Frequency response characteristic processing method and apparatus, and computer device

Abstract

This application relates to the field of power system technology, and in particular to a method, apparatus, and computer equipment for processing frequency response characteristics. The method includes: acquiring the operating status of each wind turbine; clustering the wind turbines according to their operating status to obtain multiple target wind turbine clusters; for any target wind turbine cluster, determining the aggregated direct-axis current of the cluster at various times based on the operating status of each wind turbine within the cluster and a preset frequency time-series curve; and for any target wind turbine cluster, determining the actual active power curve of the cluster based on the direct-axis component of the terminal voltage and the aggregated direct-axis current at various times. This method reduces computational load, thereby improving the processing speed of frequency response characteristics.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a method, apparatus and computer equipment for processing frequency response characteristics. Background Technology

[0002] The large-scale integration of wind power into the power system has led to a decrease in the proportion of traditional synchronous turbines, resulting in a reduction in power source inertia and frequency regulation capabilities, and an increase in the risk of frequency instability after power system disturbances. To enhance frequency stability, wind turbines are modified through control strategies to enable them to have frequency response capabilities such as inertial response and primary frequency regulation, which alleviates the frequency instability problem after system disturbances to some extent.

[0003] However, the frequency response characteristics of wind turbines are influenced by multiple factors, including control strategies and operating conditions, resulting in time-varying active power support characteristics. These characteristics are difficult to characterize using low-order models similar to synchronous turbines. Frequency response analysis of wind turbines requires retaining detailed models of multiple control components. However, the control parameters and operating conditions of different wind turbines within a wind farm vary significantly. Retaining detailed models of all turbines for system frequency analysis would lead to a sharp increase in computational load, making rapid calculation and analysis difficult.

[0004] It is evident that current methods for analyzing the frequency response characteristics of wind farms suffer from problems such as high computational complexity and slow analysis speed. Summary of the Invention

[0005] Therefore, it is necessary to provide a frequency response characteristic processing method, device, computer equipment, computer-readable storage medium, and computer program product to address the above-mentioned technical problems, so as to realize the overall frequency response characteristic analysis of multiple wind turbines under different control parameters and different operating conditions in a wind farm, and improve the speed of wind power frequency response characteristic analysis.

[0006] In a first aspect, this application provides a frequency response characteristic processing method, the method comprising:

[0007] Obtain the operating status of each wind turbine unit;

[0008] Based on the operating status of each wind turbine, the wind turbines are clustered to obtain multiple target wind turbine clusters;

[0009] For any of the target wind turbine clusters, the aggregate direct-axis current of the target wind turbine cluster at each moment is determined based on the operating status of each wind turbine in the target wind turbine cluster and the preset frequency timing curve.

[0010] For any of the target wind turbine clusters, the actual active power curve of the target wind turbine cluster is determined based on the direct-axis component of the terminal voltage and the aggregated direct-axis current of the target wind turbine cluster at each time.

[0011] In one embodiment, the step of clustering the wind turbines according to their operating states to obtain multiple target wind turbine clusters includes:

[0012] The operating status of each wind turbine is normalized to obtain the normalized operating status of each wind turbine.

[0013] Hierarchical clustering is used to cluster the wind turbines based on their normalized operating states, resulting in multiple target wind turbine clusters.

[0014] In one embodiment, determining the aggregate direct-axis current of the target wind turbine cluster at various times based on the operating status of each wind turbine within the target wind turbine cluster and a preset frequency timing curve includes:

[0015] Based on the operating status of each wind turbine in the target wind turbine cluster, determine the state variables and input variables of the target wind turbine cluster;

[0016] Based on the state variables and input variables of the target wind turbine cluster, determine the aggregation parameters corresponding to the target wind turbine cluster;

[0017] Based on the aggregated parameters corresponding to the target wind turbine cluster, the state variables and input variables of the target wind turbine cluster, and the preset frequency timing curve, the aggregated direct-axis current of the target wind turbine cluster at each time moment is determined.

