Method and equipment for determining confidence interval of equivalent frequency modulation parameters of wind power plant

Abstract

The invention relates to the technical field of wind power plant control, in particular to a method and equipment for determining a confidence interval of equivalent frequency modulation parameters of a wind power plant. The method comprises the steps that a plurality of historical samples are obtained, and each historical sample comprises the theoretical power of a wind power plant and the wind speed of each wind turbine generator at the same historical moment; based on the multiple historical samples, multiple pieces of estimated wind speed scene information of the wind power plant under the target theoretical power are determined, and the estimated wind speed scene information comprises the estimated wind speed of each wind turbine generator; determining a confidence interval of an equivalent frequency modulation parameter corresponding to the wind power plant under the target theoretical power based on the load reduction power of the wind power plant, the multiple pieces of estimated wind speed scene information and the constructed function relationship of the equivalent frequency modulation parameter of the wind power plant; wherein the function relationship of the equivalent frequency modulation parameters is determined based on a frequency response model between the wind power plant and the power system, and by adopting the scheme of the invention, the confidence interval of the equivalent frequency modulation parameters of the wind power plant can be determined under the condition that the wind speed is unknown.

Description

Technical Field

[0001] The present application relates to the technical field of wind farm control, and in particular to a method and device for determining a confidence interval of an equivalent frequency modulation parameter of a wind farm. Background Art

[0002] Wind farms connected to the power system and operated in grid-connected operation must have fast frequency response capability, that is, they can quickly and actively adjust their active power according to the frequency deviation of the power system, so as to suppress the frequency change of the system and ensure the frequency safety of the system. The existence of frequency deviation in the power system indicates that there is unbalanced power in the power system, which requires wind farms to adjust the active power connected to the power system. The dispatching department needs to master the fast frequency response capability of wind farms in order to coordinate various frequency regulation resources in the power system. Therefore, quantitative evaluation of the fast frequency response capability of wind farms is a key issue.

[0003] The existing wind farm equivalent frequency regulation parameter evaluation and calculation methods generally require the input of wind speed information of each wind turbine in the wind farm. However, in the prediction and evaluation scenario, the detailed wind speed information of each wind turbine in the wind farm is not available. Therefore, the existing technology generally calculates the equivalent frequency regulation parameters of the wind farm based on the initial operating point linearization method. However, when the speed of the wind turbines in the wind farm deviates greatly from the initial operating point during the frequency support process or there is output power saturation, the initial operating point linearization method will fail, and it is difficult to accurately describe the equivalent frequency regulation parameters of the wind farm.

[0004] Therefore, how to determine the equivalent frequency modulation parameters of a wind farm under the condition of unknown wind speed is an urgent problem to be solved. Summary of the invention

[0005] Based on this, it is necessary to provide a method and device for determining a confidence interval of an equivalent frequency modulation parameter of a wind farm, which can more accurately estimate the equivalent frequency modulation parameter of the wind farm, in response to the above technical problems.

[0006] In a first aspect, the present application provides a method for determining a confidence interval of an equivalent frequency regulation parameter of a wind farm, comprising:

[0007] Acquire multiple historical samples, each of which includes the theoretical power of the wind farm and the wind speed of each wind turbine at the same historical moment;

[0008] Based on the multiple historical samples, determine multiple estimated wind speed scenario information of the wind farm under the target theoretical power, the estimated wind speed scenario information including the estimated wind speed of each wind turbine;

[0009] Determine a confidence interval of the equivalent frequency modulation parameter corresponding to the wind farm under the target theoretical power based on the load reduction power of the wind farm, the multiple estimated wind speed scenario information and the constructed functional relationship of the equivalent frequency modulation parameter of the wind farm;

[0010] The functional relationship of the equivalent frequency regulation parameters is determined based on a frequency response model between the wind farm and the power system.

[0011] In one embodiment, determining multiple estimated wind speed scenario information of the wind farm at the target theoretical power based on the multiple historical samples includes:

[0012] Screening the plurality of historical samples based on the target theoretical power to obtain a valid sample;

[0013] Based on the wind speed of each wind turbine in each of the valid samples, a marginal cumulative distribution function is constructed to characterize the wind speed of each wind turbine;

[0014] Based on the connection function, the marginal cumulative distribution function of the wind speed of each wind turbine is connected in parallel to obtain a joint distribution function of the wind speed in the wind farm;

[0015] Performing an estimation processing step on the joint distribution function to determine multiple estimated wind speed scenario information of the wind farm under the target theoretical power, the estimation processing step comprising:

[0016] Sampling the joint distribution function to obtain I wind speed scenario information, where the wind speed scenario information includes the wind speed of each wind turbine in the wind farm, where I is the number of samples and I is greater than 1;

[0017] Based on the I wind speed scenario information, a plurality of estimated wind speed scenario information corresponding to the wind farm at a target theoretical power is determined, wherein the estimated wind speed scenario information includes an estimated wind speed of each wind turbine.

[0018] In one embodiment, the screening from the plurality of historical samples based on the target theoretical power to obtain a valid sample includes:

[0019] Determining a power boundary interval based on the target theoretical power and a set boundary floating value;

[0020] Determine the historical samples whose theoretical power belongs to the power boundary interval as valid samples.

[0021] In one of the embodiments, determining, based on the I wind speed scenario information, a plurality of estimated wind speed scenario information corresponding to the wind farm at the target theoretical power includes:

[0022] Processing each of the wind speed scenario information based on the theoretical power model of the wind farm to obtain the theoretical power of the wind farm corresponding to each of the I wind speed scenario information, wherein the theoretical power model represents the corresponding relationship between the theoretical power of the wind farm and the wind speed of each wind turbine;

[0023] Based on the theoretical power of the wind farm corresponding to each of the I wind speed scenario information, multiple estimated wind speed scenario information corresponding to the wind farm at the target theoretical power are determined from the I wind speed scenario information, and the estimated wind speed scenario information is the theoretical power of the corresponding wind farm, which is the wind speed scenario information belonging to the power boundary interval.

