Method and system for determining lower limit of data sampling frequency of wind farm grid-connected performance supervision

Abstract

The present application relates to the technical field of data sampling frequency determination, and provides a data sampling frequency lower limit determination method and system for grid-related performance supervision of a wind farm, comprising: obtaining a data segment of actual power generation set value and actual power generation related to grid-related performance of the wind farm; estimating noise variance of the actual power generation based on the data segment of the actual power generation; establishing a dynamic model of the wind farm based on the data segment of the actual power generation set value and the actual power generation as input; based on the noise variance of the actual power generation and the dynamic model of the wind farm, performing several simulations, each simulation obtaining performance index estimation values under multiple different sampling frequencies; at each sampling frequency, obtaining a coefficient of variation based on the performance index estimation values obtained in all simulations; selecting a sampling frequency corresponding to a coefficient of variation meeting a condition as a sampling frequency lower limit. The present application overcomes the problem of excessively high data sampling frequency determined by existing methods and lays a foundation for technical supervision data transmission under limited bandwidth.

Description

Technical Field

[0001] This invention belongs to the technical field of data sampling frequency determination, and particularly relates to a method and system for determining the lower limit of data sampling frequency for monitoring the grid connection performance of wind farms. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] With rapid economic development and accelerated industrialization and urbanization, energy demand is growing rapidly, and the contradiction between energy supply and demand is becoming increasingly prominent. Developing wind power is of great significance for alleviating energy shortages and achieving sustainable development. Although wind power has many advantages such as being clean, pollution-free, and renewable, it also has inherent drawbacks such as volatility and intermittency. Large-scale wind power grid connection poses challenges to the stable operation of the power grid. To ensure the stable operation of the power grid, the grid's requirements for the grid connection performance of wind power are constantly increasing, and various technical supervision and management measures have been successively introduced to ensure the quality of wind power grid connection.

[0004] Because wind farms typically consist of numerous turbines and are located in remote areas, the number of measurement points requiring data transmission is substantial during technical supervision. For example, to meet current technical supervision needs, approximately 120 measurement points are required per turbine. Assuming a wind farm design of 100 turbines, this translates to a total of 12,000 measurement points. With floating-point data and a sampling frequency of 100Hz, the hourly data transmission volume during technical supervision reaches approximately 1.65GB. Furthermore, the dedicated network connecting the wind farm and the production management area has limited bandwidth. It must handle not only technical supervision data transmission but also the transmission of various other information such as production scheduling and technical reports. Excessively high data sampling frequencies can severely burden network communication, potentially causing congestion. Therefore, it is necessary to research a method for determining the lower limit of the wind farm data sampling frequency, targeting the specific needs of technical supervision.

[0005] Currently, wind farm grid connection performance monitoring indicators can be broadly categorized into statistical indicators and dynamic performance indicators. Statistical indicators include the completeness and accuracy of the theoretical and actual available power of the wind farm, and the accuracy of short-term and ultra-short-term power predictions. Dynamic performance indicators include the primary frequency regulation performance of the wind farm. Statistical indicators have lower requirements for data sampling frequency, while dynamic performance indicators have higher requirements.

[0006] Currently, the main method for determining the data sampling frequency is based on Shannon's sampling theorem. However, the measurements at most points in a wind farm contain not only measurement noise but also random disturbances with unknown characteristics. The sampling frequency obtained based on Shannon's sampling theorem is much higher than the technical supervision requirements, leading to the problem of unilaterally pursuing high-frequency sampling data in actual engineering, which actually causes great difficulties in data transmission. Summary of the Invention

[0007] To address the technical problems mentioned above, this invention provides a method and system for determining the lower limit of data sampling frequency for wind farm grid connection performance monitoring. This overcomes the problem of excessively high data sampling frequencies determined by existing methods, laying the foundation for data transmission for technical monitoring under limited bandwidth.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] The first aspect of the present invention provides a method for determining the lower limit of data sampling frequency for monitoring the grid connection performance of wind farms, comprising:

[0010] Obtain the data segment of the actual generated power setpoint and the actual generated power related to the grid connection performance of the wind farm;

[0011] Estimate the noise variance of the actual power output using the data segment of the actual power output;

[0012] A dynamic model of a wind farm is established by taking the data segment of the actual generated power setpoint as input and the data segment of the actual generated power as output.