[0018] In one embodiment, determining the aggregated direct-axis current of the target wind turbine cluster at each moment based on the aggregated parameters corresponding to the target wind turbine cluster, the state variables and input variables of the target wind turbine cluster, and a preset frequency-time curve includes:

[0019] Obtain the main shaft motion equation of the wind turbine and the active power reference value model of the turbine-side converter of the wind turbine;

[0020] Based on the main shaft motion equation and the active power reference value model, the state variables, input variables, aggregate parameters, and preset frequency time series curves corresponding to the target wind turbine cluster are processed to obtain the active power reference values ​​of the target wind turbine cluster at each time.

[0021] Based on the active power reference value, input quantity, aggregation parameter, and direct-axis component of the terminal voltage of the target wind turbine cluster at each time, the aggregated direct-axis current of the target wind turbine cluster at different times is determined.

[0022] In one embodiment, the process of processing the state variables, input variables, aggregate parameters, and preset frequency time-series curves corresponding to the target wind turbine cluster based on the main shaft motion equation and the active power reference value model to obtain the active power reference values ​​of the target wind turbine cluster at various times includes:

[0023] The mechanical power of the target wind turbine cluster is determined based on the calculation coefficient of the mechanical power, the state quantity corresponding to the target wind turbine cluster, and the input quantity.

[0024] Based on the main shaft motion equation and the active power reference value model, the state variables, aggregate parameters, mechanical power, and preset frequency time series curves corresponding to the target wind turbine cluster are processed to obtain the active power reference values ​​of the target wind turbine cluster at each time.

[0025] In one embodiment, the input includes an aggregated reactive power reference value, the aggregation parameter includes an aggregated current limit value, and the aggregated direct-axis current of the target wind turbine cluster at different times is determined based on the active power reference value, the input, the aggregation parameter, and the direct-axis component of the terminal voltage of the target wind turbine cluster at different times, including:

[0026] Based on the active power reference value of the target wind turbine cluster at each time moment and the direct-axis component of the terminal voltage, determine the direct-axis current of the target wind turbine cluster at each time moment.

[0027] The quadrature-axis current is determined based on the aggregated reactive power reference value and the direct-axis component of the terminal voltage.

[0028] The aggregated direct-axis current of the target wind turbine cluster at each time moment is determined based on the quadrature-axis current, the aggregated current limit, and the direct-axis current of the target wind turbine cluster at each time moment.

[0029] Secondly, this application also provides a frequency response characteristic processing device, the device comprising:

[0030] The status acquisition module is used to acquire the operating status of each wind turbine unit;

[0031] The clustering module is used to cluster the wind turbines according to their operating status to obtain multiple target wind turbine clusters.

[0032] The current calculation module is used to determine the aggregate direct-axis current of any target wind turbine cluster at each moment, based on the operating status of each wind turbine in the target wind turbine cluster and a preset frequency timing curve.

[0033] The power calculation module is used to determine the actual active power curve of any target wind turbine cluster based on the direct-axis component of the terminal voltage and the aggregated direct-axis current of the target wind turbine cluster at various times.

[0034] In one embodiment, the clustering module is further configured to normalize the operating status of each wind turbine to obtain the normalized operating status of each wind turbine; and to use hierarchical clustering to cluster each wind turbine based on the normalized operating status of each wind turbine to obtain multiple target wind turbine clusters.

[0035] In one embodiment, the current calculation module is further configured to determine the state variables and input variables of the target wind turbine cluster based on the operating states of each wind turbine within the target wind turbine cluster; determine the aggregation parameters corresponding to the target wind turbine cluster based on the state variables and input variables of the target wind turbine cluster; and determine the aggregated direct-axis current of the target wind turbine cluster at each time step based on the aggregation parameters corresponding to the target wind turbine cluster, the state variables and input variables of the target wind turbine cluster, and a preset frequency timing curve.

[0036] In one embodiment, the current calculation module is further configured to obtain the main shaft motion equation of the wind turbine and the active power reference value model of the turbine-side converter of the wind turbine; based on the main shaft motion equation and the active power reference value model, process the state variables, input variables, aggregation parameters, and preset frequency time-series curves corresponding to the target wind turbine cluster to obtain the active power reference value of the target wind turbine cluster at each time; and determine the aggregated direct-axis current of the target wind turbine cluster at different times based on the active power reference value, input variables, aggregation parameters, and the direct-axis component of the terminal voltage of the target wind turbine cluster at each time.