[0024] In one embodiment, the method further comprises:

[0025] If in the current round, the amount of the estimated wind speed scene information obtained by executing the estimation processing step is less than L, the number of samples is increased, and the increased number of samples is used as the number of samples for executing the estimation processing step in the next round;

[0026] Execute the next round of the estimation processing steps until a preset condition is met;

[0027] Among them, L is greater than 1, and the preset condition is that a preset cycle round is reached, or the amount of the estimated wind speed scene information obtained is greater than or equal to L.

[0028] In one of the embodiments, determining the confidence interval of the equivalent frequency regulation parameter corresponding to the wind farm under the target theoretical power based on the load reduction power of the wind farm, the multiple estimated wind speed scenario information and the constructed functional relationship of the equivalent frequency regulation parameter of the wind farm includes:

[0029] Based on a preset rule, the load reduction power of the wind farm is distributed to each wind turbine in the wind farm, and a stable operating state parameter of each wind turbine in the wind farm is obtained when the load reduction power is applied;

[0030] Based on the functional relationship among the stable operating state parameters of each wind turbine set of the wind farm when the load is reduced, the multiple estimated wind speed scenario information and the constructed equivalent frequency regulation parameters of the wind farm, the confidence interval of the equivalent frequency regulation parameters corresponding to the wind farm under the target theoretical power is determined.

[0031] In a second aspect, the present application further provides a device for determining a confidence interval of an equivalent frequency modulation parameter of a wind farm, the device comprising a historical sample acquisition module, an estimated wind speed scene information determination module and a confidence interval determination module, wherein:

[0032] A historical sample acquisition module, used to acquire multiple historical samples, each of which includes the theoretical power of the wind farm and the wind speed of each wind turbine at the same historical moment;

[0033] An estimated wind speed scenario information determination module, used to determine a plurality of estimated wind speed scenario information of the wind farm under a target theoretical power based on the plurality of historical samples, the estimated wind speed scenario information including an estimated wind speed of each wind turbine;

[0034] A confidence interval determination module, configured to determine a confidence interval of an equivalent frequency modulation parameter corresponding to the wind farm under the target theoretical power based on the load reduction power of the wind farm, the plurality of estimated wind speed scenario information, and the constructed functional relationship of the equivalent frequency modulation parameter of the wind farm;

[0035] The functional relationship of the equivalent frequency regulation parameters is determined based on a frequency response model between the wind farm and the power system.

[0036] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0037] Acquire multiple historical samples, each of which includes the theoretical power of the wind farm and the wind speed of each wind turbine at the same historical moment;

[0038] Based on the multiple historical samples, determine multiple estimated wind speed scenario information of the wind farm under the target theoretical power, the estimated wind speed scenario information including the estimated wind speed of each wind turbine;

[0039] Determine a confidence interval of the equivalent frequency modulation parameter corresponding to the wind farm under the target theoretical power based on the load reduction power of the wind farm, the multiple estimated wind speed scenario information and the constructed functional relationship of the equivalent frequency modulation parameter of the wind farm;

[0040] The functional relationship of the equivalent frequency regulation parameters is determined based on a frequency response model between the wind farm and the power system.

[0041] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0042] Acquire multiple historical samples, each of which includes the theoretical power of the wind farm and the wind speed of each wind turbine at the same historical moment;

[0043] Based on the multiple historical samples, determine multiple estimated wind speed scenario information of the wind farm under the target theoretical power, the estimated wind speed scenario information including the estimated wind speed of each wind turbine;

[0044] Determine a confidence interval of the equivalent frequency modulation parameter corresponding to the wind farm under the target theoretical power based on the load reduction power of the wind farm, the multiple estimated wind speed scenario information and the constructed functional relationship of the equivalent frequency modulation parameter of the wind farm;

[0045] The functional relationship of the equivalent frequency regulation parameters is determined based on a frequency response model between the wind farm and the power system.

[0046] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0047] Acquire multiple historical samples, each of which includes the theoretical power of the wind farm and the wind speed of each wind turbine at the same historical moment;

[0048] Based on the multiple historical samples, determine multiple estimated wind speed scenario information of the wind farm under the target theoretical power, the estimated wind speed scenario information including the estimated wind speed of each wind turbine;

[0049] Determine a confidence interval of the equivalent frequency modulation parameter corresponding to the wind farm under the target theoretical power based on the load reduction power of the wind farm, the multiple estimated wind speed scenario information and the constructed functional relationship of the equivalent frequency modulation parameter of the wind farm;

[0050] The functional relationship of the equivalent frequency regulation parameters is determined based on a frequency response model between the wind farm and the power system.

[0051] The above-mentioned confidence interval determination method, device, storage medium and computer program product of the equivalent frequency modulation parameters of the wind farm comprehensively consider the frequency response characteristics of the wind farm itself and the randomness of the wind speed distribution inside the wind farm. By analyzing the historical samples of the wind farm, it is possible to predict multiple estimated wind speed scenario information corresponding to the wind farm under a target theoretical power. Through multiple estimated wind speed scenario information, combined with the functional relationship of the equivalent frequency modulation parameters, the confidence interval (credibility range) of the equivalent frequency modulation parameters of the wind farm can be determined based on a determined load reduction power; since the target theoretical power can be a predicted power in the future, the method of the present application can realize the prediction of the equivalent frequency modulation parameters of the wind farm under the future target theoretical power; and the confidence interval of the equivalent inertia and equivalent droop coefficient of the wind farm can be calculated only based on the total power of the wind farm. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0053] Figure 1 It is a flow chart of a method for determining a confidence interval of equivalent frequency regulation parameters of a wind farm in one embodiment;

[0054] Figure 2 A schematic diagram of a frequency response model of a wind turbine generator system in one embodiment;

[0055] Figure 3 is a schematic diagram of a frequency response model between a wind farm and a power system in one embodiment;

[0056] Figure 4 is a schematic diagram of an equivalent frequency modulation model of a wind farm frequency response in an embodiment;

