A method and system for generating a wind farm power output sequence

Abstract

The application relates to a wind farm output sequence generation method and system, which comprises the following steps: respectively determining probability density functions of a target wind farm and adjacent wind farms of the target wind farm according to historical wind power outputs of the target wind farm and the adjacent wind farms; determining a joint probability density function between the target wind farm and the adjacent wind farms according to the probability density functions of the target wind farm and the adjacent wind farms; determining a conditional probability density function of the target wind farm according to the joint probability density function between the target wind farm and the adjacent wind farms; and generating an output sequence of the target wind farm according to the conditional probability density function of the target wind farm. The technical scheme provided by the application generates the output sequence of the wind farm on the basis of considering the probability distribution characteristics, time sequence dependency and spatial correlation of the wind farm and the adjacent wind farms, the output sequence is more consistent with the probability distribution characteristics of the wind farm, and the simulation result is more accurate when the wind power is connected to the grid for simulation analysis.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of wind power output time series dependence analysis, and particularly relates to a wind farm output sequence generation method and system. BACKGROUND

[0002] Wind power output has randomness and volatility, and its large-scale access brings great challenges to the efficient consumption and safe operation of power systems.

[0003] Wind power output time series is the basis for carrying out wind power grid connection simulation analysis, but the development speed of new energy in China is fast, and there is little historical data, so the existing output measured data is difficult to meet the research needs. It is particularly important to generate a large number of wind power output time series with high consistency in characteristics with actual data using limited wind power output measured data.

[0004] In addition, wind power is characterized by large-scale and centralized development in patches, and wind farms with similar geographical locations have similar wind activity. When modeling wind power output time series, the spatial correlation between wind farms developed in patches needs to be considered.

[0005] At present, wind power modeling methods mainly include wind speed method and wind power method. The wind speed method first generates a wind speed model, and then converts it into the corresponding wind power sequence using the power curve of the wind turbine. Since wind power is related to wind speed, wind direction, and wind farm terrain characteristics, the model accuracy is not high.

[0006] The wind power method directly uses historical wind power output data to establish a time series output model. Currently commonly used statistical analysis methods include ARMA model, neural network and Markov chain Monte Carlo, which mainly consider the time series dependence and volatility of wind power, but do not consider the influence of the spatial correlation between wind farm output and its adjacent wind farm output on the generated output sequence. SUMMARY

[0007] In view of the shortcomings of the prior art, the present application provides a wind farm output sequence generation method, which generates wind farm output sequence by considering the probability distribution characteristics, time series dependence of wind farm output, and spatial correlation between wind farm output and its adjacent wind farm output. The output sequence has higher consistency with the probability distribution characteristics of the wind farm, and the simulation result is more accurate when carrying out wind power grid connection simulation analysis.

[0008] The purpose of the present application is achieved by using the following technical solutions:

[0009] The present application provides a wind farm output sequence generation method, which improves in that the method comprises:

[0010] determine a joint probability density function between the target wind farm and the neighboring wind farms according to the probability density functions of the target wind farm and the neighboring wind farms;

[0011] determine a joint probability density function between the target wind farm and the neighboring wind farms according to the probability density functions of the target wind farm and the neighboring wind farms;

[0012] determine a joint probability density function between the target wind farm and the neighboring wind farms according to the probability density functions of the target wind farm and the neighboring wind farms;

[0013] generate an output sequence of the target wind farm according to the conditional probability density function of the target wind farm.

[0014] The present application provides a wind farm output sequence generation system, which is improved in that the system comprises:

[0015] a first determining module configured to determine a probability density function of the target wind farm and the neighboring wind farms according to historical wind farm outputs of the target wind farm and the neighboring wind farms;

[0016] a second determining module configured to determine a joint probability density function between the target wind farm and the neighboring wind farms according to the probability density functions of the target wind farm and the neighboring wind farms;

[0017] a third determining module configured to determine a conditional probability density function of the target wind farm according to the joint probability density function between the target wind farm and the neighboring wind farms;

[0018] a generating module configured to generate an output sequence of the target wind farm according to the conditional probability density function of the target wind farm.