[0037] In one embodiment, the current calculation module is further configured to determine the mechanical power of the target wind turbine cluster based on the calculation coefficient of the mechanical power, the state quantity corresponding to the target wind turbine cluster, and the input quantity; and to process the state quantity, the aggregation parameter, the mechanical power, and the preset frequency time series curve corresponding to the target wind turbine cluster based on the main shaft motion equation and the active power reference value model to obtain the active power reference value of the target wind turbine cluster at each time.

[0038] In one embodiment, the input quantity includes an aggregated reactive power reference value, the aggregation parameter includes an aggregated current limit value, and the current calculation module is further configured to determine the direct-axis current of the target wind turbine cluster at each time based on the active power reference value of the target wind turbine cluster at each time and the direct-axis component of the terminal voltage; determine the quadrature-axis current based on the aggregated reactive power reference value and the direct-axis component of the terminal voltage; and determine the aggregated direct-axis current of the target wind turbine cluster at each time based on the quadrature-axis current, the aggregated current limit value, and the direct-axis current of the target wind turbine cluster at each time.

[0039] Thirdly, this application also provides a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0040] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method embodiments.

[0041] Fifthly, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0042] The aforementioned frequency response characteristic processing method, apparatus, computer equipment, computer-readable storage medium, and computer program product acquire the operating status of each wind turbine; based on the operating status of each wind turbine, the wind turbines are clustered to obtain multiple target wind turbine clusters; for any target wind turbine cluster, the aggregated direct-axis current of the target wind turbine cluster at each moment is determined based on the operating status of each wind turbine within the target wind turbine cluster and a preset frequency time-series curve; for any target wind turbine cluster, the actual active power curve of the target wind turbine cluster is determined based on the direct-axis component of the terminal voltage and the aggregated direct-axis current of the target wind turbine cluster at each moment. Compared to traditional technologies that use detailed models of multiple control links for system frequency analysis, the frequency response characteristic processing method, apparatus, computer equipment, computer-readable storage medium, and computer program product provided in this application group wind turbines according to their operating states to obtain multiple target wind turbine clusters. Based on the operating states of the target wind turbine clusters and preset frequency timing curves, the actual active power curve of the target wind turbine clusters is determined, simplifying the frequency response characteristic analysis of wind farms. By aggregating and modeling the frequency response control links, the overall frequency response characteristic analysis of multiple wind turbines under different control parameters and operating states within a wind farm can be achieved, reducing the computational load and thus improving the frequency response characteristic processing speed. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating a frequency response characteristic processing method in one embodiment.

[0044] Figure 2 This is a flowchart illustrating step 104 in one embodiment.

[0045] Figure 3 This is a flowchart illustrating step 106 in one embodiment.

[0046] Figure 4 This is a flowchart illustrating step 306 in one embodiment.

[0047] Figure 5 This is a flowchart illustrating step 404 in one embodiment.

[0048] Figure 6 This is a flowchart illustrating step 406 in one embodiment.

[0049] Figure 7 This is a flowchart illustrating a frequency response characteristic processing method in one embodiment.

[0050] Figure 8 This is a flowchart illustrating a frequency response characteristic processing method in one embodiment.

[0051] Figure 9 This is a diagram of an analog frequency signal in one embodiment.

[0052] Figure 10 This is a graph showing the actual active power values ​​of each target wind turbine cluster in one embodiment.

[0053] Figure 11 This is a comparison chart of the actual active power curve and the true value of active power in one embodiment.

[0054] Figure 12 This is a structural block diagram of a frequency response characteristic processing device in one embodiment.