[0057] Figure 5 A schematic diagram of a process flow for determining estimated wind speed scenario information in one embodiment;

[0058] Figure 6 A schematic diagram of a flow chart of steps for determining a confidence interval of an equivalent frequency modulation parameter in an embodiment;

[0059] Figure 7 is a schematic diagram of the connection relationship between a wind farm and a power system in an example;

[0060] Figure 8 A schematic diagram of the relationship between the theoretical power and active power of a wind farm in an example;

[0061] Fig. 9 A schematic diagram of the confidence interval of the equivalent frequency regulation parameters of a wind farm in an example;

[0062] Fig.10 It is a structural block diagram of a device for determining a confidence interval of equivalent frequency regulation parameters of a wind farm in an embodiment;

[0063] Fig.11 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0065] In an exemplary embodiment, a method for determining a confidence interval of an equivalent frequency modulation parameter of a wind farm is provided, and the method is executed by a computer device, wherein the computer device may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices, and portable wearable devices, and the Internet of Things devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, and servers, etc. The portable wearable device may be a smart watch, a smart bracelet, a head-mounted device, etc. The server may be implemented as an independent server or a server cluster consisting of multiple servers.

[0066] like Figure 1 As shown, the method for determining the confidence interval of the equivalent frequency modulation parameters of a wind farm provided in the embodiment of the present application includes the following steps 10 to 30, wherein:

[0067] Step 10: Acquire multiple historical samples, each of which includes the theoretical power of the wind farm and the wind speed of each wind turbine generator set at the same historical moment.

[0068] For the embodiment of the present application, the historical sample is the real data of the wind farm at a historical moment. The wind farm includes multiple wind turbines, wherein, when the number and position of wind turbines in the wind farm are fixed, one historical sample corresponds to one historical moment, and the historical sample includes the theoretical power of the wind farm and the wind speed of each wind turbine at the historical moment corresponding to the historical sample. Among them, the theoretical power of the wind farm represents the maximum active power that the wind farm can actually output. When other parameters are fixed, the theoretical power of the wind farm is only related to the wind speed of each wind turbine in the wind farm. At the same time, in order to maintain a certain redundancy, the wind farm will set a part of the power for storage. This part of the power is the load reduction power. Therefore, the active power actually output by the wind farm is less than the theoretical power of the wind farm.

[0069] Step 20: Based on multiple historical samples, determine multiple estimated wind speed scenario information of the wind farm under the target theoretical power, where the estimated wind speed scenario information includes the estimated wind speed of each wind turbine.

[0070] For the embodiment of the present application, since wind farms are generally selected in areas with relatively stable wind resources, the wind speed distribution law of the wind farm can be determined based on the historical samples of the wind farm; since the theoretical power of the wind farm is directly related to the wind speed of each wind turbine, the estimated wind speed scenario information of multiple wind farms can be obtained for the target theoretical power; wherein each estimated wind speed scenario information includes the estimated wind speed of each wind turbine. In other words, for each estimated wind speed scenario information, the theoretical power of the corresponding wind farm is the same and is the target theoretical power.

[0071] Step 30: Based on the load reduction power of the wind farm, multiple estimated wind speed scenario information, and the constructed functional relationship of the equivalent frequency modulation parameters of the wind farm, determine the confidence interval of the equivalent frequency modulation parameters corresponding to the wind farm under the target theoretical power, wherein the functional relationship of the equivalent frequency modulation parameters is determined based on the frequency response model between the wind farm and the power system.

[0072] In an embodiment of the present application, the frequency response model between the wind farm and the power system is related to the facility configuration of the wind farm, and the frequency response model can be established in advance, that is, the functional relationship of the equivalent frequency modulation parameters can also be predetermined. After determining the multiple estimated wind speed scenario information corresponding to the target theoretical power of the wind farm, based on the determined load reduction power and the functional relationship of the equivalent frequency modulation parameters of the wind farm, the equivalent frequency modulation parameters of the wind farm under each estimated wind speed scenario can be determined. Then, based on the equivalent frequency modulation parameters of the wind farm under each estimated wind speed scenario, the confidence interval of the equivalent frequency modulation parameters is determined.

[0073] In this embodiment, the frequency response characteristics of the wind farm itself and the randomness of the wind speed distribution inside the wind farm are comprehensively considered. By analyzing the historical samples of the wind farm, multiple estimated wind speed scenario information corresponding to the wind farm under a target theoretical power can be predicted. Through multiple estimated wind speed scenario information, combined with the functional relationship of the equivalent frequency modulation parameters, the confidence interval (credibility range) of the equivalent frequency modulation parameters of the wind farm can be determined based on a certain load reduction power; since the target theoretical power can be a predicted power in the future, the method of the present application can realize the prediction of the equivalent frequency modulation parameters of the wind farm under the future target theoretical power; and the confidence interval of the equivalent inertia and the equivalent droop coefficient of the wind farm can be calculated only based on the total power of the wind farm.

[0074] Since in the embodiments of the present application, the frequency response model and the frequency response model of the wind farm can be established in advance, here, the present application first describes the frequency response model of the wind farm and the process of establishing the frequency response model between the wind farm and the power system, and describes how to determine the functional relationship of the equivalent frequency regulation parameters of the wind farm.

[0075] A wind farm generally includes multiple wind turbines. The frequency response model of the wind farm is established based on the frequency response models of each wind turbine in the wind farm. The frequency response model of a wind turbine is shown in formula (1):

[0076] ΔP e =F(Δf|v,Υ)(1);

[0077] Among them, ΔP e is the active power change of the wind turbine, Δf is the system frequency deviation, v is the wind speed of the wind turbine, and Υ represents the frequency regulation control strategy and control parameters of the wind turbine.

[0078] Formula (1) is a general expression of the frequency response model of a wind turbine generator set, which is applicable to various control strategies. Its specific form can be determined by the actual frequency regulation control strategy and control parameter γ of the wind turbine generator set, and is not specifically limited in the embodiments of the present application.

