Wind farm frequency regulation capability evaluation method based on multi-dimensional dynamic aggregation and application thereof

Abstract

The application relates to the technical field of power systems, in particular to a wind farm frequency modulation capacity evaluation method based on multi-dimensional dynamic aggregation and application thereof. Wind speed, rotor speed, grid-connected active output and pitch angle are selected as clustering indexes for clustering, the wind power generators obtained through clustering are taken as units for quantitative evaluation of system frequency response capacity and frequency modulation recovery capacity, and the frequency modulation capacity of the wind power generator is comprehensively evaluated according to the quantitative evaluation results of the two. The application aims to solve the problem of how to evaluate the dynamic characteristics of a wind farm.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a method for evaluating the frequency regulation capability of wind farms based on multi-dimensional dynamic aggregation and its application. Background Technology

[0002] With the large-scale grid connection of renewable energy sources such as wind power, the frequency regulation capability of power systems is facing unprecedented challenges. Large wind farms, containing a massive number of wind turbines, exhibit extremely complex dynamic characteristics. Directly modeling each turbine in detail would not only incur enormous computational costs but also lead to low simulation efficiency. Therefore, exploring an aggregated equivalent method that can efficiently evaluate the frequency regulation capability of wind farms is particularly important.

[0003] However, current wind farm aggregation and equivalent technologies mostly focus on steady-state characteristic analysis, which is insufficient for evaluating frequency regulation capabilities and cannot meet the actual needs of power system frequency regulation control. In addition, traditional wind farm analysis that incorporates the internal turbine status of the wind farm requires detailed modeling of each turbine when establishing the wind farm in the simulation system. Although it can accurately simulate the external characteristics of the wind farm, it places high demands on the simulation software, increases computational complexity, and requires a long simulation time.

[0004] Therefore, this application aims to provide a method for evaluating the frequency regulation capability of wind farms that takes into account the dynamic characteristics of wind farms, so as to achieve efficient and accurate evaluation of frequency regulation capability and thus provide solid technical support for the stable operation of power systems. Summary of the Invention

[0005] The main objective of this application is to provide a method for evaluating the frequency regulation capability of wind farms based on multi-dimensional dynamic aggregation, which aims to solve the problem of how to evaluate the dynamic characteristics of wind farms.

[0006] To achieve the above objectives, this application provides a multi-dimensional dynamic aggregation method for evaluating the frequency regulation capability of wind farms, the method comprising:

[0007] S10, select wind speed, rotor speed, grid-connected active power output and pitch angle as clustering indicators, and divide the wind turbine group corresponding to each wind turbine in the wind farm according to the clustering indicators and the improved KFCM clustering algorithm.

[0008] S20, taking the wind turbine as a unit, select the frequency change rate, steady-state frequency deviation, frequency minimum point, frequency second drop minimum point and frequency second drop value as the system frequency response capability evaluation index of the wind turbine, and select the rotor speed recovery time as the frequency regulation recovery capability evaluation index of the wind turbine, so as to determine the frequency regulation capability evaluation result of the wind turbine based on the frequency response capability evaluation index and the frequency regulation recovery capability evaluation index.

[0009] Optionally, in step S20, the step of determining the frequency regulation capability assessment result of the wind turbine based on the frequency response capability assessment index and the frequency regulation recovery capability assessment index includes:

[0010] Determine whether the frequency response capability evaluation index is within the corresponding frequency response index range, and determine whether the frequency modulation recovery capability evaluation index is within the corresponding frequency modulation recovery index range;

[0011] The frequency response capability of the wind turbine is quantified by the number of times each frequency response capability evaluation index falls within the frequency response index range, and the frequency regulation recovery capability of the wind turbine is quantified by the number of times each frequency regulation recovery capability evaluation index falls within the corresponding frequency regulation recovery index range.

[0012] The frequency modulation capability evaluation result is determined based on the quantized value of the frequency response capability and the quantized value of the frequency modulation recovery capability, wherein both the quantized value of the frequency response capability and the quantized value of the frequency modulation recovery capability are positively correlated with the frequency modulation capability evaluation result.

[0013] Optionally, the frequency response index range includes the frequency change rate range, the steady-state frequency deviation range, the frequency minimum point range, the frequency second drop minimum point range, and the frequency second drop value range.

[0014] The frequency modulation recovery index range includes the rotor speed recovery time range.

[0015] Optionally, in step S20, the frequency modulation capability evaluation result includes frequency response capability and frequency modulation recovery capability;

[0016] The rules that the frequency modulation capability evaluation results satisfy include: the frequency modulation capability evaluation results are positively correlated with the frequency response capability and the frequency modulation recovery capability;

[0017] The rules that the frequency response capability satisfies include: the frequency change rate, the steady-state frequency deviation, the frequency minimum point, and the frequency second drop value are all negatively correlated with the frequency response capability;

[0018] The frequency modulation recovery capability satisfies the following rule: the rotor speed recovery time is negatively correlated with the frequency modulation recovery capability.

[0019] Optionally, in step S10, the step of dividing the wind turbine clusters corresponding to each wind turbine in the wind farm according to the clustering index and the improved KFCM clustering algorithm includes:

[0020] S11, the optimal number of clusters is determined using the following formula. :

[0021]

[0022]

[0023] In the formula, n is the sample size of wind turbines. It is a set measure of dispersion within clusters, where k is the number of clusters. For sample weights, Let be the feature vector of the i-th sample, i.e., the wind turbine operating state parameters. For the c-th cluster center, For the sample To the cluster center The nuclear space distance between them For mathematical expectation, For clustering;

[0024] S12, Based on the optimal number of clusters, determine the minimization objective function of the improved KFCM clustering algorithm:

[0025]

[0026] In the formula, This represents the feature vector corresponding to the c-th cluster center in high-dimensional space. Let i be the weight of sample i. Represents the i-th data point With the c-th cluster center The distance between them is measured by m, which represents the fuzzy coefficient. This represents the membership matrix, where n represents the sample size of wind turbines. To determine the optimal number of clusters;

[0027] S13, classify the wind turbines based on the minimized objective function to obtain wind turbines of different classifications.