[0019] Compared with the closest prior art, the present application has the beneficial effects that:

[0020] The technical scheme provided by the present application determines a probability density function of the target wind farm and the neighboring wind farms according to historical wind farm outputs of the target wind farm and the neighboring wind farms, determines a joint probability density function between the target wind farm and the neighboring wind farms according to the probability density functions of the target wind farm and the neighboring wind farms, determines a conditional probability density function of the target wind farm according to the joint probability density function between the target wind farm and the neighboring wind farms, and generates an output sequence of the target wind farm according to the conditional probability density function of the target wind farm, which generates the output sequence of the wind farm on the basis of considering the probability distribution characteristics, time sequence dependence and spatial correlation of the wind farm output and the output of the neighboring wind farms, and the output sequence of the wind farm is more consistent with the probability distribution characteristics of the wind farm, and the simulation result is more accurate when the wind power is connected to the grid for simulation analysis. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 is a flow chart of a wind farm output sequence generation method;

[0022] Figure 2 is a historical output distribution and probability density function curve chart of a wind farm R1 in an embodiment of the present application;

[0023] Figure 3 is a historical output distribution and probability density function curve chart of a wind farm R2 in an embodiment of the present application;

[0024] Figure 4 is a historical output distribution and probability density function curve chart of a wind farm R2 in an embodiment of the present application;

[0025] Figure 5 is a structural diagram of a wind farm output sequence generation system. DETAILED DESCRIPTION

[0026] The specific embodiments of the present application will be further described in detail below with reference to the accompanying drawings.

[0027] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0028] The present application provides a wind farm output sequence generation method, as shown in Figure 1 The method comprises the following steps:

[0029] Step 101. Determine the probability density functions of a target wind farm and its adjacent wind farms according to the historical wind power outputs of the target wind farm and its adjacent wind farms, respectively.

[0030] Step 102. Determine the joint probability density functions between the target wind farm and its adjacent wind farms according to the probability density functions of the target wind farm and its adjacent wind farms.

[0031] Step 103. Determine the conditional probability density function of the target wind farm according to the joint probability density functions between the target wind farm and its adjacent wind farms.

[0032] Step 104. Generate the output sequence of the target wind farm according to the conditional probability density function of the target wind farm.

[0033] Preferably, the step 101 comprises:

[0034] The probability density function f of the wind farm i is determined according to the following formulai (x i ):

[0035] f i (x i )=k ai f ai (x i )+k bi f bi (x i )

[0036] In the formula, k ai f represents the weights corresponding to the exponential probability density function of wind farm i. ai (x i Let k be the exponential probability density function of wind farm i. bi f represents the weights corresponding to the normal probability density function of wind farm i. bi (x i Let k be the normal probability density function of wind farm i. ai +k bi =1, i∈(1,2), when i=1, wind farm i is an adjacent wind farm of the target wind farm, when i=2, wind farm i is the target wind farm;

[0037] The exponential probability density function f of wind farm i is determined by the following formula. ai (x i ):

[0038]

[0039] In the formula, λ i Let x be the rate parameter of the exponential probability density function of wind farm i; i Let be the normalized value of any wind power output of the exponential distribution probability density function of wind farm i;

[0040] The normal probability density function f of wind farm i is determined by the following formula. bi (x i ):

[0041]

[0042] In the formula, μ i σ is the mean of the normal probability density function of wind farm i; i Let be the standard deviation of the normal probability density function of wind farm i.

[0043] Furthermore, obtain λ from the probability density function of wind farm i. i k ai k bi μ i and σi The process comprises:

[0044] The maximum likelihood estimation value of λ i , k ai , k bi , μ i and σ i corresponding to the maximum value of the likelihood function of the probability density function of the wind farm i is solved by using the maximum likelihood estimation method, and the maximum likelihood estimation value of λ i , k ai , k bi , μ i and σ i is taken as the value of λ i , k ai , k bi , μ i and σ i .

[0045] In the specific embodiments of the present application, the maximum likelihood estimation value of λ i , k ai , k bi , μ i and σ i corresponding to the maximum value of the likelihood function of the probability density function of the wind farm i is solved by using the maximum likelihood estimation method, which is equivalent to solving the following equation group to obtain λ i , k ai , k bi , μ i and σ i :

[0046]

[0047] Further, the likelihood function L i (λ i , k ai , k bi , μ i , σ i ) of the probability density function of the wind farm i is determined according to the following formula:

[0048]

[0049] In the formula, P is the normalized value of the wind power output of the wind farm i at the tth historical moment; t ∈ (1 ~ T), and T is the total number of historical moments;

[0050] The normalized value of the wind power output of the wind farm i at the tth historical moment is determined according to the following formula:

[0051]

[0052] wherein w it is the wind power output value of the wind farm i at the tth historical time, C i is the installed capacity of the wind farm i.

[0053] Specifically, the step 102 comprises:

[0054] The joint probability density function between the target wind farm and its adjacent wind farms is determined according to the following formula

[0055]

[0056] wherein c(u, v; θ) is the probability density function of the Frank Copula function, f1(x1) is the probability density function of the adjacent wind farm of the target wind farm, and f2(x2) is the probability density function of the target wind farm.

