31, dividing a data set of the first equivalent fan into a training set and a testing set, inputting the training set into an LSTM model for training to obtain an LSTM wind speed prediction model, solving a preliminary prediction value of the test set LSTM model wind speed, and setting the total number of samples of the test set as n;
s32, solving the edge distribution value v of the second equivalent fan at the corresponding moment, wherein the number of the edge distribution value v is the same as that of the first equivalent fan test sets;
s33, subtracting the actual value of the first equivalent fan at the corresponding moment from the predicted value of the LSTM model wind speed to obtain a residual sequence, recording the residual sequence as y, and obtaining an edge distribution function w of the residual sequence, wherein the method specifically comprises the following steps:
w＝G(y)
s34, establishing a mixed Copula function of the wind speed of the equivalent fan 2 and the residual value of the equivalent fan 1;
s35, when the wind speed value of the second equivalent fan at the moment of t-1 is known, obtaining the corresponding edge distribution value v of the second equivalent fant-1V is to bet-1Substituting into the mixed Copula function expression C as follows:
C＝C(vt-1，w) (7)
s36, pairing the numbers obtained above
Figure BDA0003058427910000041
Fitting to obtain C (v)t-1W) and w are approximated by the following equation:
C＝a1wm+a2wm-1+…amw+am+1 (8)
in the formula, a1,a2,…am+1Is a polynomial coefficient, and m is the order of w;
s37, simultaneous solution of formula (7) and formula (8), which can be solved to obtain w*The variable y is at time t-1
Figure BDA0003058427910000042
The predicted value is:
Figure BDA0003058427910000043
in the formula, G-1Is the inverse of the cumulative distribution function of the residual error w;
s38, calculating the residual value of the first equivalent fan at the time t
Figure BDA0003058427910000044
Using residual error at time t-1
Figure BDA0003058427910000045
Approximately replacing the residual at time t
Figure BDA0003058427910000046
Obtaining a residual error value of the first equivalent fan at the time t
Figure BDA0003058427910000047
The method comprises the following specific steps:
Figure BDA0003058427910000048
in the formula (I), the compound is shown in the specification,
Figure BDA0003058427910000049
is the predicted value of the variable y at the time t.
Further, an accurate predicted value of the wind speed is obtained through the preliminary predicted value and the residual value of the wind speed, and the method specifically comprises the following steps:
inputting indexes such as wind speed, power, generator rotating speed and blade angle by using the LSTM wind speed prediction model to obtain a preliminary predicted value f of the wind speed at the time tt；
Obtaining a wind speed predicted value with higher precision, which is specifically as follows:
Figure BDA0003058427910000051
in the formula (f)tIs the preliminary predicted value of the wind speed of the LSTM wind speed prediction model at the time t,
Figure BDA0003058427910000052
is the wind speed residual prediction value at the time t,
Figure BDA0003058427910000053
the predicted value of the wind speed at the time t is high in precision.
The invention has the beneficial effects that: the fans in the wind power plant are classified by using a K-medoids clustering algorithm to obtain equivalent fans, the randomness of the wind speed is reduced, the equivalent fans are modeled by using a mixed Copula function and then combined with a long-term network and a short-term network, and the precision of wind speed prediction is improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 shows a flow diagram of a short term wind speed prediction method according to an embodiment of the invention;
fig. 2 is a schematic flow chart illustrating a criterion function Fs for determining k wind turbine clusters according to the short-term wind speed prediction method in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a flow chart illustrating a short-term wind speed prediction method according to an embodiment of the invention, the short-term wind speed prediction method includes the following steps:
carrying out scene clustering on the wind turbine generator by using a K-medoids clustering algorithm to obtain a plurality of equivalent fans;
constructing an LSTM (Long Short-Term Memory, Long and Short Term Memory network) model, and training a plurality of equivalent fans through the LSTM model to obtain a preliminary predicted value of the wind speed;
establishing a functional relation among a plurality of equivalent fans by using a hybrid Copula function, and solving parameters in the hybrid Copula function by using a Genetic Algorithm (GA);
solving a residual value of the wind speed by utilizing the wind speed edge distribution value and a mixed Copula function;
and summing the preliminary predicted value and the residual value of the wind speed to obtain an accurate predicted value of the wind speed.