[0013] Based on the noise variance of the actual generated power and the dynamic model of the wind farm, several simulations were performed, and each simulation yielded multiple performance index estimates at different sampling frequencies.

[0014] At each sampling frequency, a coefficient of variation is obtained based on the performance index estimates obtained from all simulations;

[0015] Select the sampling frequency corresponding to the coefficient of variation that meets the conditions as the lower limit of the sampling frequency.

[0016] Furthermore, the simulation steps include:

[0017] Based on the noise variance of actual generated power, the measurement noise of the wind farm dynamic model is constructed.

[0018] The simulation input signal is constructed, and the simulation output signal is obtained by combining the measurement noise and the dynamic model of the wind farm.

[0019] At each sampling frequency, the simulated input signal and the simulated output signal are sampled separately to obtain the sample sequence of the simulated input signal and the sample sequence of the simulated output signal. Combined with the estimation method of technical supervision performance index, the estimated value of the performance index is obtained.

[0020] Furthermore, the measurement noise follows a normal distribution with a mathematical expectation of zero and a variance equal to the actual power output.

[0021] Furthermore, the sampling frequency is obtained by traversing a given frequency range from small to large with a fixed step size.

[0022] Furthermore, the coefficient of variation at a certain sampling frequency is: the ratio of the standard deviation of the performance index estimates obtained from all simulations at that sampling frequency to the mean of the performance index estimates obtained from all simulations at that sampling frequency.

[0023] Furthermore, the lower limit of the sampling frequency is:

[0024]

[0025] Among them, f i For the i-th sampling frequency, c v (i) is the coefficient of variation at the i-th sampling frequency, and α% is the threshold.

[0026] Furthermore, the dynamic model of the wind farm is as follows:

[0027]

[0028] Where s represents the input to the wind farm dynamic model, and the parameter ω n ζ and τ are obtained through the least squares identification method.

[0029] A second aspect of the present invention provides a system for determining the lower limit of data sampling frequency for monitoring the grid connection performance of wind farms, comprising:

[0030] The data acquisition module is configured to acquire data segments of actual generated power setpoints and actual generated power related to the grid connection performance of wind farms;

[0031] The noise variance estimation module is configured to estimate the noise variance of the actual power based on the data segment of the actual power.

[0032] The model building module is configured to: take a data segment of the actual generated power setpoint as input and take a data segment of the actual generated power as output to build a dynamic model of the wind farm;

[0033] The simulation module is configured to perform several simulations based on the noise variance of the actual generated power and the dynamic model of the wind farm, and each simulation yields multiple performance index estimates at different sampling frequencies.

[0034] The coefficient of variation calculation module is configured to obtain a coefficient of variation based on the performance index estimates obtained from all simulations at each sampling frequency.

[0035] The selection module is configured to select the sampling frequency corresponding to the coefficient of variation that meets the conditions, as the lower limit of the sampling frequency.

[0036] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the method for determining the lower limit of the data sampling frequency for monitoring the grid connection performance of wind farms as described above.

[0037] A fourth aspect of the present invention provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the method for determining the lower limit of the data sampling frequency for monitoring the grid connection performance of wind farms as described above.

[0038] Compared with the prior art, the beneficial effects of the present invention are:

[0039] The present invention provides a method for determining the lower limit of data sampling frequency for wind farm grid connection performance monitoring. Based on the dynamic model of active power response of wind farm, it uses simulation method to determine the lower limit of data sampling frequency for wind farm grid connection performance monitoring. This method provides technical support for data collection aimed at technical supervision, overcomes the problem of excessively high data sampling frequency determined by existing methods, and lays the foundation for technical supervision data transmission under limited bandwidth.