[0079] In a possible implementation, the present application embodiment provides a specific example of a frequency response model of a wind turbine generator set to specifically illustrate the principle of formula (1). Figure 2 As shown, it is a structural diagram of the frequency response model of a wind turbine, which specifically includes wind energy capture, rotation equation, power tracking, pitch angle control and frequency support.

[0080] The parameter relationship in the wind energy capture link is shown in formula (2):

[0081] P m (ω,β,v)=k m C p (ω,β)v 3 (2);

[0082] Among them, P m is the mechanical energy captured by the wind turbine impeller, ω is the impeller speed, β is the pitch angle, v is the wind speed, C p is the wind energy utilization coefficient, C p The calculation method is shown in formula (2):

[0083]

[0084] Among them, k m is the power coefficient, R is the radius of the wind turbine rotor, c 1 ~c 8 and σ are related parameters.

[0085] The relationship between the parameters in the rotation equation is expressed by formula (4), where:

[0086]

[0087] Among them, P e is the active power of the wind turbine, J WTG is the time constant of the wind turbine rotor inertia.

[0088] The relationship between the parameters in the power tracking link is shown in formula (5) to formula (7), where:

[0089]

[0090] Among them, ω MPPTis the wind turbine maximum power point tracking (MPPT) speed, P MPPT is the maximum power tracking power of the wind turbine, k MPPT Maximum power tracking power coefficient of wind turbine; P e,ref is the reference value of electromagnetic power of wind turbine, η de is the load reduction rate of the wind turbine, ω init and P init are the target speed and target power respectively.

[0091] In the pitch angle control link, the relationship between the parameters is shown in formula (8), where:

[0092] β * =P PI (ω-ω max )+I Pi ∫(ω-ω max ) (8);

[0093] Among them, β * is the pitch angle reference value, ω max is the upper limit of the impeller speed, P PI and I PI are the proportional coefficient and integral coefficient of the proportional-integral controller respectively.

[0094] The relationship between the parameters in the frequency support link is expressed as formula (9) and formula (10), where:

[0095]

[0096] Among them, ΔP e,ref is the reference power added value, ω min is the lower limit of the impeller speed, H W is the wind turbine inertia response control coefficient, K W is the droop response control coefficient of the wind turbine, and Δf is the system frequency deviation value.

[0097] The above formulas (2) to (10) represent a specific example of the frequency response model of a wind turbine. Combined with formula (1), assuming that the number of wind turbines in the wind farm is N, then for the nth wind turbine, its frequency response model can be expressed by formula (11), where:

[0098] ΔP e,n =F n (Δf|v n ,Υ n ) (11);

[0099] The power of the wind farm is the sum of the power values ​​of each wind turbine. The frequency response model of the wind farm is expressed by formula (12), where:

[0100]

[0101] Where n is the serial number of the wind turbine in the wind farm, n∈(1,2,3......N), ΔP e,WF is the active power change of the wind farm.

[0102] The frequency response model of the power system including the wind farm, that is, the frequency response model between the wind farm and the power system is as follows: Figure 3 As shown, and expressed by formula (13) and formula (14), where:

[0103]

[0104] Among them, ΔP IM is the unbalanced power of the power system, ΔP G is the power change of the synchronous machine, H G , K G are the synchronous machine inertia time constant and active frequency droop coefficient, T G is the time constant of the speed regulator of the power system, K L is the active power-frequency droop coefficient of the load.

[0105] Furthermore, using the equivalent inertia H eq and equivalent droop coefficient K eq The aggregate equivalent frequency modulation parameters describing the frequency response of the wind farm are as follows: Figure 4 shown.

[0106] Setting ΔP IM For a step power disturbance, the maximum value of the rate of change of frequency (ROCOF) and the lowest point of the frequency drop after the step power disturbance are key indicators for measuring the frequency dynamic response characteristics. Figure 3 The frequency response model of the medium wind farm has the maximum and minimum values ​​of ROCOF in low frequency events, respectively. det and f nadir,det ; Figure 4 In the equivalent parameter model, the maximum value of ROCOF is ROCOF agg The minimum frequency is f nadir,agg The calculation principle of the equivalent frequency modulation parameters of the wind farm frequency response is: the maximum frequency change rate and the lowest frequency point of the system after the wind farm is represented by the equivalent parameter model are consistent with those when the wind farm is represented by the detailed model, as shown in formula (15), where:

[0107]

[0108] The detailed model of the wind farm in the embodiment of the present application is the parameter relationship represented by formula (12).

[0109] The equivalent frequency modulation parameters specifically include the equivalent inertia H eq and equivalent droop coefficient K eq , can be expressed by formula group (16), where:

[0110]

[0111] Among them, ROCOF det,0～1 and f nadir,det They are Figure 3 The frequency response model between the wind farm and the power system shown in the figure includes the average frequency change rate and the lowest frequency drop point within 0 to 1 second after the power system accident occurs, which can be simulated by MATLAB (Matrix Laboratory) software; ROCOF agg,0～1 and f nadir,agg They are Figure 4 The equivalent parameter model of the wind farm frequency response shown in the figure shows the average frequency change rate and the lowest frequency drop point within 0 to 1 second after the power system accident; RO and FN are error terms respectively. The average frequency change rate and the lowest frequency drop point can be expressed as the equivalent frequency modulation parameter (equivalent inertia H eq and equivalent droop coefficient K eq ) function.

[0112] Newton's method is used to solve the formula group (16), and the Jacobian matrix of the formula group (16) is constructed as shown in formula (17), where:

[0113]

[0114] Given an initial value and By iterating the matrix (18) along the reverse direction of the gradient, the next point is found, and it is iterated step by step until the error value of two adjacent solutions meets the convergence condition. eq and K eq . Among them, the matrix (18) is:

[0115]

[0116] The above formula group (16) is the pre-established functional relationship of the equivalent frequency modulation parameters, and the matrix (17) and the matrix (18) are the construction matrices required to solve the formula group (16).