The fans in the wind power plant are classified by using a K-medoids clustering algorithm, then the classes are regarded as equivalent fans, modeling is carried out on the equivalent fans by using a hybrid Copula, and then the equivalent fans are combined with a long-term and short-term memory network to obtain a more accurate wind speed predicted value.
Long-short term memory is a special recurrent neural network, in which a concept called "gate" is introduced, which can play a role in regulating information. Through the construction of individual gates, useful information of previous sequences can be well "remembered". The LSTM model can be easily constructed using the Keras (Keras is an open source artificial neural network library written by Python) framework.
Carrying out scene clustering on the wind turbine generator by using a K-medoids clustering algorithm to obtain a plurality of equivalent fans, which comprises the following steps:
and acquiring historical wind speed data of each wind turbine generator from the wind power plant.
For a wind power plant, indexes such as wind speed, wind power and the like have certain regularity. Since the wind speed is due to the air flow caused by the change of solar thermal radiation, the wind speed has an integral characteristic that the wind speed at a future time is related to the wind speed at the present time and the historical wind speed. Therefore, according to historical relevant data, the rule is learned by using a deep learning or machine learning method such as LSTM, and the like, and a wind speed prediction model can be obtained.
Historical wind speed, power, generator speed and blade angle data of the region are obtained from a meteorological station, and are analyzed and processed to reduce random noise generated in wind speed measurement and signal acquisition and determine model input and output variables.
Carrying out scene clustering on the wind turbine generator by adopting K-medoids clustering, taking the wind speed and the power of a fan as horizontal and vertical coordinate values respectively, constructing a rectangular coordinate system, and establishing a criterion function Fs, wherein the criterion function Fs is the Euclidean distance from all other points in the current class to the medoids of the central point, and the expression of the ith class (i is more than or equal to 1 and less than or equal to K) is as follows:
Figure BDA0003058427910000071
in the formula, j represents the number of the fan variables in each class, n represents the total number of the fan variables in each class, and xi、yiAs a coordinate of the center point, xj、yjCoordinates in the class other than the center point.
Referring to fig. 2, fig. 2 shows a flow diagram of determining k wind turbine clusters according to a criterion solving function Fs of a short-term wind speed prediction method according to an embodiment of the present invention, where the criterion solving function Fs determines the k wind turbine clusters, and the specific steps are as follows:
and S11, randomly selecting k points from the n samples as central points medoids.
And S12, distributing the rest points to the current best cluster according to the principle of being closest to the center.
S13, sequentially calculating the value of the criterion function Fs when the points are medoids for all other points except the medoids in each class, traversing all the possibilities, and selecting the point corresponding to the minimum criterion function Fs as a new medoid.
S14, repeating S2 and S3 until all medoids are not changing.
And S15, finally determining k wind turbine generator clusters through continuous iterative operation.
Determining each type of wind turbine generator as an equivalent fan, wherein the method for determining each type of wind turbine generator as the equivalent fan specifically comprises the following steps:
the power of the equivalent fan is the sum of the powers of the clustered wind turbine generators;
the wind speed of the equivalent fan is the average value of the wind speeds of the clustered wind generation sets;
the generator rotating speed of the equivalent fan is the average value of the generator rotating speeds of the clustered wind turbine generators;
and the blade angle of the equivalent fan is the average value of the blade angles of the clustered wind turbine generators.
Constructing an LSTM model, training a plurality of equivalent fans through the LSTM model, and obtaining an initial predicted value of the wind speed, wherein the method specifically comprises the following steps:
and (3) the calculated wind speed, power, generator rotating speed and blade angle of the equivalent fan are arranged into a new data set, the new data set is divided into a training set and a testing set, and the training set is input into an LSTM model for training to obtain a model parameter and a preliminary predicted value of the wind speed.
For example, the training set and test set have a data ratio of 8: 2.