[0028] Optionally, the selection steps for the clustering index include:

[0029] S100, collect wind turbine operating status parameters, including wind turbine speed, rotor speed, grid-connected active power output, pitch angle, mechanical torque, electromagnetic torque, stator current d-axis component, stator current q-axis component, rotor current d-axis component, rotor current q-axis component, stator active power output, stator reactive power output, and grid-connected reactive power output;

[0030] S200, Standardize the operating status parameters of the fan:

[0031]

[0032] Where, x ijThis refers to the operating status parameters of the wind turbine. s represents the average value of the fan operating status parameters; j The standard deviation of the wind turbine's operating parameters. These are the standardized operating status parameters of the wind turbine;

[0033] S300, Calculate the covariance matrix of the wind turbine operating state parameters based on the standardized wind turbine operating state parameters:

[0034]

[0035] In the formula, This represents the operating status parameter of the j-th fan. mean and the operating status parameters of the kth fan mean The covariance between them, where m is the number of wind turbine operating state parameters;

[0036] S400, calculate the eigenvalues ​​of each wind turbine operating state parameter in the covariance matrix, calculate the cumulative contribution rate based on the eigenvalues, and select the first 4 cumulative contribution rates as the clustering index.

[0037] In addition, to achieve the above objectives, this application also provides a frequency regulation method, which uses a cluster frequency regulation strategy to regulate the frequency of wind turbines in the wind farm frequency regulation capability assessment method based on multi-dimensional dynamic aggregation as described above.

[0038] In addition, to achieve the above objectives, this application also provides a wind farm frequency regulation capability assessment method based on multi-dimensional dynamic aggregation as described above, and its application in wind farm frequency regulation capability assessment.

[0039] In addition, to achieve the above objectives, this application also provides a computer system, the computer system comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, it implements the steps of the wind farm frequency regulation capability assessment method based on multi-dimensional dynamic aggregation as described in any of the preceding claims.

[0040] In addition, to achieve the above objectives, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the wind farm frequency regulation capability assessment method based on multi-dimensional dynamic aggregation as described in any of the preceding claims.

[0041] This application has at least the following beneficial effects:

[0042] 1. Select wind speed, rotor speed, grid-connected active power output and pitch angle as clustering indicators for clustering. Use the wind turbine units obtained from the clustering as units to conduct quantitative evaluation of the system frequency response capability and frequency regulation recovery capability. Based on the quantitative evaluation results of the two, comprehensively evaluate the frequency regulation capability of the wind turbine units.

[0043] 2. By selecting quantitative indicators to evaluate the system frequency response characteristics of doubly-fed wind turbines, a basis is provided for accurately evaluating the frequency regulation performance of wind turbine units;

[0044] 3. By introducing the rotor speed recovery time as an indicator, the recovery capability of the doubly-fed wind turbine after participating in frequency regulation was further evaluated, providing an important reference for the operation and control of wind turbine units;

[0045] 4. Considering the cluster effect and interaction of wind turbines within a wind farm, this paper proposes to combine the obtained wind turbines with a cluster frequency regulation strategy to achieve coordinated control of the wind turbine output power and improve the overall frequency regulation effect of the wind farm. Attached Figure Description

[0046] Figure 1 This is a flowchart of the first embodiment of the wind farm frequency regulation capability assessment method based on multi-dimensional dynamic aggregation of this application;

[0047] Figure 2 This is a comparative diagram of the independent explained variance and the cumulative explained variance involved in the embodiments of this application;

[0048] Figure 3 This is a schematic diagram illustrating the relevant evaluation indicators involved in the embodiments of this application;

[0049] Figure 4 This is a schematic diagram of system frequency-related indicators involved in the embodiments of this application;

[0050] Figure 5 This is a schematic diagram of the rotor speed recovery index of the doubly fed wind turbine generator involved in the embodiments of this application;

[0051] Figure 6 This is a schematic diagram of the multi-dimensional wind farm aggregation involved in the embodiments of this application;

[0052] Figure 7 This is a schematic diagram illustrating the membership degree involved in the embodiments of this application;

[0053] Figure 8 This is a comparison chart of the active power response curves of various models under stable operating conditions involved in the embodiments of this application;

[0054] Figure 9 This is a comparison chart of the reactive power response curves of various models under stable operating conditions involved in the embodiments of this application;

[0055] Figure 10This is a comparison chart of the active response curves of various models under the wind speed disturbance conditions involved in the embodiments of this application;

[0056] Figure 11 This is a comparison chart of the reactive power response curves of various models under the wind speed disturbance conditions involved in the embodiments of this application;

[0057] Figure 12 This is a diagram illustrating the frequency modulation effect after clustering in an embodiment of this application.

[0058] Figure 13 This is a diagram illustrating the frequency modulation effect after multiplication by a single machine, as described in an embodiment of this application.

[0059] Figure 14 This is a comparison chart of the frequency modulation effects of cluster and single-machine multiplication in the embodiments of this application;

[0060] Figure 15 This is a schematic diagram of the hardware operating environment of the computer system involved in the embodiments of this application.

[0061] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0062] To better understand the above technical solutions, exemplary embodiments of this disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of this disclosure to those skilled in the art.

[0063] First Embodiment

[0064] Reference Figure 1 This embodiment provides a method for evaluating the frequency regulation capability of wind farms based on multi-dimensional dynamic aggregation. The method includes the following steps:

[0065] S10, select wind speed, rotor speed, grid-connected active power output and pitch angle as clustering indicators, and divide the wind turbine group corresponding to each wind turbine in the wind farm according to the clustering indicators and the improved KFCM clustering algorithm.