[0040] The method for determining the lower limit of data sampling frequency for wind farm grid connection performance monitoring provided by this invention only requires a portion of the actual generated power setpoint and high-frequency actual generated power data of the wind farm during its application. It has significant advantages in terms of low application complexity and wide applicability, and is of great significance for carrying out extensive and in-depth technical supervision of wind farms.

[0041] The method for determining the lower limit of data sampling frequency for wind farm grid connection performance monitoring provided by this invention can effectively reduce the amount of data to be transmitted while meeting the technical supervision requirements. Attached Figure Description

[0042] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0043] Figure 1 This is a flowchart of the method for determining the lower limit of data sampling frequency for monitoring the grid connection performance of wind farms according to Embodiment 1 of the present invention;

[0044] Figure 2 This is the high-frequency sampling data segment of Embodiment 1 of the present invention. and Trend curve;

[0045] Figure 3 This is a schematic diagram of the simulation scheme of Embodiment 1 of the present invention;

[0046] Figure 4 The rise time t of the first frequency modulation in Embodiment 1 of the present invention r The trend graph of the coefficient of variation of the estimated results. Detailed Implementation

[0047] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0048] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0049] Example 1

[0050] In order to reduce the amount of data transmitted during the technical supervision process while meeting the technical supervision requirements, and to overcome the practical problems of unilaterally pursuing high-frequency sampling data and the excessively high lower limit of frequency determined by existing methods, this embodiment provides a method for determining the lower limit of data sampling frequency for wind farm grid-connected performance supervision.

[0051] The method for determining the lower limit of data sampling frequency for wind farm grid connection performance monitoring provided in this embodiment mainly targets dynamic performance indicators to determine the lower limit of data sampling frequency.

[0052] The method for determining the lower limit of data sampling frequency for wind farm grid connection performance monitoring provided in this embodiment determines the relevant parameters of the simulation process and the parameter values ​​of the wind farm dynamic model based on the high-frequency sampling data segments of relevant measurement points for wind farm technical supervision; for specific technical supervision performance indicators, the minimum sampling frequency of the required sample data is determined by numerical simulation method.

[0053] The method for determining the lower limit of sampling frequency for wind farm grid connection performance monitoring data provided in this embodiment has significant advantages such as strong pertinence and ease of implementation, and is of great significance for carrying out extensive and in-depth technical supervision of wind farms.

[0054] The method for determining the lower limit of sampling frequency for wind farm grid connection performance monitoring data provided in this embodiment takes wind farm grid connection performance monitoring indicators as its starting point. First, based on obtaining the actual generated power setpoint and high-frequency sampling data segment of the actual generated power related to the wind farm's grid connection performance, the noise variance of the actual generated power is estimated using the high-frequency sampling data segment of the actual generated power. A dynamic model of the wind farm is then established using the actual generated power setpoint as input and the actual generated power as output. Second, using the estimated noise variance of the actual generated power as a reference, the output measurement noise of the dynamic model and the model simulation input signal are constructed to obtain the model's simulation output signal. Third, the model simulation input... The signal and output signal are sampled at a given frequency to obtain the sample sequences corresponding to the simulation input and output. The performance index is estimated under the given sampling frequency condition using the technical supervision performance index estimation method. When the sampling frequency is traversed from small to large in a fixed step size, a set of estimated values ​​of the performance index at different sampling frequencies is obtained. Finally, the above simulation steps are repeated to obtain multiple sets of estimated values ​​of the performance index at different sampling frequencies. The performance index estimation results at each sampling frequency are evaluated by using the coefficient of variation frequency. The minimum sampling frequency corresponding to the coefficient of variation being greater than a given threshold is selected as the lower limit of the sampling frequency.