Establishing a functional relation among a plurality of equivalent fans by using a hybrid Copula function, and solving parameters in the hybrid Copula function by using a Genetic Algorithm (GA), wherein the parameters are as follows:
for the classified scenes, according to the sklar theorem, the joint distribution of N random variables can be decomposed into N univariate distributions and a Copula function.
Let x be1,x2,…xnN random wind speed variables, the edge distribution of the n random wind speed variables is respectively f1(x1),f2(x2),…,fn(xn) The combined distribution is f (x)1,x2,…xn) Let there be a function C, such that:
f(x1,x2,…xn)＝C(f1(x1)，f2(x2)，…，fn(xn)) (2)
let u1＝f1(x1),u2＝f2(x2),…,un＝fn(xn) Wherein u is1,u2…unObtaining a mathematical table of Copula function for the edge distribution of wind speedThe expression is as follows: c (u)1,u2,…un)。
The Copula functions with different characteristics are adopted to form a mixed Copula to establish a dependent structure among the wind speeds, so that the prediction accuracy can be improved. Gumbel, Clayton and Frank are combined according to a certain proportion, wherein Cg、CcAnd CfAbbreviations for Gumbel, Clayton, Frank Copula functions, respectively. The model integrates the advantages of each single Copula and can better reflect data characteristics.
Taking two-dimensional Copula as an example, a hybrid Copula function is established, and the specific form C is as follows:
C(u,v；θ)＝ω1Cg(u,v；θ1)+ω2Cc(u,v；θ2)+ω3Cf(u,v；θ3) (3)
ω1+ω2+ω3＝1，ω1,ω2,ω3≥0 (4)
wherein u is the wind speed edge distribution value of the first equivalent fan, v is the wind speed edge distribution value of the second equivalent fan, omega1,ω2,ω3As a weight parameter of the model, θ1，θ2，θ3Are dependent parameters of the model.
From the above equation (3), it can be known that the mixed Copula has six parameters, and compared with a single Copula, the mixed Copula can describe the correlation degree between variables more easily, and the parameters thereof more specifically show the dependency relationship between the variables, and a Genetic Algorithm (GA) is used to solve the parameter ω of the mixed Copula in the above equation (3)1、ω2、ω3、θ1、θ2、θ3The method comprises the following specific steps:
s21, edge distribution values u and v of the wind speeds of the first equivalent fan and the second equivalent fan are obtained by a Gaussian kernel density estimation method, wherein u and v represent the probability that the wind speeds of the first equivalent fan and the second equivalent fan are smaller than a specific value.
S22, determining a penalty factor gamma of a fitness function of a Genetic Algorithm (GA), and combining constraint conditions in the formula (4) to establishVertical fitness function fsThe method comprises the following steps:
Figure BDA0003058427910000091
wherein, Ce(u, v) is an empirical Copula function, ui,viAnd the ith value represents the edge distribution of the first and second equivalent fans, and i represents the number of the edge distribution of the fan.
Taking a two-dimensional Copula function as an example, Ce(u, v) the specific formula is as follows:
Ce(u,v)＝P{U<u,V<v} (6)
in the formula, U and V are edge distributions of the historical wind speed.
S23, determining the maximum number G of genetic iterationsmAnd a crossing rate pcThe maximum number of genetic iterations represents the number of iterations, the crossover rate pcIs the probability of individual variation in the population, GmSet to 200-500, pcSet to 0.9-0.97.
S24, solving omega by combining the formulas (4), (5) and (6)1，ω2，ω3And theta1，θ2，θ3。
And solving a residual value of the wind speed through the wind speed edge distribution values of the equivalent fans and a mixed Copula function, wherein the residual value is as follows:
and S31, dividing the obtained data set of the first equivalent fan into a training set and a testing set according to the ratio of 8:2, inputting the training set into an LSTM model for training to obtain an LSTM wind speed prediction model, obtaining a preliminary predicted value of the wind speed of the LSTM model in the testing set, and setting the total number of samples in the testing set to be n.