[0066] In this step, data collection and processing is a fundamental and crucial step in the wind farm frequency regulation capability assessment method. Based on in-depth modeling of the wind turbine's mechanical and electrical control systems, it is clear that the operating status of the wind turbine is affected by multiple wind turbine operating parameters, including wind speed, rotor speed, grid-connected active power output, pitch angle, etc.

[0067] However, not all wind turbine operating parameters can be used as key clustering indicators for classifying wind turbine units; therefore, a screening process is necessary.

[0068] In some alternative implementations, principal component analysis is used to select clustering indices from the wind turbine operating parameters, as detailed below:

[0069] S100, collect wind turbine operating status parameters, including wind turbine speed, rotor speed, grid-connected active power output, pitch angle, mechanical torque, electromagnetic torque, stator current d-axis component, stator current q-axis component, rotor current d-axis component, rotor current q-axis component, stator active power output, stator reactive power output, and grid-connected reactive power output;

[0070] S200, Standardize the operating status parameters of the fan:

[0071]

[0072] Where, x ij This refers to the operating status parameters of the wind turbine. s represents the average value of the fan operating status parameters; j The standard deviation of the wind turbine's operating parameters. These are the standardized operating status parameters of the wind turbine;

[0073] S300, Calculate the covariance matrix of the wind turbine operating state parameters based on the standardized wind turbine operating state parameters:

[0074]

[0075] In the formula, This represents the operating status parameter of the j-th fan. mean and the operating status parameters of the kth fan mean The covariance between them, where m is the number of wind turbine operating state parameters;

[0076] S400, calculate the eigenvalues ​​of each wind turbine operating state parameter in the covariance matrix, calculate the cumulative contribution rate based on the eigenvalues, and select the first 4 cumulative contribution rates as the clustering index.

[0077] In step S100, it should be noted that, for example, the calculation of the initial values ​​of the 13 state variables is as follows:

[0078] (1) The wind speed measured by each wind turbine at a certain moment The rotor speed of each wind turbine can be obtained from the following formula. Mechanical power output of wind turbine and propeller pitch angle .

[0079]

[0080] In the above formula, r represents the radius of the wind turbine blade; Indicates air density; Indicates wind speed; Indicates the pitch angle; Indicates mechanical power; It represents the wind energy conversion efficiency coefficient, which expresses the ability of a wind turbine to capture energy from the wind; This indicates the tip speed ratio (the ratio of the blade tip speed to the wind speed).

[0081] (2) The stator active power output is calculated from this. Stator reactive power output The assumption is that the fan operates at a unity power factor.

[0082]

[0083] In the formula, U represents the stator winding voltage. sa U sb and U sc These represent the three-phase voltages (A, B, and C) of the stator winding, respectively. Indicates rotor resistance. This indicates the slippage rate.

[0084] (3) The d-axis component of the stator current is calculated according to the following formula. q-axis component of stator current .

[0085]

[0086] (4) The d-axis component of the rotor current is calculated according to the following formula. q-axis component of rotor current .

[0087]

[0088] In the formula, Indicates the mechanical angular velocity of the rotor. Mutual inductance refers to the magnetic coupling between the stator and rotor. This indicates the stator inductance.

[0089] (5) Mechanical torque and electromagnetic torque The calculations are as follows:

[0090]

[0091]

[0092] In the formula, The torque coefficient, This represents the number of pole pairs of the motor. The d-axis component of the stator flux linkage.

[0093] The following example illustrates the selection of the four clustering indicators in this step:

[0094] In some specific implementations, the wind speed at a certain moment in the doubly fed wind farm operation data is selected as the initial input wind speed for each wind turbine in the detailed model of the wind farm, as shown in Table 1.

[0095] Table 1. Wind speed parameters for each typhoon fan

[0096]

[0097] The data were processed according to the principal component analysis steps, and the eigenvalues, independent explained variance, and cumulative explained variance are shown in Table 2.

[0098] Table 2. Independent and cumulative explained variances for each eigenvalue

[0099]

[0100] From Table 2, and as shown in Table 2, Figure 2 The diagram showing the comparison between independent explained variance and cumulative explained variance reveals that the cumulative explained variance of the first three principal components reaches 99.945%, indicating that the first three principal components contain 99.945% of the original information of the system. Therefore, the number of principal components is determined to be 3. Further calculations yield the factor loading matrix composed of the state variables and principal components, as shown in Table 3.

[0101] In Table 3, ω r P represents the rotor speed. g Indicates grid-connected active power output, β represents the pitch angle, and T e Represents mechanical torque, i ds i represents the d-axis component of the stator current. qs i represents the q-axis component of the stator current. dr i represents the d-axis component of the rotor current. qr P represents the q-axis component of the rotor current. s Q represents the stator active power output. s T represents the stator reactive power output. m Q represents mechanical torque. g This indicates the reactive power output of the grid connection.

[0102] Table 3. Factor loading matrix composed of state variables and principal components

[0103] State variables First principal component Second principal component <![CDATA[ω r ]]> 0.948 -0.091 <![CDATA[P g ]]> 0.999 -0.045 β 0.495 0.869 <![CDATA[T e ]]> 0.997 -0.049 <![CDATA[i ds ]]> 0.997 -0.050 <![CDATA[i qs ]]> 0 0 <![CDATA[i dr ]]> -0.998 0.052 <![CDATA[i qr ]]> -0.998 0.049 <![CDATA[P s ]]> 0.996 -0.051 <![CDATA[Q s ]]> 0 0 <![CDATA[T m ]]> -0.997 0.049 <![CDATA[Q g ]]> 0 0

[0104] As shown in Table 3, after considering redundancy and correlation, among the variables characterizing the operating state of a doubly-fed wind turbine, the rotor speed ω is the most important. r Grid-connected active power output P g The pitch angle β can reflect the dynamic characteristics of the doubly fed wind turbine relatively completely, while the wind speed v, as the most direct and representative state variable, is also selected as the grouping index in this invention.