[0055] Figure 13 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0057] The large-scale integration of wind power into the power system has led to a decrease in the proportion of traditional synchronous turbines, resulting in reduced power source inertia and frequency regulation capabilities, and an increased risk of frequency instability after system disturbances. To enhance frequency stability, wind turbines have undergone control strategy modifications to acquire frequency response capabilities such as inertial response and primary frequency regulation, which has alleviated frequency instability after system disturbances to some extent. However, because the frequency response characteristics of wind turbines are affected by multiple factors such as control strategies and operating states, their active power support characteristics exhibit time-varying features, making it difficult to characterize them using low-order models similar to synchronous turbines. Frequency response characteristic analysis of wind turbines requires retaining detailed models of multiple control links. However, the control parameters and operating states of different wind turbines within a wind farm vary significantly. Retaining detailed models of all turbines for system frequency analysis would lead to a sharp increase in computational load, hindering rapid calculation and analysis. Therefore, this paper proposes an aggregated modeling method for wind farm frequency response characteristic analysis, which is of great significance for improving the speed of wind power frequency response characteristic analysis and ensuring the accuracy of stable calculations for new energy power systems.

[0058] Existing methods for aggregated modeling of wind farm transient characteristics mostly focus on analyzing doubly-fed induction generators (DFIGs) and direct-drive wind turbines operating under traditional maximum power point tracking (MPPT) modes. These methods lack virtual inertia control or droop control within the turbines. The goal of these methods is to obtain the terminal voltage and current variation curves of multiple wind turbines during voltage transients, with a focus on the effects of low-voltage ride-through and grid-side converter control parameters. Aggregated turbine modeling is achieved through parameter weighting and equivalence, but no method for aggregated modeling of frequency response control components is proposed. Existing models are therefore insufficient for directly performing aggregated analysis of the frequency response characteristics of wind farms.

[0059] Based on this, the embodiments of this application provide a frequency response characteristic processing method, which simplifies the frequency response characteristic analysis of wind farms by grouping the units in the wind farm according to their operating status, and realizes the frequency response characteristic analysis of the entire field.

[0060] In one embodiment, such as Figure 1 As shown, a frequency response characteristic processing method is provided. This embodiment illustrates the application of this method to a server. It is understood that this method can also be applied to a terminal, and further to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0061] Step 102: Obtain the operating status of each wind turbine unit.

[0062] In this embodiment, the operating status of each wind turbine can be collected by the wind farm's main control station to form an operating status sample set for each wind turbine. The operating status may include turbine terminal wind speed, main shaft speed, blade pitch angle, active power, and reactive power. For example, the operating status sample set of a single wind turbine can be as shown in Formula (I).

[0063]

[0064] Where, x b This is a sample of the b-th wind turbine unit. For the terminal wind speed of the b-th wind turbine, For the main shaft speed of the b-th wind turbine, β b For the blade pitch angle of the b-th wind turbine, P b For the active power and Q of the bth wind turbine unit b This represents the reactive power of the b-th wind turbine unit.

[0065] Step 104: Based on the operating status of each wind turbine, the wind turbines are clustered to obtain multiple target wind turbine clusters.

[0066] In this embodiment of the application, hierarchical clustering can be used to cluster the normalized operating status samples of each wind turbine. By setting the set distance and number of clusters, the clusters with the closest set distance are continuously merged until the number of clusters reaches the requirement, resulting in multiple target wind turbine clusters after merging.

[0067] Step 106: For any target wind turbine cluster, determine the aggregate direct-axis current of the target wind turbine cluster at each moment based on the operating status of each wind turbine in the target wind turbine cluster and the preset frequency time sequence curve.

[0068] In this embodiment, the operating status and model parameters of each wind turbine within the target wind turbine cluster can be aggregated and solved based on their respective operating states to obtain the state variables, input variables, and aggregated parameters of the target wind turbine cluster. The model parameters of each wind turbine may include the active power coefficient for maximum power point tracking (MPPT), primary frequency regulation coefficient, virtual inertia coefficient, main shaft rotational inertia, and current limit. The model parameters corresponding to each wind turbine can be directly obtained from the power grid. The state variables of the target wind turbine cluster may include aggregated main shaft speed, and the input variables may include aggregated wind speed, aggregated pitch angle, and aggregated reactive power reference value. The aggregated parameters may include aggregated wind energy utilization coefficient, aggregated active power coefficient for MPPT, aggregated primary frequency regulation coefficient, aggregated virtual inertia coefficient, aggregated main shaft rotational inertia, and aggregated current limit.