[0117] Furthermore, in one of the embodiments, step 20 may specifically include a process of determining boundary conditions, a process of fitting the marginal distribution of wind speed, a process of t-Copula fitting and scene generation, and a process of effective scene screening.

[0118] The process of determining boundary conditions:

[0119] The boundary conditions are determined based on the target theoretical power, where the target theoretical power is input by the user. In one scenario, the target theoretical power can be the predicted theoretical power of the wind farm in the future, or the power required by the power system to be input by the wind farm.

[0120] The wind speed in the actual environment is random and there is no deterministic rule to follow. Considering the randomness of wind speed, the wind speed of the nth wind turbine in the wind farm is a random variable v n , the theoretical power of the wind farm is a random variable Θ. Among them, the number of historical samples is M, that is, there are M groups v n and Θ, each set of historical samples is the measurement value of each state quantity by the monitoring system of the wind farm at the same historical moment. n The sample value at time m is v n,m ; The theoretical power Θ of the wind farm at time m is sampled as θ m In order to generate wind speed scenario information (wind speed of each wind turbine in the wind farm) when the total theoretical power of the wind farm is θ, the boundary condition θ=θ needs to be given in advance.

[0121] The process of fitting the marginal distribution of wind speed may specifically include: screening from multiple historical samples based on the target theoretical power to obtain valid samples; and constructing a marginal cumulative distribution function characterizing the wind speed of each wind turbine based on the wind speed of each wind turbine in each valid sample.

[0122] Specifically, based on the target theoretical power and the set boundary floating value, a power boundary interval is determined, and then historical samples whose theoretical power belongs to the power boundary interval are determined as valid samples.

[0123] To satisfy the condition Θ = θ, the samples with Θ near θ are called valid samples of the conditional distribution, so it is necessary to screen the multiple historical samples obtained. θ Indicates that the condition θ is satisfied m ∈[θ-Δθ,θ+Δθ] is the numbered set of historical samples, as shown in formula (19). Δθ is the boundary floating value, [θ-Δθ,θ+Δθ] is the power margin interval; when fitting the wind speed marginal distribution, the solution that satisfies m∈M is adopted. θ The samples are regarded as valid samples.

[0124] Μ θ={m|θ-Δθ≤θ m ≤θ+Δθ,m=1,2,...,M} (19).

[0125] Since the theoretical power of the wind farm and the wind speed distribution of the wind turbine are unknown, in the embodiment of the present application, the non-parametric Gaussian kernel density estimation method is used to construct the wind speed distribution function of the wind turbine. Under the condition that the theoretical power of the wind farm Θ = θ, the wind speed v n In n The probability density function at can be expressed by formula (20), where:

[0126]

[0127] Where h is the bandwidth of the Gaussian kernel density estimate, and card() represents the number of elements in the set;

[0128] The marginal cumulative distribution function of the wind speed of the wind turbine can be calculated by formula (21), where:

[0129]

[0130] The process of t-Copula fitting and scenario generation includes: based on the connection function, the marginal cumulative distribution function of the wind speed of each wind turbine is connected in parallel to obtain the joint distribution function of the wind speed in the wind farm; performing an estimation processing step on the joint distribution function to determine multiple estimated wind speed scenario information of the wind farm under the target theoretical power.

[0131] Specifically, from the above formulas (20) and (21), the wind speed distribution function of the wind turbine can be obtained as follows: The distribution function is simplified to cdf |Θ (v n ). According to Sklar's theorem of conditional distribution, when N random variables v 1 ,v 2 ,...,v N The marginal conditional distribution function cdf |Θ (v n ) is continuous, its joint conditional distribution function cdf |Θ (v 1 ,v 2 ,...,v N )=Copula |Θ (cdf |Θ (v 1 ),cdf |Θ (v 2 )...,cdf |Θ (v N )) can be expressed as shown in formula (22), where:

[0132] cdf |Θ (v 1 ,v 2 ,...,v N )=Copula |Θ (cdf |Θ (v 1 ),cdf |Θ (v 2 )...,cdf |Θ (v N )) (twenty two).

[0133] Furthermore, the t-Copula connection function is used to characterize the correlation relationship of the marginal distribution of wind speed of each wind turbine, and the cdf |Θ (v n ) is abbreviated as u n , the t-Copula connection function is shown in formula (23):

[0134]

[0135] Among them, T ∑,v represents the cumulative distribution function of the t distribution with a correlation matrix of ∑ and v degrees of freedom, is the inverse function of the probability density function of the t distribution with v degrees of freedom.

[0136] Specifically, the estimation processing step includes: sampling the joint distribution function to obtain I wind speed scene information, the wind speed scene information includes the wind speed of each wind turbine in the wind farm, wherein I is the sampling number, and I is greater than 1.

[0137] Based on I wind speed scenario information, multiple estimated wind speed scenario information corresponding to the wind farm at the target theoretical power is determined, and the estimated wind speed scenario information includes the estimated wind speed of each wind turbine.

[0138] The correlation matrix ∑ and degrees of freedom v of t-Copula are fitted by the maximum likelihood estimation method as shown in formula (23). The fitted t-Copula distribution function is randomly sampled to generate I samples, that is, I wind speed scene information. Each sample i is restored to the wind speed scene by fitting the wind speed marginal distribution That is, wind speed scene distribution information, where i represents the sequence number of the wind speed scene information, where i∈(1......I), I>1.

[0139] Furthermore, not all of the I wind speed scene information obtained are valid, and therefore, a step of valid scene screening needs to be further performed. Among them, the estimated wind speed scene information is the available wind speed scene determined from the I wind speed scene information.

[0140] The process of effective scene screening:

[0141] Specifically, for each wind speed scenario information The wind farm's wind speed scenario information is obtained through the theoretical power model of the wind farm. The corresponding theoretical power Specifically, the theoretical model calculation formula of the wind farm can be expressed by formula (24), where:

[0142]

[0143] Among them, v ci is the cut-in wind speed, v rated is the rated wind speed, v co is the cut-out wind speed, P m is the mechanical energy captured by the impeller of the wind turbine, ω min is the lower limit of the impeller speed, ω max is the upper limit of the impeller speed, P MPPT is the maximum power tracking power of the wind turbine, v is the speed of the wind turbine, ω is the speed of the impeller, and β is the pitch angle.