[0105] Furthermore, after selecting the above-mentioned clustering indicators, the wind turbines in the wind farm are classified according to the four clustering indicators, and each wind turbine is assigned to a unit.

[0106] In some alternative implementations, the principal components extracted in the previous step are clustered based on the improved KFCM (Kernelized Fuzzy C-Means) clustering algorithm to classify each wind turbine sample in the wind farm and obtain wind turbine units of different classifications.

[0107] In some alternative implementations, the traditional k-means clustering algorithm requires the value k to be given manually, which introduces a degree of subjectivity into the clustering results. Therefore, this embodiment introduces a Gap Value and balances simulation accuracy with computational complexity to determine the optimal number of clusters. :

[0108]

[0109] in:

[0110]

[0111] In the formula, n is the sample size of wind turbines. It is a set measure of dispersion within clusters, where k is the number of clusters. For sample weights, Let be the feature vector of the i-th sample, i.e., the wind turbine operating state parameters. For the c-th cluster center, For the sample To the cluster center The nuclear space distance between them For mathematical expectation, For clustering;

[0112] To obtain the optimal number of clusters Then, the improved Kernel-FCM clustering algorithm is used to group wind farms:

[0113]

[0114] In the formula, This represents the feature vector corresponding to the c-th cluster center in high-dimensional space. Let i be the weight of sample i. Represents the i-th data point With the c-th cluster center The distance between them is measured by m, which represents the fuzzy coefficient. This represents the membership matrix, where n represents the sample size of wind turbines. The optimal number of clusters.

[0115] It should be noted here that the core idea of ​​the kernel function method is to use nonlinear mapping. The input pattern space (i.e., the dataset in this paper) The spatial location is mapped to a high-dimensional feature space, which can be represented as: In this higher-dimensional space, data can be more easily separated or better structured, and there are no constraints on the form of this mapping; the mapping function requires almost no computation. Then, machine learning algorithms are used; in this embodiment, the KFCM clustering algorithm is used for cluster analysis. Since the algorithm only uses the inner product between data points for calculation, as long as a kernel function satisfying the Mercer condition is selected, it is not necessary to know the nonlinear mapping. The specific form.

[0116] sample The nonlinear mapping from the original low-dimensional space to the high-dimensional feature space takes the form of:

[0117]

[0118] Let K be a value defined in the dataset. The kernel function on the high-dimensional feature space characterizes the similarity between data, and its dot product form is:

[0119]

[0120] In the above formula, .

[0121] The kernel function K satisfies the Mercer condition. Because the Gaussian kernel function has strong nonlinear mapping capabilities and flexibility, it can handle various complex data distributions. Its smoothness and robustness make it suitable for different types of data, and it does not require explicit computation of the high-dimensional feature space, reducing computational complexity. In this embodiment, the Gaussian kernel function is chosen.

[0122]

[0123] In the above formula Indicates kernel parameters.

[0124] Calculate the data in a high-dimensional feature space using kernel functions. The inner product in the algorithm is used to ultimately perform clustering by minimizing the objective function, resulting in the improved Kernel-FCM clustering algorithm. .

[0125] Further and optionally, in the improved Kernel-FCM clustering algorithm, the distance metric The calculation expression is:

[0126]

[0127] Wherein, the membership matrix The iteration satisfies the following expression:

[0128]

[0129] The feature vector The iteration satisfies the following expression:

[0130]

[0131] In the formula, Represents samples in a high-dimensional feature space Its inner product, Represents samples in a high-dimensional feature space With cluster center The inner product, Represents cluster centers in a high-dimensional feature space Its internal product with itself.

[0132] Finally, the wind turbines are classified based on the minimized objective function. First, a data matrix is ​​collected and constructed, and then the data matrix is ​​standardized. Next, the cluster centers, number of iterations, threshold, and fuzzy parameters are determined, and the objective function is constructed. Based on this, the membership matrix and cluster centers are updated. It is checked whether the objective function is less than the threshold; if not, the membership matrix and cluster centers are updated again until the objective function is less than the threshold. Once the condition is met, the algorithm converges, outputting all clusters and obtaining the wind turbines classified into different categories.

[0133] S20, taking the wind turbine as a unit, select the frequency change rate, steady-state frequency deviation, frequency minimum point, frequency second drop minimum point and frequency second drop value as the system frequency response capability evaluation index of the wind turbine, and select the rotor speed recovery time as the frequency regulation recovery capability evaluation index of the wind turbine, so as to determine the frequency regulation capability evaluation result of the wind turbine based on the frequency response capability evaluation index and the frequency regulation recovery capability evaluation index.

[0134] After classifying the wind turbines in the wind farm in step S10, the frequency regulation capability is evaluated on a unit basis using the obtained wind turbines.

[0135] Reference Figure 3 The diagram showing relevant evaluation indicators illustrates how, in order to quantify the frequency regulation performance of a doubly fed wind turbine, this embodiment creatively proposes evaluation indicators for both system frequency and rotor speed recovery.

[0136] Specifically, regarding system frequency, refer to Figure 4 The diagram showing the system's frequency-related indicators selects the rate of change of frequency, steady-state frequency deviation, minimum frequency point, minimum frequency dip point, and frequency dip value as evaluation indicators for the system's frequency response capability, in order to comprehensively analyze the system's frequency response characteristics.

[0137] In the study of the stepwise control strategy for doubly-fed wind turbines, referring to Figure 5 The diagram illustrating the rotor speed recovery index of a doubly-fed induction generator (DFIG) wind turbine is shown. Considering that after providing active power support, the active power output deviates from the maximum tracking operating point during the rotor speed recovery process, essentially operating under "unloaded" conditions, which affects the wind turbine's economic efficiency, this embodiment specifically selects rotor speed recovery time as an index to quantify the DFIG's ability to recover to its initial operating point (i.e., a point on the maximum power tracking curve) after participating in frequency adjustment.