[0055] The method for determining the lower limit of data sampling frequency for wind farm grid connection performance monitoring provided in this embodiment is as follows: Figure 1 As shown, it includes:

[0056] Step 1: Using the sampling frequency of the relevant sensors as a reference, obtain high-frequency sampling data segments of the wind farm's actual generated power setpoint u0(t) and actual generated power y0(t). and The sampling frequency of the high-frequency sampling data segment should be on the same order of magnitude as the sensor's sampling frequency (the sensor's sampling frequency is typically in the range of 1000 to 0.1 MHz), and then... Estimate the noise variance of y0(t) High-frequency sampling data segment For input, For the output, the least squares identification method is used to obtain the dynamic model G(s) of the wind farm. The details are as follows:

[0057] Step 101: Obtain high-frequency sampling data segments of the actual generated power setpoint u0(t) and the actual generated power y0(t) of the wind farm. and

[0058] Step 102, for Polynomial fitting is used, that is:

[0059]

[0060] Where n represents the sample ordinal number, N0 is the number of sample points in the high-frequency sampling data segment, and β0, β1 and β2 are the parameters to be fitted;

[0061] Let β=[β0,β1,β2], T=[1,2,…,N0] T , Y=[y0(1),y0(2),…,y0(N0)] T Then β is:

[0062] β=(T T T) -1 T T Y (2)

[0063] Then the noise variance in y0(t) The estimate is:

[0064]

[0065] Step 103: Let the dynamic model G(s) of the wind farm be:

[0066]

[0067] Where s is the Laplace operator, ω n ζ and τ are system parameters to be determined; then... and Based on this, the parameter ω is obtained using the least squares identification method. n The specific values ​​of ζ and τ are:

[0068]

[0069] Among them, parameters This indicates that the dynamic model G(s) is in the parameter ω n Under the conditions of ζ and τ The obtained dynamic model output value.

[0070] Step 2: Let To mitigate the measurement noise in the dynamic model output y(t), a step input u(t) to the field dynamic model G(s) is designed to obtain the simulation output signal y(t) of the dynamic model G(s). The details are as follows:

[0071] Step 201: Estimate the noise variance in y0(t) Based on this, the measurement noise e(t) of the output y(t) of the dynamic model G(s) is constructed as follows:

[0072]

[0073] That is, the measured noise follows a normal distribution with a mathematical expectation of zero and a variance equal to the variance of the noise in the actual generated power.

[0074] Step 202: Redesign the simulation step input u(t) of the dynamic model G(s) as follows:

[0075]

[0076] Where u0(t) is the value of u(t) before the step change, and u1(t) is the value of u(t) after the step change. Generally, the step change amplitude of u(t), u1(t) - u0(t), is a per-unit value of 0.15, and the basis of this per-unit value is the installed capacity of the wind farm.

[0077] Step 203: Under the condition that the input of the dynamic model G(s) is u(t), and combined with the measurement noise e(t), according to... Figure 3 The simulation framework shown is used to perform the simulation, and the simulation output signal y(t) is obtained. Figure 3 In the diagram, u(t) is the input of the dynamic model, G(s) is the identified dynamic model, and y(t) is the system output containing measurement noise e(t).

[0078] The simulation output y(t) is obtained by superimposing the measurement noise e(t) on the output of the dynamic model G(s). This is to simulate the uncertainty of the actual generated power in the electric field process. The variance of the measurement noise is obtained by fitting the trend curve based on the high-frequency sampling data of the actual generated power.