[0144] Due to the randomness of scenario generation, it is impossible to guarantee that the theoretical power of the wind farm under each wind speed scenario information generated by the t-Copula method is the target theoretical power θ. Therefore, when the theoretical power of the wind farm corresponding to the wind speed scenario information i satisfies When , the wind speed scenario information is considered to be valid; that is, the wind farm corresponds to the theoretical power under the estimated wind speed scenario information and belongs to the power boundary interval [θ-Δθ,θ+Δθ].

[0145] Furthermore, if it is determined that the number of estimated wind speed scene information obtained is less than L, it will affect the accuracy of the final confidence interval. Therefore, if in the current round, the number of estimated wind speed scene information obtained by executing the estimation processing step is less than L, that is, the number of samples I is increased, and the increased number of samples is used as the number of samples for executing the next round of estimation processing steps; and the next round of estimation processing steps is executed until the preset condition is met; wherein, L is greater than 1, and the preset condition is to reach the preset cycle round, or the number of estimated wind speed scene information obtained is greater than or equal to L. The specific value of L is not specifically limited in the embodiments of the present application, but L is less than or equal to I.

[0146] Through the above step 20, L groups of estimated wind speed distribution information satisfying the situation that the theoretical power of the wind farm is the target theoretical power can be generated.

[0147] Further, if Figure 6 As shown, step 30 may specifically include step 31 and step 32:

[0148] Step 31: Based on a preset rule, the load reduction power of the wind farm is distributed to each wind turbine in the wind farm, and stable operation state parameters of each wind turbine when the wind farm is under load reduction are obtained;

[0149] Step 31: Determine the confidence interval of the equivalent frequency regulation parameter corresponding to the target theoretical power of the wind farm based on the functional relationship among the stable operating state parameters of each wind turbine set when the wind farm is load-reduced, multiple estimated wind speed scenario information, and the constructed equivalent frequency regulation parameter of the wind farm.

[0150] Specifically, for each set of estimated wind speed scenario information l, the load reduction power P de,WF According to the preset rules of the wind farm, the wind turbines in the wind farm are allocated to each wind turbine in the wind farm, and the steady-state operation state parameters of each wind turbine in the wind farm under each estimated wind speed scenario information are obtained. Among them, the steady-state operation state parameters may include pitch angle and moment of inertia. l∈(1......L).

[0151] Furthermore, based on the steady-state operating parameters of each wind turbine in the wind farm under each estimated wind speed scenario information, the above-established formula group (16) is used, and combined with matrix (17) and matrix (18) for calculation, the equivalent frequency modulation parameter H of the wind farm under each estimated wind speed distribution information is obtained. eq and K eq .

[0152] Then the kernel density method is used to estimate H eq and K eq The probability density function of is obtained by integrating it and obtaining the cumulative distribution function of the equivalent frequency modulation parameter.

[0153] Furthermore, given a confidence level α, find the equivalent parameter H eq and K eq The cumulative distribution function of the variable at the values ​​of α / 2 and 1-α / 2 is recorded as and It is used as the boundary of the confidence interval. eq and K eq The confidence intervals are Π H and Π K , where the confidence interval of the equivalent frequency modulation parameter is expressed as shown in formula group (25):

[0154]

[0155] Among them, P MPPT,WF is the target theoretical power, P de,WF To reduce load power.

[0156] Wind farm equivalent inertia H eqand droop coefficient K eq The quantitative evaluation results can provide an important reference for the dispatching department to understand the frequency response characteristics of the wind farm and coordinate various frequency regulation resources within the system.

[0157] Using the above method, a confidence interval of the equivalent frequency modulation parameter of a wind farm containing 20 wind turbines is calculated as an example, where the connection relationship between the wind farm and the power system is as follows: Figure 7 shown.

[0158] Figure 8 express Figure 7 The relationship between the theoretical power and active power of a wind farm in a typical day is shown in Figure 1. de,WF is the load reduction power, P MPPT,WF is the theoretical power, P MPPT,WF -P de,WF is the active power.

[0159] Fig. 9 In wind farms Figure 8 Under the corresponding relationship between theoretical power and active power, the equivalent inertia H of the wind farm is eq and droop coefficient K eq Confidence interval of .

[0160] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0161] Based on the same inventive concept, the embodiment of the present application also provides a device for determining the confidence interval of equivalent frequency modulation parameters of a wind farm for implementing the above-mentioned method for determining the confidence interval of equivalent frequency modulation parameters of a wind farm. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of one or more devices for determining the confidence interval of equivalent frequency modulation parameters of a wind farm provided below can refer to the limitations of the method for determining the confidence interval of equivalent frequency modulation parameters of a wind farm above, and will not be repeated here.

[0162] In an exemplary embodiment, Fig.10As shown, a device for determining a confidence interval of equivalent frequency modulation parameters of a wind farm is provided, the device comprising a historical sample acquisition module 1001, an estimated wind speed scene information determination module 1002 and a confidence interval determination module 1003, wherein:

[0163] A historical sample acquisition module 1001 is used to acquire multiple historical samples, each of which includes the theoretical power of the wind farm and the wind speed of each wind turbine at the same historical moment;

[0164] An estimated wind speed scenario information determination module 1002 is used to determine a plurality of estimated wind speed scenario information of a wind farm under a target theoretical power based on a plurality of historical samples, wherein the estimated wind speed scenario information includes an estimated wind speed of each wind turbine;

[0165] The confidence interval determination module 1003 is used to determine the confidence interval of the equivalent frequency regulation parameter corresponding to the wind farm under the target theoretical power based on the load reduction power of the wind farm, multiple estimated wind speed scenario information and the constructed functional relationship of the equivalent frequency regulation parameter of the wind farm;

[0166] Among them, the functional relationship of the equivalent frequency regulation parameters is determined based on the frequency response model between the wind farm and the power system.