[0138] The definitions of the above indicators are explained below:

[0139] (1) Rate of change of frequency

[0140] The expression for the rate of change of frequency is as follows:

[0141]

[0142] In the formula: f is the system frequency value; t is the frequency change time.

[0143] If the system frequency change rate is too large, it will trigger some protection devices in the power grid that operate based on the frequency change rate. This will cause the low-frequency load shedding action to cause the power supply to trip, resulting in a major frequency accident and affecting the normal operation of the power grid.

[0144] (2) Steady-state frequency deviation

[0145] When a power system transitions to a new steady state, the difference between the actual frequency and the nominal frequency is called the steady-state frequency deviation. The formula for expressing the steady-state frequency deviation is as follows:

[0146]

[0147] In the formula: f ref f0 is the nominal frequency; f0 is the actual steady-state system frequency.

[0148] (3) The lowest frequency point f min

[0149] The lowest frequency point can also reflect the change in the maximum frequency offset A of the system. The formula for calculating the lowest frequency point is as follows:

[0150]

[0151] (4) The lowest point of the second frequency drop

[0152] During the second frequency drop, the frequency corresponding to the lowest point is defined as the lowest point of the second frequency drop.

[0153] (5) Frequency second drop value Δf D

[0154] The frequency second drop value is defined as the difference between the frequency inflection point value before the lowest point of the frequency second drop and the lowest point. The formula for calculating the frequency second drop value is as follows:

[0155]

[0156] In the formula: This refers to the frequency inflection point value before the second lowest point; This is the lowest point of the second frequency drop.

[0157] (6) Rotor speed recovery time t0

[0158] The rotor speed recovery time is defined as the period from moment zero until the rotor speed recovers to a value less than 1% of the initial speed.

[0159] Furthermore, the frequency regulation capability assessment result referred to in this embodiment is a quantitative data point used to reflect the frequency regulation capability of the wind turbine. The better the frequency regulation capability assessment result, the stronger the frequency regulation capability of the wind turbine. Optionally, the result can be displayed in a visual form.

[0160] In some alternative implementations, the frequency modulation capability assessment results are correlated with the numerical range in which the indicators fall:

[0161] Step S21: Determine whether the frequency response capability evaluation index is within the corresponding frequency response index range, and determine whether the frequency modulation recovery capability evaluation index is within the corresponding frequency modulation recovery index range.

[0162] Step S22: The frequency response capability of the wind turbine is quantified based on the number of times each frequency response capability evaluation index falls within the frequency response index range, and the frequency regulation recovery capability of the wind turbine is quantified based on the number of times each frequency regulation recovery capability evaluation index falls within the corresponding frequency regulation recovery index range.

[0163] Step S23: Determine the frequency modulation capability evaluation result based on the frequency response capability quantization value and the frequency modulation recovery capability quantization value, wherein the frequency response capability quantization value and the frequency modulation recovery capability quantization value are both positively correlated with the frequency modulation capability evaluation result.

[0164] In step S21, the frequency response index range includes the frequency change rate range, the steady-state frequency deviation range, the frequency minimum point range, the frequency second drop minimum point range, and the frequency second drop value range; the frequency regulation recovery index range includes the rotor speed recovery time range.

[0165] In other words, each frequency evaluation index has its corresponding range. The more times an index falls within its corresponding range, the more the frequency regulation capability of the wind turbine unit meets expectations, i.e., the stronger it is.

[0166] In some alternative implementations, the frequency regulation capability assessment results are directly correlated with the numerical values ​​of the indicators. The frequency regulation capability assessment results include frequency response capability and frequency regulation recovery capability. The rules that the frequency regulation capability assessment results satisfy include: the frequency regulation capability assessment results are positively correlated with the frequency response capability and the frequency regulation recovery capability, that is, the stronger the frequency response capability and the frequency regulation recovery capability, the stronger the frequency regulation capability of the wind turbine.

[0167] The rules that the frequency response capability satisfies include: the frequency change rate, the steady-state frequency deviation, the frequency minimum point, and the frequency second drop value are all negatively correlated with the frequency response capability;

[0168] The frequency modulation recovery capability satisfies the following rule: the rotor speed recovery time is negatively correlated with the frequency modulation recovery capability.

[0169] In other words, the greater the frequency change rate, the steady-state frequency deviation, the minimum frequency point, and the second frequency drop value, the weaker the wind turbine's response capability when receiving disturbances, and vice versa; while the smaller the rotor speed recovery time, that is, the shorter the required recovery time, the stronger the wind turbine's frequency regulation recovery capability, and vice versa.

[0170] In the technical solution provided in this embodiment, wind speed, rotor speed, grid-connected active power output and pitch angle are selected as clustering indicators for clustering. The system frequency response capability and frequency regulation recovery capability are quantitatively evaluated using the wind turbine units obtained from the clustering as units. The frequency regulation capability of the wind turbine units is comprehensively evaluated based on the quantitative evaluation results of the two.

[0171] Second Embodiment

[0172] Based on the wind farm frequency regulation capability assessment method based on multi-dimensional dynamic aggregation proposed in the first embodiment, this embodiment constructs as follows: Figure 6 The diagram shown illustrates a multi-dimensional wind farm simulation aggregation. Based on this diagram, the method involved in the first embodiment is verified:

[0173] The improved Kernel-FCM clustering algorithm proposed in the first embodiment was used to group wind farms together with the traditional hard C-means clustering algorithm. The grouping results of the two clustering algorithms are shown in Table 4.