[0079] Step 3: Simulate the model input signal u(t) and output signal y(t) at frequency f. i Perform sampling, f i ∈[f min ,f max ], thus obtaining the sample sequences u(t) and y(t). i (n) and y i (n), and then according to the estimation method f of the technical supervision performance index θ. θ To obtain performance index estimates Let f i With varying step size Δ f Traverse the interval [f] from smallest to largest min ,f max This yields a set of estimated values ​​for the performance index θ at different sampling frequencies. Specifically as follows:

[0080] Step 301: Simulate the model input signal u(t) and output signal y(t) at a frequency f. i Perform sampling, f i ∈[f min ,f max ], thus obtaining the sample sequences u(t) and y(t). i (n) and y i(n), and then according to the estimation method f of the technical supervision performance index θ. θ To obtain performance index estimates Right now:

[0081]

[0082] Step 302, when f i With step size Δ f In the interval [f min ,f max When the values ​​change, different sample sequences of u(t) and y(t) can be obtained. These sample sequences can then be used to obtain the performance index θ at the sampling frequency f. i A set of estimates for different values Where I represents a sampling frequency of f i Performance index estimates The quantity; parameter f min with f max These are the minimum and maximum values ​​of the sampling frequency, which are determined by the user. Because this embodiment uses data simulation as a technical means, the interval boundary f... min It can be small enough, f max It can be large enough to fully ensure [f min ,f max Includes the lower limit of the sampling frequency f. Low .

[0083] Step 4: Repeat steps 2 and 3 M times to obtain M sets of estimated values ​​for the performance index θ at different sampling frequencies. And using the coefficient of variation c v (i) Right The evaluations were performed separately, i = 1, 2, ..., I, to obtain the coefficient of variation sequence. Based on the coefficient of variation threshold c v,th Determine the effective estimation boundary of the performance index θ by checking whether it exceeds a given threshold, and obtain the corresponding sampling frequency f. i , at this time f i Consider it as the lower limit of the sampling frequency f Low The details are as follows:

[0084] Step 401: Repeat the simulation process in steps 2 and 3 M times to obtain a two-dimensional set of performance index θ estimates. That is, at the sampling frequency f i Under these conditions, the Mth estimated value of the performance index θ is... The number of repetitions, M, needs to be large enough to ensure that the estimates at each sampling frequency meet the requirements of statistical analysis. For a two-dimensional set... Medium sampling frequency f i The corresponding set With coefficient of variation c v (i) measures its effectiveness, c v (i) is defined as:

[0085]

[0086] in, The sampling frequency f obtained in the m-th simulation is... i The estimated performance index σ at that time θ,i for The sample standard deviation, μ θ,i for The sample mean, For all f i f i ∈[f min ,f max ], which yields c v (i) Sequence, i=1,2,…,I.

[0087] Step 402: According to the general principle of the coefficient of variation in application, when the coefficient of variation is greater than the threshold α%, the estimation result is considered to have lost its reliability. The value of α% is generally 15%, that is, when c... v (i) When ≥15%, the estimated value can be considered as It has become invalid. Therefore, a threshold value c for the coefficient of variation is set. v,th When α% corresponds to the sampling frequency f i That is, the lower limit of the sampling frequency f. Low ,Right now:

[0088]

[0089] The following is an application of the method for determining the lower limit of data sampling frequency for wind farm grid connection performance monitoring provided in this embodiment in a specific case, and the difference between the method for determining the lower limit of data sampling frequency for wind farm grid connection performance monitoring provided in this embodiment and existing methods is illustrated through this case.

[0090] Case Study: In this case, the actual generated power setpoint is u(t) in equation (7), and the actual generated power is y(t). The objective is to determine the rise time t of the primary frequency regulation active power. r Effectively estimate the lower limit of the required sampling frequency. First frequency modulation rise time t r The estimation method is as follows:

[0091]

[0092] Here, parameter n1 is the time corresponding to the change in u(t) when the change in y(t) is greater than 10%, and it is determined as follows:

[0093]

[0094] The time when the parameter n2 satisfies the condition that the change in y(t) is greater than 90% of the change in u(t) is determined as follows:

[0095]