[0167] The present application comprehensively considers the frequency response characteristics of the wind farm itself and the randomness of the wind speed distribution inside the wind farm. By analyzing the historical samples of the wind farm, it is possible to predict multiple estimated wind speed scenario information corresponding to the wind farm under a target theoretical power. Through multiple estimated wind speed scenario information, combined with the functional relationship of the equivalent frequency modulation parameters, the confidence interval (credibility range) of the equivalent frequency modulation parameters of the wind farm can be determined based on a certain load reduction power; since the target theoretical power can be a predicted power in the future, the method of the present application can realize the prediction of the equivalent frequency modulation parameters of the wind farm under the future target theoretical power; and the confidence interval of the equivalent inertia and equivalent droop coefficient of the wind farm can be calculated only based on the total power of the wind farm.

[0168] In one embodiment, the estimated wind speed scene information determination module 1002 is specifically used to:

[0169] Filter multiple historical samples based on target theoretical power to obtain valid samples;

[0170] Based on the wind speed of each wind turbine in each valid sample, a marginal cumulative distribution function is constructed to characterize the wind speed of each wind turbine.

[0171] Based on the connection function, the marginal cumulative distribution function of the wind speed of each wind turbine is connected in parallel to obtain the joint distribution function of the wind speed in the wind farm;

[0172] An estimation processing step is performed on the joint distribution function to determine multiple estimated wind speed scenario information of the wind farm under the target theoretical power. The estimation processing step includes:

[0173] Sampling the joint distribution function to obtain I wind speed scenario information, where the wind speed scenario information includes the wind speed of each wind turbine in the wind farm, where I is the number of samples and I is greater than 1;

[0174] Based on I wind speed scenario information, multiple estimated wind speed scenario information corresponding to the wind farm at the target theoretical power is determined, and the estimated wind speed scenario information includes the estimated wind speed of each wind turbine.

[0175] In one embodiment, the estimated wind speed scene information determination module 1002 is specifically used to:

[0176] Determine the power boundary interval based on the target theoretical power and the set boundary floating value;

[0177] It is determined that historical samples whose theoretical power belongs to the power boundary interval are valid samples.

[0178] In one embodiment, the estimated wind speed scene information determination module 1002 is specifically used to:

[0179] Based on the theoretical power model of the wind farm, each wind speed scenario information is processed to obtain the theoretical power of the wind farm corresponding to each wind speed scenario information. The theoretical power model represents the corresponding relationship between the theoretical power of the wind farm and the wind speed of each wind turbine;

[0180] Based on the theoretical power of the wind farm corresponding to each of the I wind speed scenario information, multiple estimated wind speed scenario information corresponding to the wind farm at the target theoretical power are determined from the I wind speed scenario information. The estimated wind speed scenario information is the theoretical power of the corresponding wind farm, which is the wind speed scenario information within the power boundary interval.

[0181] In one embodiment, the estimated wind speed scene information determination module 1002 is further used to:

[0182] If the amount of estimated wind speed scene information obtained by executing the estimation processing step in the current round is less than L, the number of samples is increased, and the increased number of samples is used as the number of samples for executing the next round of estimation processing steps;

[0183] Execute the next round of estimated processing steps until the preset conditions are met;

[0184] Wherein, L is greater than 1, and the preset condition is that a preset cycle round is reached, or the amount of estimated wind speed scene information obtained is greater than or equal to L.

[0185] In one embodiment, the confidence interval determination module 1003 is specifically configured to:

[0186] Based on preset rules, the load reduction power of the wind farm is distributed to each wind turbine in the wind farm, and the stable operating state parameters of each wind turbine when the wind farm is under load reduction are obtained;

[0187] Based on the functional relationship among the stable operating state parameters of each wind turbine set when the wind farm is reducing load power, multiple estimated wind speed scenario information and the constructed equivalent frequency regulation parameters of the wind farm, the confidence interval of the equivalent frequency regulation parameters corresponding to the target theoretical power of the wind farm is determined.

[0188] Each module in the above-mentioned device for determining the confidence interval of equivalent frequency modulation parameters of a wind farm can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0189] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Fig.11 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a confidence interval determination method for equivalent frequency modulation parameters of a wind farm is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0190] Those skilled in the art will understand that Fig.11The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0191] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0192] Acquire multiple historical samples, each historical sample including the theoretical power of the wind farm and the wind speed of each wind turbine at the same historical moment;

[0193] Based on multiple historical samples, multiple estimated wind speed scenario information of the wind farm under target theoretical power is determined, where the estimated wind speed scenario information includes an estimated wind speed of each wind turbine;

[0194] Based on the load reduction power of the wind farm, multiple estimated wind speed scenario information and the functional relationship of the constructed equivalent frequency regulation parameters of the wind farm, the confidence interval of the equivalent frequency regulation parameters corresponding to the wind farm under the target theoretical power is determined;

[0195] Among them, the functional relationship of the equivalent frequency regulation parameters is determined based on the frequency response model between the wind farm and the power system.

[0196] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0197] Acquire multiple historical samples, each historical sample including the theoretical power of the wind farm and the wind speed of each wind turbine at the same historical moment;

[0198] Based on multiple historical samples, multiple estimated wind speed scenario information of the wind farm under target theoretical power is determined, where the estimated wind speed scenario information includes an estimated wind speed of each wind turbine;

[0199] Based on the load reduction power of the wind farm, multiple estimated wind speed scenario information and the functional relationship of the constructed equivalent frequency regulation parameters of the wind farm, the confidence interval of the equivalent frequency regulation parameters corresponding to the wind farm under the target theoretical power is determined;

[0200] Among them, the functional relationship of the equivalent frequency regulation parameters is determined based on the frequency response model between the wind farm and the power system.