[0174] Table 4 Clustering Results

[0175]

[0176] Refer to Table 4, and Figure 7 The membership diagram shown illustrates the significant differences in clustering results among different clustering algorithms while maintaining consistent clustering values. Traditional hard C-means clustering is sensitive to initial centers, prone to local optima, and susceptible to noise outliers. In contrast, kernel-fuzzy clustering, through global particle swarm search, reduces dependence on initial centers, improving stability and accuracy; avoids local optima, enhancing global optimization capabilities; and introduces fuzziness to reduce the impact of noise outliers, improving robustness. To verify the effectiveness of the improved Kernel-FCM algorithm, its dynamic response characteristics under different operating conditions were compared and analyzed with single-machine multiplication, traditional hard C-means clustering, and detailed models.

[0177] (1) Comparative analysis under stable operating conditions

[0178] Using the wind speeds shown in Table 1 of the first embodiment as the input wind speeds for a stable wind farm, the dynamic response curves of different equivalent models at the PCC under stable operating conditions were obtained, as follows: Figure 8 , Figure 9 As shown in the figure. Through the above comparison, it can be seen that the equivalent method in this invention and the traditional hard C-means clustering method have the best simulation effect on active and reactive power, while the response curves of active and reactive power of the single-unit multiplication method are furthest from the detailed model. This is because the wind speed of each wind turbine in the wind farm is different, resulting in different operating states. The single-unit multiplication method cannot effectively simulate the actual operating conditions of the wind farm.

[0179] (2) Comparative analysis under gust conditions

[0180] Dynamic response curves of different equivalent models at PCC, such as Figure 10 , Figure 11 As shown in the diagram. The above analysis shows that as wind speed suddenly increases, the active power output of the wind farm also increases, while the reactive power output decreases, returning to its initial state after the gust disturbance ends. Furthermore, the comparison shows that the equivalent method of this invention performs best, the traditional hard C-means clustering method is slightly inferior, and the single-machine multiplication method has the worst simulation effect for the active and reactive power response curves.

[0181] In addition, as an implementation scheme, this embodiment also provides a frequency modulation method, which uses a cluster frequency modulation strategy to modulate the frequency of the wind turbines in the aforementioned wind farm frequency modulation capability assessment method based on multi-dimensional dynamic aggregation.

[0182] Specifically, in this embodiment, the cluster frequency regulation strategy is applied to the wind turbine generators obtained by the aforementioned method for frequency regulation, which has significant advantages over traditional frequency regulation methods. A detailed comparative analysis is as follows:

[0183] First, based on the wind turbine clustering results obtained using the improved Kernel-FCM clustering algorithm, an equivalent model was established for each cluster, as shown in Table 5:

[0184] Table 5. Wind turbine cluster indicators after clustering

[0185]

[0186] The simulation conditions are set as follows: wind speed is 10 m / s, system initial frequency is rated frequency 50 Hz, and at simulation t=5s, the load is suddenly increased by 40 MW to simulate the sudden power deficit in the system. The wind power penetration value is taken as 19.4%. In previous studies, the frequency dead zone is generally selected as (±0.02~0.05) Hz. In this embodiment, ±0.02 Hz is selected, and the parameter index values ​​after clustering are obtained as shown in Table 6.

[0187] Table 6. Parameter Calculation After Clustering

[0188]

[0189] A comparison was made between a simple wind turbine multiplication frequency regulation strategy and a cluster frequency regulation strategy. The simple wind turbine multiplication frequency regulation strategy includes the selection of the maximum index, the minimum index, and the average index.

[0190] The selection of maximum indexes refers to selecting the maximum wind speed, maximum mechanical power, maximum rotational speed, and maximum pitch angle of all wind turbines in the wind farm at a certain moment as multiplier indexes to calculate the frequency regulation capability of the wind farm under that condition.

[0191] Minimum index selection refers to selecting the minimum wind speed, minimum mechanical power, minimum rotational speed, and minimum pitch angle of all wind turbines in the wind farm at a certain moment as multiplier indexes, and similarly calculating the frequency regulation capability of the wind farm under this condition.

[0192] The selection of average indexes refers to calculating the average values ​​of wind speed, mechanical power, rotational speed, and pitch angle of all wind turbines in a wind farm at a certain moment, and using these values ​​as multiplier indicators to evaluate the overall frequency regulation capability of the wind farm.

[0193] Refer to Table 7 for the selection of multiplier indicators:

[0194] Table 7. Wind Power Multiplier Index per Unit

[0195]

[0196] By comparison, a simple wind turbine multiplication frequency regulation strategy was implemented. This strategy simply multiplies the frequency regulation capability of each wind turbine and then sums them to obtain the frequency regulation capability of the entire wind farm, resulting in the calculated values ​​of the single-turbine multiplication parameters shown in Table 8. This strategy does not consider the interaction between wind turbine units and the clustering effect.

[0197] Table 8 Calculation of Multiplication Parameters for a Single Machine

[0198]

[0199] Finally, based on the collected data or simulation results, the key indicators such as system frequency response characteristics, steady-state frequency deviation, minimum frequency point, minimum frequency second drop point, and frequency second drop value under the two frequency modulation strategies are calculated and compared.

[0200] Through comparative analysis, we obtained the following results: Figure 12 The diagram shown illustrates the frequency modulation effect after clustering, and as follows: Figure 13 The diagram showing the frequency modulation effect after multiplication of a single unit is illustrated. The thickness image of the entire unit group is fitted with the multiplied image using the cubic spline method. Figure 14 This is a comparison chart of the two. It is clear that the wind turbines after clustering have a higher frequency regulation capability. By comparing and analyzing the specific values ​​of various evaluation indicators under the two frequency regulation strategies, the significant advantage of the clustered frequency regulation strategy in improving the frequency regulation capability of wind farms can be clearly seen.

[0201] As an implementation scheme, this embodiment also provides a wind farm frequency regulation capability assessment method based on multi-dimensional dynamic aggregation as described above, and its application in wind farm frequency regulation capability assessment.