[0096] The first step is to obtain high-frequency sampling data segments of the actual generated power setpoint u0(t) and the actual generated power y0(t) of the wind farm. and N0 = 2.1 × 10 5 ,like Figure 2 As shown, the dashed lines represent Solid lines indicate by Based on this, the noise variance is estimated according to equations (1) to (3). enter, As the output, according to equation (5), the dynamic model G(s) of the wind farm is obtained as follows:

[0097]

[0098] The second step is to use the noise variance estimate in y0(t) Based on this, the measurement noise e(t) of the output y(t) of the dynamic model G(s) is constructed as follows:

[0099] e(t)~N(0,2.5150×10 -7 )

[0100] The simulation step input u(t) of the redesigned dynamic model G(s) is:

[0101]

[0102] according to Figure 3 The simulation framework diagram shown above, under the condition that the input of the dynamic model G(s) is u(t), yields the simulation output y(t);

[0103] Step 3: Simulate the model input signal u(t) and the corresponding output signal y(t) at a frequency f. i Perform sampling, f i The sample sequences u(t) and y(t) are obtained by varying the Hz frequency from 0.6662 Hz to 1000 Hz with a fixed step size. i (n) and y i (n), and then based on the first frequency modulation rise time t r The estimation method yields estimated values ​​of performance indicators. For different f i The rise time t of the first frequency modulation is obtained.r At different sampling frequencies f i A set of estimates at time

[0104] Step 4: Repeat the simulation process in steps 2 and 3 100 times to obtain the frequency modulation rise time t. r Two-dimensional set of estimated values For two-dimensional sets Medium sampling frequency f i The corresponding set Calculate according to formula (9) coefficient of variation c v (i) measures its effectiveness and obtains the coefficient of variation sequence. like Figure 4 As shown, the dashed line represents the coefficient of variation threshold, and the solid line represents the coefficient of variation sequence. When taking the coefficient of variation threshold c v,th When = 15%, the lower limit value f of the sampling frequency obtained according to equation (10) Low f Low =0.87Hz.

[0105] Example 2

[0106] This embodiment provides a system for determining the lower limit of data sampling frequency for monitoring the grid connection performance of wind farms, which specifically includes:

[0107] The data acquisition module is configured to acquire data segments of actual generated power setpoints and actual generated power related to the grid connection performance of wind farms;

[0108] The noise variance estimation module is configured to estimate the noise variance of the actual power based on the data segment of the actual power.

[0109] The model building module is configured to: take a data segment of the actual generated power setpoint as input and take a data segment of the actual generated power as output to build a dynamic model of the wind farm;

[0110] The simulation module is configured to perform several simulations based on the noise variance of the actual generated power and the dynamic model of the wind farm, and each simulation yields multiple performance index estimates at different sampling frequencies.

[0111] The coefficient of variation calculation module is configured to obtain a coefficient of variation based on the performance index estimates obtained from all simulations at each sampling frequency.

[0112] The selection module is configured to select the sampling frequency corresponding to the coefficient of variation that meets the conditions, as the lower limit of the sampling frequency.

[0113] It should be noted that each module in this embodiment corresponds one-to-one with each step in Embodiment 1, and their specific implementation processes are the same, so they will not be repeated here.

[0114] Example 3

[0115] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the method for determining the lower limit of data sampling frequency for wind farm grid connection performance monitoring as described in Embodiment 1 above.

[0116] Example 4

[0117] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the method for determining the lower limit of data sampling frequency for wind farm grid connection performance monitoring as described in Embodiment 1 above.