And S32, obtaining the edge distribution value v of the second equivalent fan at the corresponding moment, wherein the number of the edge distribution value v is the same as that of the first equivalent fan test sets.
S33, subtracting the actual value of the first equivalent fan at the corresponding moment from the predicted value of the LSTM model wind speed to obtain a residual sequence, recording the residual sequence as y, and obtaining an edge distribution function w of the residual sequence, wherein the method specifically comprises the following steps:
w＝G(y)
and S34, establishing a mixed Copula function of the wind speed of the equivalent fan 2 and the residual value of the equivalent fan 1.
S35, when the wind speed value of the second equivalent fan at the moment of t-1 is known, obtaining the corresponding edge distribution value v of the second equivalent fant-1V is to bet-1Substituting into the mixed Copula function expression C as follows:
C＝C(vt-1，w) (7)
s36, pairing the numbers obtained above
Figure BDA0003058427910000101
(n is the total number of samples) and fitting to obtain C (v)t-1W) and w are approximated by the following equation:
C＝a1wm+a2wm-1+…amw+am+1 (8)
in the formula, a1，a2，…am+1Is a polynomial coefficient, and m is the order of w.
S37, simultaneous solution of formula (7) and formula (8), which can be solved to obtain w*The variable y is at time t-1
Figure BDA0003058427910000102
The predicted value is:
Figure BDA0003058427910000103
in the formula, G-1Is the inverse of the cumulative distribution function of the residuals w.
S38, calculating the residual value of the first equivalent fan at the time t
Figure BDA0003058427910000104
Since the residual error has differential characteristics, the residual error at the time t-1 is used
Figure BDA0003058427910000105
Approximately replacing the residual at time t
Figure BDA0003058427910000106
Obtaining the residual error value of the first equivalent fan at the time t
Figure BDA0003058427910000107
The method comprises the following specific steps:
Figure BDA0003058427910000108
in the formula (I), the compound is shown in the specification,
Figure BDA0003058427910000109
is the predicted value of the variable y at the time t.
Summing the preliminary predicted value and the residual value of the wind speed to obtain an accurate predicted value of the wind speed, which is as follows:
inputting indexes such as wind speed, power, generator rotating speed and blade angle by using the obtained LSTM wind speed prediction model to obtain a preliminary predicted value f of the wind speed at the time tt。
Summing the residual value and the corresponding preliminary predicted value of the wind speed to obtain a wind speed predicted value with higher precision, which is as follows:
Figure BDA0003058427910000111
in the formula (f)tIs the preliminary predicted value of the wind speed of the LSTM wind speed prediction model at the time t,
Figure BDA0003058427910000112
is the wind speed residual prediction value at the time t,
Figure BDA0003058427910000113
the predicted value of the wind speed at the time t is high in precision.
Although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A short-term wind speed prediction method is characterized by comprising the following steps:
carrying out scene clustering on the wind turbine generator by using a K-medoids clustering algorithm to obtain a plurality of equivalent fans;
constructing an LSTM model, and training a plurality of equivalent fans through the LSTM model to obtain an initial predicted value of the wind speed;
establishing a functional relation among a plurality of equivalent fans by using a hybrid Copula function, and solving parameters in the hybrid Copula function by using a genetic algorithm;
solving a residual value of the wind speed through the wind speed edge distribution values of the equivalent fans and a mixed Copula function;
and obtaining an accurate predicted value of the wind speed through the initial predicted value and the residual value of the wind speed.
2. The short term wind speed prediction method of claim 1,
carrying out scene clustering on the wind turbine generator by using a K-medoids clustering algorithm to obtain a plurality of equivalent fans, which comprises the following steps:
acquiring historical wind speed data of each wind turbine generator from a wind power plant;
acquiring historical data of wind speed, power, generator rotating speed and blade angle of a region from a meteorological station, analyzing and processing the data, and determining model input and output variables;
carrying out scene clustering on the wind turbine generator by adopting K-medoids clustering, respectively taking the wind speed and the power of the wind turbine generator as horizontal and vertical coordinate values, constructing a rectangular coordinate system, and establishing a criterion function Fs, wherein the method specifically comprises the following steps:
Figure FDA0003058427900000011
in the formula, j represents the number of the fan variables in each class, n represents the total number of the fan variables in each class, and xi、yiAs a coordinate of the center point, xj、yjCoordinates in class except the central point;
solving a criterion function Fs, and determining k wind turbine generator clusters;
and determining each type of wind turbine generator as an equivalent fan.