[0057] The probability density function c(u, v; θ) of the Frank Copula function is determined according to the following formula:

[0058]

[0059] The probability distribution function u of the probability density function of the adjacent wind farm of the target wind farm is determined according to the following formula:

[0060]

[0061] The probability distribution function v of the probability density function of the target wind farm is determined according to the following formula:

[0062]

[0063] wherein u is the probability distribution function of the adjacent wind farm of the target wind farm, v is the probability distribution function of the target wind farm, θ is the characteristic parameter of the probability density function of the Frank Copula function, x1 is any wind power normalized value in the probability density function of the adjacent wind farm of the target wind farm, and x2 is any wind power normalized value in the probability density function of the target wind farm.

[0064] Further, the process of obtaining the characteristic parameter θ of the probability density function of the Frank Copula function comprises:

[0065] The maximum likelihood estimation value of θ corresponding to the maximum value of the likelihood function of the joint probability density function between the target wind farm and its adjacent wind farms is solved by using the maximum likelihood estimation method, and the maximum likelihood estimation value of θ is taken as the value of θ.

[0066] In the best embodiment of the present application, the maximum likelihood estimation value of θ corresponding to the maximum value of the likelihood function of the joint probability density function between the target wind farm and its adjacent wind farm is solved by using the maximum likelihood estimation method, which is equivalent to solving the following equation to obtain θ:

[0067]

[0068] Further, the likelihood function L of the joint probability density function between the target wind farm and its adjacent wind farm is determined according to the following formula: R1,R2 (θ):

[0069]

[0070] In the formula, f2(x2) is the probability density function of the target wind farm, is the probability value corresponding to the normalized value of the wind power output of the adjacent wind farm of the target wind farm at the tth historical moment, is the probability value corresponding to the normalized value of the wind power output of the target wind farm at the tth historical moment, t∈(1~T), and T is the total number of historical moments.

[0071] Specifically, the step 103 comprises:

[0072] The conditional probability density function of the target wind farm at the tth historical moment is determined according to the following formula:

[0073]

[0074] In the formula, f2(x2) is the probability density function of the target wind farm, is the probability density function of the Frank Copula function after the normalized value of the wind power output of the adjacent wind farm of the target wind farm at the tth historical moment is substituted into the probability density function of the Frank Copula function, f2(x2) is the probability density function of the target wind farm, is the joint probability density function between the target wind farm and its adjacent wind farm after the normalized value of the wind power output of the adjacent wind farm of the target wind farm at the tth historical moment is substituted into the joint probability density function between the target wind farm and its adjacent wind farm, is the probability value corresponding to the normalized value of the wind power output of the adjacent wind farm of the target wind farm at the tth historical moment, t∈(1~T), and T is the total number of historical moments.

[0075] Specifically, the step 104 comprises:

[0076] The output sequence W'2 of the target wind farm is determined according to the following formula:

[0077]

[0078] In the formula, C2 is the installed capacity of the target wind farm;

[0079] The normalized output sequence of the target wind farm is determined according to the following formula

[0080]

[0081] In the formula, Ct is the normalized value of the wind power output generated by the target wind farm at the tth historical moment, t e (1~T), and T is the total number of historical moments.

[0082] Further, the process of using the selection method sampling to obtain the normalized value of the wind power output generated by the target wind farm at the tth historical moment includes:

[0083] Step 1: Calculate the integral S of the conditional probability density function of the target wind farm at the tth historical moment in the definition domain [0~1] a ;

[0084] Step 2: Generate random numbers r1 and r2 uniformly distributed on [0~1];

[0085] Step 3: Substitute the random number r1 into the conditional probability density function of the target wind farm at the tth historical moment to obtain the value of the conditional probability density function of the target wind farm at the tth historical moment

[0086] Step 4: Determine whether r2 satisfies If yes, the normalized value of the wind power output generated by the target wind farm at the tth historical moment is Otherwise, return to step 2.

[0087] In the specific embodiments of the present application, the normalized value of the wind power output generated by the target wind farm at each historical moment is determined by using the selection method sampling.

[0088] In the specific embodiments of the present application, the following takes the wind farms R1 and R2 with similar geographical positions in a certain place as examples to illustrate the present application:

[0089] Step a1: Read the output historical data of the wind farms R1 and R2.