[0069] The state variables, input variables, and aggregated parameters of the target wind turbine cluster, along with a pre-defined frequency time-series curve, are substituted into a dynamic model used for wind turbine frequency response characteristic analysis to obtain the aggregated d-axis (direct-axis) current of the aggregated turbines within the target wind turbine cluster at various times. The dynamic model is a pre-built model for wind turbine frequency response characteristic analysis. Different frequency drop events can be set; the frequency drop value changes over time, causing the frequency after the frequency drop to change over time. The change in frequency after the frequency drop over time is the pre-defined frequency time-series curve. The pre-defined frequency time-series curve is used to characterize different frequency drop events.

[0070] Step 108: For any target wind turbine cluster, determine the actual active power curve of the target wind turbine cluster based on the direct-axis component of the terminal voltage and the aggregated direct-axis current of the target wind turbine cluster at each moment.

[0071] In this embodiment, the actual active power generated by the target wind turbine cluster is positively correlated with the direct-axis component of the terminal voltage and the aggregated direct-axis current of the target wind turbine cluster at various times. The direct-axis component of the terminal voltage is a fixed value. After obtaining the direct-axis component of the terminal voltage, the aggregated terminal voltage can be determined based on the direct-axis component of the terminal voltage. The aggregated terminal voltage is the d-axis component of the voltage at the wind farm's grid connection point.

[0072] Ignoring the dynamic process of the phase-locked loop (PLL) within the converter, i.e., ensuring the PLL phase can flawlessly track the grid voltage phase, the product of the aggregator terminal voltage and the aggregated direct-axis current of the target wind turbine cluster at each moment is the actual active power generated at that moment, represented as an actual active power curve. The horizontal axis of the actual active power curve represents time (s), and the vertical axis represents actual active power (pu). For example, the actual active power generated by the target wind turbine cluster can satisfy the following formula (II).

[0073] P e.real =i d.agg ×u d.agg Formula (II)

[0074] Among them, P e.real i represents the actual active power generated by the target wind turbine cluster. d.agg To aggregate the direct-axis current, u d.agg This is the polymerizer terminal voltage. The polymerizer terminal voltage can satisfy the following formula (III).

[0075]

[0076] Where j represents the wind turbines within the i-th target wind turbine cluster, u d,j G is the direct-axis component of the terminal voltage of wind turbine j. j Let be the equivalent conductance of the line between wind turbine j and the grid connection point of the wind farm.

[0077] The aforementioned frequency response characteristic processing method obtains the operating status of each wind turbine; based on the operating status of each wind turbine, the wind turbines are clustered to obtain multiple target wind turbine clusters; for any target wind turbine cluster, the aggregated direct-axis current of the target wind turbine cluster at each moment is determined based on the operating status of each wind turbine within the target wind turbine cluster and the preset frequency time-series curve; for any target wind turbine cluster, the actual active power curve of the target wind turbine cluster is determined based on the direct-axis component of the terminal voltage and the aggregated direct-axis current of the target wind turbine cluster at each moment. Compared to traditional technologies that use detailed models of multiple control links for system frequency analysis, the frequency response characteristic processing method provided in this application groups wind turbines according to their operating states to obtain multiple target wind turbine clusters. Based on the operating states of the target wind turbine clusters and the preset frequency time-series curves, the actual active power curve of the target wind turbine clusters is determined. This simplifies the frequency response characteristic analysis of wind farms, aggregates and models the frequency response control links, and enables the overall frequency response characteristic analysis of multiple wind turbines under different control parameters and operating states within a wind farm. This reduces the computational load and thus improves the frequency response characteristic processing speed.

[0078] In one embodiment, such as Figure 2 As shown, in step 104, based on the operating status of each wind turbine, the wind turbines are clustered to obtain multiple target wind turbine clusters, which may include:

[0079] Step 202: Normalize the operating status of each wind turbine to obtain the normalized operating status of each wind turbine.

[0080] The operating status can include turbine end wind speed, main shaft speed, blade pitch angle, active power, and reactive power. The operating status of each wind turbine is normalized by using the maximum value of the corresponding operating status of all wind turbines in the field as the standard value for calculation. For example, the normalized operating status of each wind turbine can satisfy the following formula (IV).