[0118] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0119] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0120] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0121] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0122] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0123] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for determining the lower limit of data sampling frequency for monitoring the grid connection performance of wind farms, characterized in that, include: Obtain the data segment of the actual generated power setpoint and the actual generated power related to the grid connection performance of the wind farm; Estimate the noise variance of the actual power output using the data segment of the actual power output; A dynamic model of a wind farm is established by taking the data segment of the actual generated power setpoint as input and the data segment of the actual generated power as output. Based on the noise variance of the actual generated power and the dynamic model of the wind farm, several simulations were performed, and each simulation yielded multiple performance index estimates at different sampling frequencies. At each sampling frequency, a coefficient of variation is obtained based on the performance index estimates obtained from all simulations; Select the sampling frequency corresponding to the coefficient of variation that meets the conditions as the lower limit of the sampling frequency; The simulation steps include: constructing the measurement noise of the wind farm dynamic model based on the noise variance of the actual generated power; constructing the simulation input signal and combining the measurement noise and the wind farm dynamic model to obtain the simulation output signal; sampling the simulation input signal and the simulation output signal at each sampling frequency to obtain the sample sequence of the simulation input signal and the sample sequence of the simulation output signal, and combining the estimation method of technical supervision performance index to obtain the estimated value of the performance index; The coefficient of variation at a certain sampling frequency is: the ratio of the standard deviation of the performance index estimates obtained from all simulations at that sampling frequency to the mean of the performance index estimates obtained from all simulations at that sampling frequency. The dynamic model of the wind farm is as follows: ;in, s This represents the input parameters of the wind farm dynamic model. , and It is obtained through the least squares identification method.

2. The method for determining the lower limit of data sampling frequency for wind farm grid connection performance monitoring as described in claim 1, characterized in that, The measured noise follows a normal distribution with a mathematical expectation of zero and a variance equal to the actual power output.

3. The method for determining the lower limit of data sampling frequency for wind farm grid connection performance monitoring as described in claim 1, characterized in that, The sampling frequency is obtained by traversing a given frequency range from small to large with a fixed step size.

4. The method for determining the lower limit of data sampling frequency for wind farm grid connection performance monitoring as described in claim 1, characterized in that, The lower limit of the sampling frequency is: in, For the first i Each sampling frequency, For the first i The coefficient of variation at each sampling frequency The threshold value is used.

5. A system for determining the lower limit of data sampling frequency for monitoring the grid connection performance of wind farms, characterized in that, include: The data acquisition module is configured to acquire data segments of actual generated power setpoints and actual generated power related to the grid connection performance of wind farms; The noise variance estimation module is configured to estimate the noise variance of the actual power based on the data segment of the actual power. The model building module is configured to: take the data segment of the actual generated power setpoint as input and the data segment of the actual generated power as output to build a dynamic model of the wind farm; The simulation module is configured to perform several simulations based on the noise variance of the actual generated power and the dynamic model of the wind farm, and each simulation yields multiple performance index estimates at different sampling frequencies. The coefficient of variation calculation module is configured to obtain a coefficient of variation based on the performance index estimates obtained from all simulations at each sampling frequency. The selection module is configured to select the sampling frequency corresponding to the coefficient of variation that meets the conditions as the lower limit of the sampling frequency; The simulation steps include: constructing the measurement noise of the wind farm dynamic model based on the noise variance of the actual generated power; constructing the simulation input signal and combining the measurement noise and the wind farm dynamic model to obtain the simulation output signal; sampling the simulation input signal and the simulation output signal at each sampling frequency to obtain the sample sequence of the simulation input signal and the sample sequence of the simulation output signal, and combining the estimation method of technical supervision performance index to obtain the estimated value of the performance index; The coefficient of variation at a certain sampling frequency is: the ratio of the standard deviation of the performance index estimates obtained from all simulations at that sampling frequency to the mean of the performance index estimates obtained from all simulations at that sampling frequency. The dynamic model of the wind farm is as follows: ;in, s This represents the input parameters of the wind farm dynamic model. , and It is obtained through the least squares identification method.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the method for determining the lower limit of data sampling frequency for wind farm grid connection performance monitoring as described in any one of claims 1-4.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the method for determining the lower limit of data sampling frequency for wind farm grid connection performance monitoring as described in any one of claims 1-4.

Images