[0090] Through the data acquisition and monitoring system, the historical output time sequences W1 and W2 of two adjacent wind farms R1 and R2 with similar geographical positions in a certain place from 0:00:00 on January 1, 2017 to 23:45:00 on June 30, 2018 are read, the time resolution is 15 min, and the sequence length T=52416. The historical output time sequences of the wind farms R1 and R2 are:

[0091] W1 = {w 11 ,w 12 ,…,w 1-52416}

[0092] W2 = {w 21 ,w 22 ,…,w 2-52416}

[0093] Step b1: Calculate the normalized historical wind power output time series according to the normalization principle.

[0094] The installed capacity of wind farms R1 and R2 is 45 MW and 30 MW respectively, and the historical output data of wind farms R1 and R2 is normalized according to its installed capacity, as shown in the following formula:

[0095]

[0096]

[0097] Step c1: Solve the probability density function of the historical output of wind farms R1 and R2.

[0098] The empirical distribution of the normalized historical output of wind farms R1 and R2 in step b1 is fitted using a mixture distribution, and the parameters of the mixture distribution are solved using the maximum likelihood estimation method, and the probability density functions are respectively:

[0099]

[0100]

[0101] The distribution of the historical output of wind farms R1 and R2 and the fitted probability density function curve are shown in Figure 2 、 Figure 3

[0102] Step d1: Solve the joint probability density function of wind farms R1 and R2 using the probability density function of the Copula function.

[0103] The parameter θ of the probability density function of the Copula function is obtained by using the maximum likelihood estimation method θ = 50.28, then the joint probability density function f R1,R2 (x1,x2) of wind farms R1 and R2 is:

[0104] f R1,R2 (x1,x2) = c(u,v; 50.28) · f1(x1) · f2(x2) (27)

[0105] ​Step e1: according to the conditional probability density function of the wind farm R2, a new output time series Y of the wind farm R2 is generated by using the rejection method, wherein the distribution of the normalized values of the newly generated sequence is highly consistent with the probability density function curve as shown in Figure 4

[0106] Step f1: the new output time series Y of the wind farm R2 and the average value and the standard deviation of the historical output sequence of the wind farm R2 are calculated, as shown in Table 1.

[0107] Table 1

[0108] Sequence Average Standard deviation New sequence of R2 12.86 7.30 Historical sequence of R2 12.69 7.14

[0109] It can be seen from Figure 4 that the output distribution of the newly generated wind farm R2 is highly consistent with the probability density function curve, and according to Table 1, the average value and the standard deviation of the newly generated output of the wind farm R2 are very small compared with the average value and the standard deviation of the historical sequence, which verifies the accuracy and effectiveness of the method.

[0110] The present application provides a wind farm output sequence generation system, as shown in Figure 5 , the system comprises:

[0111] A first determination module is configured to determine the probability density functions of the target wind farm and its adjacent wind farms according to the historical wind power outputs of the target wind farm and its adjacent wind farms, respectively.

[0112] A second determination module is configured to determine the joint probability density functions between the target wind farm and its adjacent wind farms according to the probability density functions of the target wind farm and its adjacent wind farms.

[0113] A third determination module is configured to determine the conditional probability density function of the target wind farm according to the joint probability density functions between the target wind farm and its adjacent wind farms.

[0114] A generation module is configured to generate the output sequence of the target wind farm according to the conditional probability density function of the target wind farm.

[0115] Specifically, the first determination module is configured to:

[0116] determine the probability density function f i (x i ) of the wind farm i according to the following formula:

[0117] f i (x i )=k ai f ai (x i )+k bi f bi (x i )​

[0118] wherein k ai is the weight corresponding to the exponential distribution probability density function of the wind farm i, f ai (x i ) is the exponential distribution probability density function of the wind farm i, k bi is the weight corresponding to the normal distribution probability density function of the wind farm i, f bi (x i ) is the normal distribution probability density function of the wind farm i, k ai +k bi =1, i∈(1,2), when i=1, the wind farm i is the adjacent wind farm of the target wind farm, when i=2, the wind farm i is the target wind farm;

[0119] wherein the exponential distribution probability density function f ai (x i ) of the wind farm i is determined according to the following formula:

[0120]

[0121] wherein λ i is the rate parameter of the exponential distribution probability density function of the wind farm i; x i is any wind power normalized value of the exponential distribution probability density function of the wind farm i;

[0122] the normal distribution probability density function f bi (x i ) of the wind farm i is determined according to the following formula:

[0123]

[0124] wherein μ i is the mean value in the normal distribution probability density function of the wind farm i; σ i is the standard deviation in the normal distribution probability density function of the wind farm i.