[0202] As one implementation scheme, Figure 15 This is a schematic diagram of the hardware operating environment of the computer system involved in the embodiments of this application.

[0203] like Figure 15 As shown, the computer system may include: a processor 1001, such as a CPU; a memory 1005; a user interface 1003; a network interface 1004; and a communication bus 1002. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0204] Those skilled in the art will understand that Figure 15 The computer system architecture shown does not constitute a limitation on the computer system and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0205] like Figure 15 As shown, the memory 1005, as a storage medium, may include an operating system, a network communication module, a user interface module, and computer programs. The operating system is a program that manages and controls the hardware and software resources of the computer system, as well as the operation of the computer programs and other software or programs.

[0206] exist Figure 15 In the computer system shown, the user interface 1003 is mainly used to connect to the terminal and communicate with the terminal; the network interface 1004 is mainly used to communicate with the backend server; and the processor 1001 can be used to call the computer program stored in the memory 1005.

[0207] In this embodiment, the computer system includes: a memory 1005, a processor 1001, and a computer program stored in the memory and executable on the processor, wherein:

[0208] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0209] S10, select wind speed, rotor speed, grid-connected active power output and pitch angle as clustering indicators, and divide the wind turbine group corresponding to each wind turbine in the wind farm according to the clustering indicators and the improved KFCM clustering algorithm.

[0210] S20, taking the wind turbine as a unit, select the frequency change rate, steady-state frequency deviation, frequency minimum point, frequency second drop minimum point and frequency second drop value as the system frequency response capability evaluation index of the wind turbine, and select the rotor speed recovery time as the frequency regulation recovery capability evaluation index of the wind turbine, so as to determine the frequency regulation capability evaluation result of the wind turbine based on the frequency response capability evaluation index and the frequency regulation recovery capability evaluation index.

[0211] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0212] Determine whether the frequency response capability evaluation index is within the corresponding frequency response index range, and determine whether the frequency modulation recovery capability evaluation index is within the corresponding frequency modulation recovery index range;

[0213] The frequency response capability of the wind turbine is quantified by the number of times each frequency response capability evaluation index falls within the frequency response index range, and the frequency regulation recovery capability of the wind turbine is quantified by the number of times each frequency regulation recovery capability evaluation index falls within the corresponding frequency regulation recovery index range.

[0214] The frequency modulation capability evaluation result is determined based on the quantized value of the frequency response capability and the quantized value of the frequency modulation recovery capability, wherein both the quantized value of the frequency response capability and the quantized value of the frequency modulation recovery capability are positively correlated with the frequency modulation capability evaluation result.

[0215] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0216] The rules that the frequency modulation capability evaluation results satisfy include: the frequency modulation capability evaluation results are positively correlated with the frequency response capability and the frequency modulation recovery capability;

[0217] The rules that the frequency response capability satisfies include: the frequency change rate, the steady-state frequency deviation, the frequency minimum point, and the frequency second drop value are all negatively correlated with the frequency response capability;

[0218] The frequency modulation recovery capability satisfies the following rule: the rotor speed recovery time is negatively correlated with the frequency modulation recovery capability.

[0219] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0220] S11, the optimal number of clusters is determined using the following formula. :

[0221]

[0222]

[0223] In the formula, n is the sample size of wind turbines. It is a set measure of dispersion within clusters, where k is the number of clusters. For sample weights, Let be the feature vector of the i-th sample, i.e., the wind turbine operating state parameters. For the c-th cluster center, For the sample To the cluster center The nuclear space distance between them For mathematical expectation, For clustering;

[0224] S12, Based on the optimal number of clusters, determine the minimization objective function of the improved KFCM clustering algorithm:

[0225]

[0226] In the formula, This represents the feature vector corresponding to the c-th cluster center in high-dimensional space. Let i be the weight of sample i. Represents the i-th data point With the c-th cluster center The distance between them is measured by m, which represents the fuzzy coefficient. This represents the membership matrix, where n represents the sample size of wind turbines. To determine the optimal number of clusters;

[0227] S13, classify the wind turbines based on the minimized objective function to obtain wind turbines of different classifications.

[0228] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0229] S100, collect wind turbine operating status parameters, including wind turbine speed, rotor speed, grid-connected active power output, pitch angle, mechanical torque, electromagnetic torque, stator current d-axis component, stator current q-axis component, rotor current d-axis component, rotor current q-axis component, stator active power output, stator reactive power output, and grid-connected reactive power output;

[0230] S200, Standardize the operating status parameters of the fan:

[0231]

[0232] Where, x ij This refers to the operating status parameters of the wind turbine. s represents the average value of the fan operating status parameters; j The standard deviation of the wind turbine's operating parameters. These are the standardized operating status parameters of the wind turbine;

[0233] S300, Calculate the covariance matrix of the wind turbine operating state parameters based on the standardized wind turbine operating state parameters:

[0234]

[0235] In the formula, This represents the operating status parameter of the j-th fan. mean and the operating status parameters of the kth fan mean The covariance between them, where m is the number of wind turbine operating state parameters;

[0236] S400, calculate the eigenvalues ​​of each wind turbine operating state parameter in the covariance matrix, calculate the cumulative contribution rate based on the eigenvalues, and select the first 4 cumulative contribution rates as the clustering index.

[0237] Furthermore, those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program includes program instructions and can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in a computer system to implement the process steps of the embodiments of the above methods.

[0238] Therefore, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the various steps of the wind farm frequency regulation capability assessment method based on multi-dimensional dynamic aggregation as described in the above embodiments.

[0239] The computer-readable storage medium can be any computer-readable storage medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), magnetic disk, or optical disk.

[0240] It should be noted that, since the storage medium provided in the embodiments of this application is the storage medium used to implement the methods of the embodiments of this application, those skilled in the art can understand the specific structure and variations of the storage medium based on the methods described in the embodiments of this application, and therefore will not be repeated here. All storage media used in the methods of the embodiments of this application fall within the scope of protection of this application.