3. The short term wind speed prediction method of claim 2,
solving a criterion function Fs, and determining k wind turbine generator clusters, specifically as follows:
s11, randomly selecting k points from the n samples as central points medoids;
s12, distributing the rest points to the current best cluster according to the principle of being nearest to the center;
s13, sequentially calculating the value of the criterion function Fs when the points are medoids for all other points except the medoids in each class, traversing all possibilities, and selecting the point corresponding to the minimum criterion function Fs as a new medoid;
s14, repeating S12 and S13 until all medoids are not changed;
and S15, finally determining k wind turbine generator clusters through continuous iterative operation.
4. The short-term wind speed prediction method according to claim 2 or 3, wherein the method for determining an equivalent fan is specifically as follows:
the power of the equivalent fan is the sum of the powers of the clustered wind turbine generators;
the wind speed of the equivalent fan is the average value of the wind speeds of the clustered wind generation sets;
the generator rotating speed of the equivalent fan is the average value of the generator rotating speeds of the clustered wind turbine generators;
and the blade angle of the equivalent fan is the average value of the blade angles of the clustered wind turbine generators.
5. The short term wind speed prediction method of claim 4,
constructing an LSTM model, training a plurality of equivalent fans through the LSTM model, and obtaining an initial predicted value of the wind speed, wherein the method specifically comprises the following steps:
and (3) the calculated wind speed, power, generator rotating speed and blade angle of the equivalent fan are arranged into a new data set, the new data set is divided into a training set and a testing set, and the training set is input into an LSTM model for training to obtain a model parameter and a preliminary predicted value of the wind speed.
6. The short term wind speed prediction method of claim 5, wherein the training set and the test set have a data ratio of 8: 2.
7. The short term wind speed prediction method of claim 6,
establishing a functional relation among a plurality of equivalent fans by using a hybrid Copula function, and solving parameters in the hybrid Copula function by using a genetic algorithm, wherein the parameters are as follows:
let x be1，x2，...xnN random wind speed variables, the edge distribution of the n random wind speed variables is respectively f1(x1)，f2(x2)，...，fn(xn) The combined distribution is f (x)1，x2，...xn) Let there be a function C, such that:
f(x1，x2，...xn)＝C(f1(x1)，f2(x2)，...，fn(xn)) (2)
let u1＝f1(x1)，u2＝f2(x2)，...，un＝fn(xn) Wherein u is1，u2...unFor the edge distribution of wind speed, a mathematical expression of the Copula function is obtained: c (u)1，u2，...un)；
Establishing a mixed Copula function, wherein the concrete form C is as follows:
C(u，v；θ)＝ω1Cg(u，v；θ1)+ω2Cc(u，v；θ2)+ω3Cf(u，v；θ3) (3)
ω1+ω2+ω3＝1，ω1，ω2，ω3≥0 (4)
wherein u is the wind speed edge distribution value of the first equivalent fan, v is the wind speed edge distribution value of the second equivalent fan, omega1，ω2，ω3As a weight parameter of the model, θ1，θ2，θ3Is a dependent parameter of the model;
solving parameter omega of mixed Copula in the formula (3) by adopting genetic algorithm1、ω2、ω3、θ1、θ2、θ3。
8. The short term wind speed prediction method of claim 7,
solving parameter omega of mixed Copula in the formula (3) by adopting genetic algorithm1、ω2、ω3、θ1、θ2、θ3The method comprises the following steps:
s21, calculating edge distribution values u, v of the wind speeds of the first equivalent fan and the second equivalent fan by a Gaussian kernel density estimation method;
s22, determining a penalty factor gamma of a fitness function of a Genetic Algorithm (GA), and establishing a fitness function f by combining constraint conditions in a formula (4)sThe method comprises the following steps:
Figure FDA0003058427900000031
wherein, Ce(u, v) is an empirical Copula function, ui，viThe ith value represents the edge distribution of the first and second equivalent fans, i represents the number of the edge distribution of the fan, Ce(u, v) are specifically as follows：