[0125] Further, the process of obtaining λ i , k ai , k bi , μ i and σ i in the probability density function of the wind farm i includes:

[0126] the maximum likelihood estimation values of λ i , k ai , k bi , μ i and σ i corresponding to the maximum value of the likelihood function of the probability density function of the wind farm i are solved by using the maximum likelihood estimation method, and λ i , kai k bi μ i and σ i The maximum likelihood estimate is used as λ i k ai k bi μ i and σ i The value of .

[0127] Furthermore, the likelihood function L of the probability density function of wind farm i is determined by the following formula. i (λ i ,k ai ,k bi ,μ i ,σ i ):

[0128]

[0129] In the formula, Let be the normalized value of wind power output of wind farm i at the t-th historical moment; t∈(1~T), where T is the total number of historical moments;

[0130] The normalized value of wind power output of wind farm i at the t-th historical moment is determined by the following formula.

[0131]

[0132] In the formula, w it Let C be the wind power output of wind farm i at the t-th historical moment. i Let i be the installed capacity of wind farm i.

[0133] Specifically, the second determining module is used for:

[0134] The joint probability density function between the target wind farm and its neighboring wind farms is determined by the following formula.

[0135]

[0136] In the formula, c(u,v;θ) is the probability density function of the Frank Copula function, f1(x1) is the probability density function of the adjacent wind farms of the target wind farm, and f2(x2) is the probability density function of the target wind farm.

[0137] The probability density function c(u,v; θ) of the Frank Copula function is determined by the following formula:

[0138]

[0139] The probability distribution function u of the adjacent wind farm of the target wind farm is determined according to the following formula:

[0140]

[0141] The probability distribution function v of the target wind farm is determined according to the following formula:

[0142]

[0143] In the formula, u is the probability distribution function of the adjacent wind farm of the target wind farm, v is the probability distribution function of the target wind farm, θ is a characteristic parameter of the probability density function of the Frank Copula function, x1 is any wind power normalized value in the probability density function of the adjacent wind farm of the target wind farm, and x2 is any wind power normalized value in the probability density function of the target wind farm.

[0144] Further, the process of obtaining the characteristic parameter θ of the probability density function of the Frank Copula function includes:

[0145] The maximum likelihood estimation value of θ corresponding to the maximum value of the likelihood function of the joint probability density function between the target wind farm and its adjacent wind farm is solved by using the maximum likelihood estimation method, and the maximum likelihood estimation value of θ is taken as the value of θ.

[0146] Further, the likelihood function L(θ) of the joint probability density function between the target wind farm and its adjacent wind farm is determined according to the following formula: R1,R2 (θ):

[0147]

[0148] In the formula, is the probability value corresponding to the wind power normalized value at the tth historical moment in the probability density function of the adjacent wind farm of the target wind farm, is the probability value corresponding to the wind power normalized value at the tth historical moment in the probability density function of the target wind farm, t ∈ (1 ~ T), and T is the total number of historical moments.

[0149] Specifically, the third determination module is configured to:

[0150] The conditional probability density function of the target wind farm at the tth historical moment is determined according to the following formula:

[0151]

[0152] In the formula, f2(x2) is the probability density function of the target wind farm, f2(x2) is the probability density function of the target wind farm, f2(x2) is the probability density function of the target wind farm,

[0153] Specifically, the generating module is configured to:

[0154] The output sequence W'2 of the target wind farm is determined according to the following formula:

[0155]

[0156] In the formula, W'2 is the normalized output sequence of the target wind farm, and C2 is the installed capacity of the target wind farm.

[0157] The normalized output sequence of the target wind farm is determined according to the following formula:

[0158]

[0159] In the formula, W'2 is the normalized output sequence of the target wind farm, and C2 is the installed capacity of the target wind farm.

[0160] Further, the process of obtaining the normalized wind power output value generated by the target wind farm at the tth historical moment includes:

[0161] Step 1: Calculate the integral S of the conditional probability density function of the target wind farm at the tth historical moment in the definition domain [0-1] a ;

[0162] Step 2: Generate random numbers r1 and r2 uniformly distributed in [0-1];

[0163] Step 3: Substitute the random number r1 into the conditional probability density function of the target wind farm at the tth historical moment to obtain the value of the conditional probability density function of the target wind farm at the tth historical moment

[0164] Step 4: Determine whether r2 satisfies If yes, let the target wind farm generate wind power output normalized value at the tth historical moment Otherwise, return to step 2.

[0165] Those skilled in the art will understand that embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) containing computer usable program code.

[0166] The present application is described with reference to the flowchart and / or block diagrams of the methods, apparatus (systems) and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as a combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 The functions specified in the flowchart and / or block diagram block or blocks. Figure 1 The functions specified in the flowchart and / or block diagram block or blocks.