[0241] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application 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, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0242] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0243] 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.

[0244] 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.

[0245] It should be noted that any reference signs placed between parentheses in the claims should not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. This application can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0246] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0247] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for evaluating frequency modulation capability of a wind farm based on multi-dimensional dynamic aggregation, characterized in that, The method comprises the following steps: S10, selecting wind speed, rotor speed, grid-connected active output and pitch angle as a clustering index, and dividing each wind turbine generator corresponding to the wind turbine generator in the wind farm according to the clustering index and an improved KFCM clustering algorithm; S20, selecting frequency change rate, steady-state frequency deviation, frequency minimum point, frequency secondary drop minimum point and frequency secondary drop value as the system frequency response capability evaluation index of the wind turbine generator, and selecting rotor speed recovery time as the frequency modulation recovery capability evaluation index of the wind turbine generator, so as to determine the frequency modulation capability evaluation result of the wind turbine generator according to the frequency response capability evaluation index and the frequency modulation recovery capability evaluation index; In the S20, the step of determining the frequency modulation capability evaluation result of the wind turbine generator according to the frequency response capability evaluation index and the frequency modulation recovery capability evaluation index comprises: determining whether the frequency response capability evaluation index is in the corresponding frequency response index interval, and determining whether the frequency modulation recovery capability evaluation index is in the corresponding frequency modulation recovery index interval; According to the number of each frequency response capability evaluation index in the frequency response index interval, the frequency response capability quantization value of the wind turbine generator is obtained, and according to the number of the frequency modulation recovery capability evaluation index in the corresponding frequency modulation recovery index interval, the frequency modulation recovery capability quantization value of the wind turbine generator is obtained; According to the frequency response capability quantization value and the frequency modulation recovery capability quantization value, the frequency modulation capability evaluation result is determined, wherein the frequency response capability quantization value and the frequency modulation recovery capability quantization value are positively correlated with the frequency modulation capability evaluation result; The frequency response index interval comprises a frequency change rate interval, a steady-state frequency deviation interval, a frequency minimum point interval, a frequency secondary drop minimum point interval and a frequency secondary drop value interval; The frequency modulation recovery index interval comprises a rotor speed recovery time interval.

2. The method of claim 1, wherein, In the S20, the frequency modulation capability evaluation result comprises frequency response capability and frequency modulation recovery capability; The rule satisfied by the frequency modulation capability evaluation result comprises that the frequency modulation capability evaluation result is positively correlated with the frequency response capability and the frequency modulation recovery capability; The rule satisfied by the frequency response capability comprises that the frequency change rate, the steady-state frequency deviation, the frequency minimum point and the frequency secondary drop value are negatively correlated with the frequency response capability; The rule satisfied by the frequency modulation recovery capability comprises that the rotor speed recovery time is negatively correlated with the frequency modulation recovery capability.

3. The method of claim 1, wherein, In the S10, the step of dividing each wind turbine generator corresponding to the wind turbine generator in the wind farm according to the clustering index and the improved KFCM clustering algorithm comprises: S11, the optimal cluster number is determined using the following formula : ； ； where n is the sample size of wind turbines, is the collective measure of dispersion within clusters, k is the number of clusters, is the sample weight, is the feature vector of the ith sample, i.e., the wind turbine operating state parameters, is the cth cluster center, is the sample to the cluster center in the kernel space, is the mathematical expectation, is the cluster; S12, determining a minimum objective function of the improved KFCM clustering algorithm according to the optimal clustering number: ； In the formula, represents the eigenvector corresponding to the cth cluster center in the high-dimensional space, is the weight of the sample i, represents the ith data point and the cth cluster center between the distance measure, m represents the fuzzy coefficient; represents the membership matrix, n represents the wind turbine sample amount, is the optimal cluster number; S13, classifying the wind turbine generators based on the minimum objective function to obtain wind turbine generators of different classifications.

4. The method of claim 1, wherein, The selection step of the clustering index comprises: S100, collecting fan operation state parameters, wherein the fan operation state parameters include fan speed, rotor speed, grid-connected active output, pitch angle, mechanical torque, electromagnetic torque, stator current d-axis component, stator current q-axis component, rotor current d-axis component, rotor current q-axis component, stator active output, stator reactive output and grid-connected reactive output; S200, performing standardization processing on the fan operation state parameters; ； wherein x ij is the fan operating state parameter data; is the mean of the fan operating state parameter; s j is the standard deviation of the fan operating state parameter, is the normalized fan operating state parameter; S300, calculating a covariance matrix of the fan operation state parameters according to the fan operation state parameters after the standardization processing; ； wherein represents the mean value of the jth fan operating state parameter represents the mean value of the kth fan operating state parameter represents the covariance between the mean value of the jth fan operating state parameter and the mean value of the kth fan operating state parameter , and m is the number of fan operating state parameters. S400, calculating eigenvalues of each fan operation state parameter in the covariance matrix, calculating cumulative contribution rates according to the eigenvalues, and selecting the first four cumulative contribution rates as the clustering indexes.

5. The method of claim 1, wherein, The wind turbine generators in the wind farm frequency modulation capacity evaluation method based on multi-dimensional dynamic aggregation adopt a cluster frequency modulation strategy for frequency modulation.

6. A computer system, characterized by The computer system comprises a memory, a processor and a computer program stored on the memory and executable on the processor, and the computer program, when executed by the processor, implements the steps of the wind farm frequency modulation capacity evaluation method based on multi-dimensional dynamic aggregation according to any one of claims 1 to 4.

7. A computer readable storage medium characterized in that, The computer readable storage medium stores a computer program, and the computer program, when executed by the processor, implements the steps of the wind farm frequency modulation capacity evaluation method based on multi-dimensional dynamic aggregation according to any one of claims 1 to 4.

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