[0201] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0202] Acquire multiple historical samples, each historical sample including the theoretical power of the wind farm and the wind speed of each wind turbine at the same historical moment;

[0203] Based on multiple historical samples, multiple estimated wind speed scenario information of the wind farm under target theoretical power is determined, where the estimated wind speed scenario information includes an estimated wind speed of each wind turbine;

[0204] Based on the load reduction power of the wind farm, multiple estimated wind speed scenario information and the functional relationship of the constructed equivalent frequency regulation parameters of the wind farm, the confidence interval of the equivalent frequency regulation parameters corresponding to the wind farm under the target theoretical power is determined;

[0205] Among them, the functional relationship of the equivalent frequency regulation parameters is determined based on the frequency response model between the wind farm and the power system.

[0206] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0207] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0208] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0209] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A confidence interval determination method for equivalent frequency modulation parameters of wind farms. It is characterized in that The method comprises: Acquire multiple historical samples, each of which includes the theoretical power of the wind farm and the wind speed of each wind turbine at the same historical moment; Based on the multiple historical samples, determine multiple estimated wind speed scenario information of the wind farm under the target theoretical power, the estimated wind speed scenario information including the estimated wind speed of each wind turbine; Determine a confidence interval of the equivalent frequency modulation parameter corresponding to the wind farm under the target theoretical power based on the load reduction power of the wind farm, the multiple estimated wind speed scenario information and the constructed functional relationship of the equivalent frequency modulation parameter of the wind farm; The functional relationship of the equivalent frequency regulation parameters is determined based on a frequency response model between the wind farm and the power system.

2. The method according to claim 1, It is characterized in that The determining, based on the multiple historical samples, multiple estimated wind speed scenario information of the wind farm under the target theoretical power includes: Screening the plurality of historical samples based on the target theoretical power to obtain a valid sample; Based on the wind speed of each wind turbine in each of the valid samples, a marginal cumulative distribution function is constructed to characterize the wind speed of each wind turbine; Based on the connection function, the marginal cumulative distribution function of the wind speed of each wind turbine is connected in parallel to obtain a joint distribution function of the wind speed in the wind farm; Performing an estimation processing step on the joint distribution function to determine multiple estimated wind speed scenario information of the wind farm under the target theoretical power, the estimation processing step comprising: Sampling the joint distribution function to obtain I wind speed scenario information, where the wind speed scenario information includes the wind speed of each wind turbine in the wind farm, where I is the number of samples and I is greater than 1; Based on the I wind speed scenario information, a plurality of estimated wind speed scenario information corresponding to the wind farm at a target theoretical power is determined, wherein the estimated wind speed scenario information includes an estimated wind speed of each wind turbine.

3. The method according to claim 2, It is characterized in that The step of screening the plurality of historical samples based on the target theoretical power to obtain a valid sample comprises: Determining a power boundary interval based on the target theoretical power and a set boundary floating value; Determine the historical samples whose theoretical power belongs to the power boundary interval as valid samples.

4. The method according to claim 3, It is characterized in that The determining, based on the I wind speed scenario information, a plurality of estimated wind speed scenario information corresponding to the wind farm at the target theoretical power includes: Processing each of the wind speed scenario information based on the theoretical power model of the wind farm to obtain the theoretical power of the wind farm corresponding to each of the I wind speed scenario information, wherein the theoretical power model represents the corresponding relationship between the theoretical power of the wind farm and the wind speed of each wind turbine; Based on the theoretical power of the wind farm corresponding to each of the I wind speed scenario information, multiple estimated wind speed scenario information corresponding to the wind farm at the target theoretical power are determined from the I wind speed scenario information, and the estimated wind speed scenario information is the theoretical power of the corresponding wind farm, which is the wind speed scenario information belonging to the power boundary interval.

5. The method according to claim 4, It is characterized in that The method further comprises: If in the current round, the amount of the estimated wind speed scene information obtained by executing the estimation processing step is less than L, the number of samples is increased, and the increased number of samples is used as the number of samples for executing the estimation processing step in the next round; Execute the next round of the estimation processing steps until a preset condition is met; Among them, L is greater than 1, and the preset condition is that a preset cycle round is reached, or the amount of the estimated wind speed scene information obtained is greater than or equal to L.

6. The method according to any one of claims 1 to 5, It is characterized in that The step of determining a confidence interval of the equivalent frequency modulation parameter corresponding to the wind farm under the target theoretical power based on the load reduction power of the wind farm, the multiple estimated wind speed scenario information, and the constructed functional relationship of the equivalent frequency modulation parameter of the wind farm comprises: Based on a preset rule, the load reduction power of the wind farm is distributed to each wind turbine in the wind farm, and a stable operating state parameter of each wind turbine in the wind farm is obtained when the load reduction power is applied; Based on the functional relationship among the stable operating state parameters of each wind turbine set of the wind farm when the load is reduced, the multiple estimated wind speed scenario information and the constructed equivalent frequency regulation parameters of the wind farm, the confidence interval of the equivalent frequency regulation parameters corresponding to the wind farm under the target theoretical power is determined.

7. A device for determining a confidence interval of equivalent frequency modulation parameters of a wind farm, It is characterized in that The device includes a historical sample acquisition module, an estimated wind speed scene information determination module and a confidence interval determination module, wherein: A historical sample acquisition module, used to acquire multiple historical samples, each of which includes the theoretical power of the wind farm and the wind speed of each wind turbine at the same historical moment; An estimated wind speed scenario information determination module, used to determine a plurality of estimated wind speed scenario information of the wind farm under a target theoretical power based on the plurality of historical samples, the estimated wind speed scenario information including an estimated wind speed of each wind turbine; A confidence interval determination module, configured to determine a confidence interval of an equivalent frequency modulation parameter corresponding to the wind farm under the target theoretical power based on the load reduction power of the wind farm, the plurality of estimated wind speed scenario information, and the constructed functional relationship of the equivalent frequency modulation parameter of the wind farm; The functional relationship of the equivalent frequency regulation parameters is determined based on a frequency response model between the wind farm and the power system.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program. It is characterized in that When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, It is characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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