[0167] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction devices that implement the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 The functions specified in the flowchart and / or block diagram block or blocks. Figure 1 The functions specified in the flowchart and / or block diagram block or blocks.

[0168] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide a process for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 The functions specified in the flowchart and / or block diagram block or blocks. Figure 1 The functions specified in the flowchart and / or block diagram block or blocks.

[0169] It should be pointed out finally that the above embodiments are only used to illustrate the technical solutions of the present application but not to limit it. Although the present application has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that the specific embodiments of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and any modification or equivalent replacement without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.

Claims

1. A method of generating a wind farm power output sequence, characterized by, The method comprises: According to the historical wind power output of the target wind farm and its adjacent wind farms, the probability density functions of the target wind farm and its adjacent wind farms are determined respectively; According to the probability density functions of the target wind farm and its adjacent wind farms, the joint probability density function between the target wind farm and its adjacent wind farms is determined; According to the joint probability density function between the target wind farm and its adjacent wind farms, the conditional probability density function of the target wind farm is determined; According to the conditional probability density function of the target wind farm, the output sequence of the target wind farm is generated; According to the conditional probability density function of the target wind farm, the output sequence of the target wind farm is generated, which comprises: The output sequence W2' of the target wind farm is determined according to the following formula: In the formula, C2 is the installed capacity of the target wind farm. The normalized output sequence of the target wind farm is determined according to the following formula In the formula, is the normalized value of the wind power output generated by the target wind farm at the tth historical time, t∈(1~T), and T is the total number of historical times. The process of sampling the normalized value of the wind power output generated by the target wind farm at the tth historical time point by using the selection method comprises: Step 1: Calculate the integral S of the conditional probability density function of the target wind farm at the tth historical moment over the domain [0~1] a ; Step 2: generate random numbers r1 and r2 with uniform distribution on [0, 1]; Step 3: Substitute the random number r1 into the conditional probability density function of the target wind farm at the tth historical time to obtain the value of the conditional probability density function of the target wind farm at the tth historical time Step 4: judge whether r2 satisfies If yes, let the normalized value of the wind power output generated by the target wind farm at the tth historical moment be Otherwise, return to Step 2.

2. The method of claim 1, wherein, According to the historical wind power output of the target wind farm and its adjacent wind farms, the probability density functions of the target wind farm and its adjacent wind farms are determined respectively, which comprises: The probability density function f of the wind farm i is determined as follows i (x i ): f i (x i )=k ai f ai (x i )+k bi f bi (x i ) wherein k ai is the weight corresponding to the exponential distribution probability density function of the wind farm i, f ai (x i ) is the exponential distribution probability density function of the wind farm i, k bi is the weight corresponding to the normal distribution probability density function of the wind farm i, f bi (x i ) is the normal distribution probability density function of the wind farm i, k ai +k bi =1, i∈(1,2), when i=1, the wind farm i is the adjacent wind farm of the target wind farm, when i=2, the wind farm i is the target wind farm. where the exponential distribution probability density function f of the wind farm i is determined as follows ai (x i ): where λ i is the rate parameter of the exponential distribution probability density function of wind farm i; x i is any wind power output normalized value of wind farm i; The normal distribution probability density function f of the wind farm i is determined as follows bi (x i ): wherein μ i is the mean value in the normal distribution probability density function of the wind farm i; σ i is the standard deviation in the normal distribution probability density function of the wind farm i.

3. The method of claim 2, wherein, The process of obtaining the probability density function of the wind farm i in terms of λ i , k ai , k bi , μ i , and σ i includes: The maximum likelihood estimation values of λ i , k ai , k bi , μ i , and σ i are obtained by solving the maximum likelihood estimation method of the probability density function of the wind farm i, and the maximum likelihood estimation values of λ i , k ai , k bi , μ i , and σ i are taken as the values of λ i , k ai , k bi , μ i , and σ i .

4. The method of claim 3, wherein, The likelihood function L of the probability density function of the wind farm i is determined as follows i (λ i ,k ai ,k bi ,μ i ,σ i ): In the formula, is the normalized value of the wind power output of the wind farm i at the tth historical moment; t ∈ (1 ~ T), and T is the total number of historical moments. wherein the wind power output normalized value of the wind farm i at the tth historical moment is determined according to the following formula In the formula, w it is the wind power output value of the wind farm i at the tth historical moment, C i is the installed capacity of the wind farm i.