Ce(u，v)＝P{U＜u，V＜v} (6)
In the formula, U and V are edge distribution of historical wind speed;
s23, determining the maximum number G of genetic iterationsmAnd a crossing rate pcThe maximum number of genetic iterations represents the number of iterations, the crossover rate pcIs the probability of individual variation in the population, GmSet to 200-500, pcSet to 0.9-0.97;
s24, solving omega by combining the formulas (4), (5) and (6)1、ω2、ω3、θ1、θ2、θ3。
9. The short term wind speed prediction method of claim 8,
and solving a residual value of the wind speed through the wind speed edge distribution values of the equivalent fans and a mixed Copula function, wherein the residual value is as follows:
s31, dividing a data set of the first equivalent fan into a training set and a testing set, inputting the training set into an LSTM model for training to obtain an LSTM wind speed prediction model, solving a preliminary prediction value of the test set LSTM model wind speed, and setting the total number of samples of the test set as n;
s32, solving the edge distribution value v of the second equivalent fan at the corresponding moment, wherein the number of the edge distribution value v is the same as that of the first equivalent fan test sets;
s33, subtracting the actual value of the first equivalent fan at the corresponding moment from the predicted value of the LSTM model wind speed to obtain a residual sequence, recording the residual sequence as y, and obtaining an edge distribution function w of the residual sequence, wherein the method specifically comprises the following steps:
w＝G(y)
s34, establishing a mixed Copula function of the wind speed of the equivalent fan 2 and the residual value of the equivalent fan 1;
s35, when the wind speed value of the second equivalent fan at the moment of t-1 is known, obtaining the corresponding edge distribution value v of the second equivalent fant-1V is to bet-1Substituting into the mixed Copula function expression C as follows:
C＝C(vt-1，w) (7)
s36, pairing the numbers obtained above
Figure FDA0003058427900000041
Fitting to obtain C (v)t-1W) and w are approximated by the following equation:
C＝a1wm+a2wm-1+…amw+am+1 (8)
in the formula, a1，a2，...am+1Is a polynomial coefficient, and m is the order of w;
s37, simultaneous solution of formula (7) and formula (8), which can be solved to obtain w*The variable y is at time t-1
Figure FDA0003058427900000051
The predicted value is:
Figure FDA0003058427900000052
in the formula, G-1Is the inverse of the cumulative distribution function of the residual error w;
s38, calculating the residual value of the first equivalent fan at the time t
Figure FDA0003058427900000053
Using residual error at time t-1
Figure FDA0003058427900000054
Approximately replacing the residual at time t
Figure FDA0003058427900000055
Obtaining a residual error value of the first equivalent fan at the time t
Figure FDA0003058427900000056
The method comprises the following specific steps:
Figure FDA0003058427900000057
in the formula (I), the compound is shown in the specification,
Figure FDA0003058427900000058
is the predicted value of the variable y at the time t.
10. The short term wind speed prediction method of claim 9,
obtaining an accurate predicted value of the wind speed through the initial predicted value and the residual value of the wind speed, which is as follows:
inputting indexes such as wind speed, power, generator rotating speed and blade angle by using the LSTM wind speed prediction model to obtain a preliminary predicted value f of the wind speed at the time tt；
Obtaining a wind speed predicted value with higher precision, which is specifically as follows:
Figure FDA0003058427900000059
in the formula (f)tIs the preliminary predicted value of the wind speed of the LSTM wind speed prediction model at the time t,
Figure FDA00030584279000000510
is the wind speed residual prediction value at the time t,
Figure FDA00030584279000000511
the predicted value of the wind speed at the time t is high in precision.
CN202110506041.8A 2021-05-10 2021-05-10 Short-term wind speed prediction method Pending CN113312836A (en)

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