5. The method of claim 1, wherein, According to the probability density functions of the target wind farm and its adjacent wind farms, the joint probability density function between the target wind farm and its adjacent wind farms is determined, which comprises: The joint probability density function between the target wind farm and its neighboring wind farms is determined as follows In the formula, c(u, v; θ) is the probability density function of the Frank Copula function, f1(x1) is the probability density function of the adjacent wind farm of the target wind farm, and f2(x2) is the probability density function of the target wind farm; The probability density function c(u, v; θ) of the Frank Copula function is determined according to the following formula: The probability distribution function u of the probability density function of the adjacent wind farm of the target wind farm is determined according to the following formula: The probability distribution function v of the probability density function of the target wind farm is determined according to the following formula: In the formula, u is the probability distribution function of the adjacent wind farm of the target wind farm, v is the probability distribution function of the target wind farm, θ is the characteristic parameter of the probability density function of the Frank Copula function, x1 is any wind power normalized value in the probability density function of the adjacent wind farm of the target wind farm, and x2 is any wind power normalized value in the probability density function of the target wind farm.

6. The method of claim 5, wherein, The process of obtaining the characteristic parameter θ of the probability density function of the Frank Copula function comprises: The maximum likelihood estimation value of θ corresponding to the maximum value of the likelihood function of the joint probability density function between the target wind farm and its adjacent wind farms is solved by using the maximum likelihood estimation method, and the maximum likelihood estimation value of θ is taken as the value of θ.

7. The method of claim 6, wherein, The likelihood function L of the joint probability density function between the target wind farm and its neighboring wind farms is determined as follows R1,R2 (θ): In the formula, is the probability value corresponding to the normalized value of the wind power output of the target wind farm at the tth historical moment in the probability density function of the adjacent wind farm, is the probability value corresponding to the normalized value of the wind power output of the target wind farm at the tth historical moment in the probability density function, t∈(1~T), and T is the total number of historical moments.

8. The method of claim 1, wherein, According to the joint probability density function between the target wind farm and its adjacent wind farms, the conditional probability density function of the target wind farm is determined, which comprises: The conditional probability density function of the target wind farm at the tth historical time is determined according to the following formula In the formula, is the probability density function of the Frank Copula function after the wind power output normalized value of the adjacent wind farm of the target wind farm at the tth historical moment is substituted into the probability density function of the Frank Copula function, f2(x2) is the probability density function of the target wind farm, is the joint probability density function between the target wind farm and its adjacent wind farm after the wind power output normalized value of the adjacent wind farm of the target wind farm at the tth historical moment is substituted into the joint probability density function between the target wind farm and its adjacent wind farm, is the probability value corresponding to the wind power output normalized value at the tth historical moment in the probability density function of the adjacent wind farm of the target wind farm, t∈(1~T), and T is the total number of historical moments.

9. A wind farm power output sequence generation system characterized by comprising: The system comprises: A first determination module is configured to determine the probability density functions of the target wind farm and its adjacent wind farms according to the historical wind power output of the target wind farm and its adjacent wind farms respectively; A second determination module is configured to determine the joint probability density function between the target wind farm and its adjacent wind farms according to the probability density functions of the target wind farm and its adjacent wind farms; and A third determination module is configured to determine the conditional probability density function of the target wind farm according to the joint probability density function between the target wind farm and its adjacent wind farms. The third determining module is configured to determine a conditional probability density function of the target wind farm according to a joint probability density function between the target wind farm and its adjacent wind farm; The generating module is configured to generate an output sequence of the target wind farm according to the conditional probability density function of the target wind farm; The generating module is configured to: Determine the output sequence W2' of the target wind farm according to the following formula: In the formula, C2 is the installed capacity of the target wind farm. The normalized output sequence of the target wind farm is determined according to the following formula In the formula, is the normalized value of the wind power output generated by the target wind farm at the tth historical time, t∈(1~T), and T is the total number of historical times. The process of sampling the normalized value of the wind power output of the target wind farm generated at the tth historical moment by using the selection method includes: Step 1: Calculate the integral S of the conditional probability density function of the target wind farm at the tth historical time on the definition domain [0~1] a ; Step 2: generate random numbers r1 and r2 with uniform distribution on [0, 1]; Step 3: Substitute the random number r1 into the conditional probability density function of the target wind farm at the tth historical time to obtain the value of the conditional probability density function of the target wind farm at the tth historical time Step 4: judge whether r2 satisfies If yes, let the normalized value of the wind power output generated by the target wind farm at the tth historical moment be Otherwise, return to Step 2.

10. The system of claim 9, wherein, The first determining module is configured to: The probability density function f of the wind farm i is determined as follows i (x i ): f i (x i )=k ai f ai (x i )+k bi f bi (x i ) wherein k ai is the weight corresponding to the exponential distribution probability density function of the wind farm i, f ai (x i ) is the exponential distribution probability density function of the wind farm i, k bi is the weight corresponding to the normal distribution probability density function of the wind farm i, f bi (x i ) is the normal distribution probability density function of the wind farm i, k ai +k bi =1, i∈(1,2), when i=1, the wind farm i is the adjacent wind farm of the target wind farm, when i=2, the wind farm i is the target wind farm. where the exponential distribution probability density function f of the wind farm i is determined as follows ai (x i ): where λ i is the rate parameter of the exponential distribution probability density function of wind farm i; x i is any wind power output normalized value of wind farm i; The normal distribution probability density function f of the wind farm i is determined as follows bi (x i ): wherein μ i is the mean value in the normal distribution probability density function of the wind farm i; σ i is the standard deviation in the normal distribution probability density function of the wind farm i.

11. The system of claim 10, wherein, The process of obtaining the probability density function of the wind farm i in terms of λ i , k ai , k bi , μ i and σ i includes: The maximum likelihood estimation values of λ i , k ai , k bi , μ i , and σ i are obtained by solving the maximum likelihood estimation method of the probability density function of the wind farm i, and the maximum likelihood estimation values of λ i , k ai , k bi , μ i , and σ i are taken as the values of λ i , k ai , k bi , μ i , and σ i .

12. The system of claim 11, wherein, The likelihood function L of the probability density function of the wind farm i is determined as follows i (λ i ,k ai ,k bi ,μ i ,σ i ) In the formula, is the normalized value of the wind power output of the wind farm i at the tth historical moment; t ∈ (1 ~ T), and T is the total number of historical moments. wherein the wind power output normalized value of the wind farm i at the tth historical moment is determined according to the following formula In the formula, w it is the wind power output value of the wind farm i at the tth historical moment, C i is the installed capacity of the wind farm i.

13. The system of claim 9, wherein, The second determining module is configured to: The joint probability density function between the target wind farm and its neighboring wind farms is determined as follows In the formula, c(u, v; θ) is a probability density function of the Frank Copula function, f1(x1) is a probability density function of the adjacent wind farm of the target wind farm, and f2(x2) is a probability density function of the target wind farm. The probability density function c(u, v; θ) of the Frank Copula function is determined according to the following formula: The probability distribution function u of the probability density function of the adjacent wind farm of the target wind farm is determined according to the following formula: The probability distribution function v of the probability density function of the target wind farm is determined according to the following formula: In the formula, u is the probability distribution function of the adjacent wind farm of the target wind farm, v is the probability distribution function of the target wind farm, θ is a characteristic parameter of the probability density function of the Frank Copula function, x1 is any normalized value of wind power output in the probability density function of the adjacent wind farm of the target wind farm, and x2 is any normalized value of wind power output in the probability density function of the target wind farm.

14. The system of claim 13, wherein, The process of obtaining the characteristic parameter θ of the probability density function of the Frank Copula function includes: The maximum likelihood estimation value of θ corresponding to the maximum value of the likelihood function of the joint probability density function between the target wind farm and its adjacent wind farm is solved by using the maximum likelihood estimation method, and the maximum likelihood estimation value of θ is taken as the value of θ.

15. The system of claim 14, wherein, The likelihood function L of the joint probability density function between the target wind farm and its neighboring wind farms is determined as follows R1,R2 (θ): In the formula, is the probability value corresponding to the normalized value of the wind power output of the target wind farm at the tth historical moment in the probability density function of the adjacent wind farm, is the probability value corresponding to the normalized value of the wind power output of the target wind farm at the tth historical moment in the probability density function, t∈(1~T), and T is the total number of historical moments.

16. The system of claim 9, wherein, The third determining module is configured to: The conditional probability density function of the target wind farm at the tth historical time is determined according to the following formula In the formula, is the probability density function of the Frank Copula function after the wind power output normalized value of the adjacent wind farm of the target wind farm at the tth historical moment is substituted into the probability density function of the Frank Copula function, f2(x2) is the probability density function of the target wind farm, is the joint probability density function between the target wind farm and its adjacent wind farm after the wind power output normalized value of the adjacent wind farm of the target wind farm at the tth historical moment is substituted into the joint probability density function between the target wind farm and its adjacent wind farm, is the probability value corresponding to the wind power output normalized value at the tth historical moment in the probability density function of the adjacent wind farm of the target wind farm, t∈(1~T), and T is the total number of historical moments.

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