[0081]

[0082] in, For the normalized turbine terminal wind speed of the b-th wind turbine, For the normalized main shaft speed of the b-th wind turbine, β b.unit For the normalized pitch angle of the b-th wind turbine, P b.unit For the normalized active power Q of the b-th wind turbine b.unit For the normalized reactive power of the b-th wind turbine, max v wThe maximum terminal wind speed and maximum w of each wind turbine unit r σ represents the maximum main shaft speed of each wind turbine, β represents the maximum pitch angle of each wind turbine, P represents the maximum active power of each wind turbine, and Q represents the maximum reactive power of each wind turbine.

[0083] Step 204: Using hierarchical clustering, based on the normalized operating status of each wind turbine, the wind turbines are clustered to obtain multiple target wind turbine clusters.

[0084] Among them, hierarchical clustering is adopted. Based on the normalized operating status of each wind turbine, each wind turbine is clustered. By setting the target number of clusters, the clusters with the closest set distance are continuously merged and the above process is repeated until the current number of clusters reaches the target number requirement.

[0085] For example, using hierarchical clustering, based on the normalized operating status of each wind turbine, the wind turbines are clustered to obtain multiple target wind turbine clusters. This can be divided into the following steps:

[0086] (1) Start clustering calculation. You can set the target number of clusters to k, temporarily store each wind turbine as a cluster, and denote the j-th cluster as C. j This application does not specify the target number of clusters for clustering, but can set it according to the specific needs of the actual application. For example, k can be 4.

[0087] (2) Construct the distance matrix M between clusters. The matrix elements in the distance matrix M satisfy the following formula (V).

[0088]

[0089] Where M(a,e) is the matrix element in the a-th row and e-th column of the inter-cluster distance matrix M, and C a Let n be the number of wind turbine units, and let n be the number of wind turbine units. Let be the set distance between two clusters, x be the normalized operating state of the wind turbine in the a-th cluster, and y be the normalized operating state of the wind turbine in the e-th cluster. dist(x,y) is the Euclidean distance, which satisfies the following formula (VI).

[0090]

[0091] Where d is the number of terms in the normalized operating state in formula (iv). For example, the operating state may include turbine end wind speed, main shaft speed, blade pitch angle, active power, and reactive power, then d is an integer from 1 to 5. dLet y be the d-th term of the normalized operating state of the wind turbine units in the a-th cluster. d Let d be the normalized operating state of a wind turbine in the e-th cluster. For example, if d is 1, then x1 is the turbine terminal wind speed of a wind turbine in the a-th cluster under normalized operating state, and y1 is the turbine terminal wind speed of a wind turbine in the e-th cluster under normalized operating state. Let x1 be the wind speed of any wind turbine in cluster a. d y is any wind turbine selected from cluster e. d Perform Euclidean distance calculation to obtain multiple Euclidean distances between cluster a and cluster e, and take the minimum value as the set distance between cluster a and cluster e.

[0092] (3) Set the initial number of clusters m = n.

[0093] (4) Determine whether the number of clusters meets the requirements. If m>k, continue to step (5). If m=k, go to step (9).

[0094] (5) Find the minimum value M(a,e) in the distance matrix between clusters M, and merge the clusters C. a and C e Generate new clusters

[0095] (6) For C e+1 All subsequent clusters were renumbered, C e+1 Decrease the cluster numbers of all subsequent clusters by 1.

[0096] (7) Delete the e-th row and e-th column of the inter-cluster distance matrix M, and recalculate the merged inter-cluster distance matrix M according to formula (v).

[0097] (8) Update m = m-1 and return to step (4).

[0098] (9) Output the number of each wind turbine unit in each cluster, end the clustering calculation, and take the final cluster as the target wind turbine unit cluster.

[0099] In this embodiment of the disclosure, the frequency response characteristic analysis of the wind farm is simplified by clustering the wind turbine units in the wind farm according to their operating status. This facilitates the processing of frequency response characteristics based on the clustered target wind turbine clusters, thereby reducing the amount of computation and increasing the computation speed while realizing the frequency response characteristic processing of the entire field.

[0100] In one embodiment, such as Figure 3 As shown, in step 106, based on the operating status of each wind turbine within the target wind turbine cluster and the preset frequency-time curve, the aggregate direct-axis current of the target wind turbine cluster at each moment is determined, which may include:

[0101] Step 302: Determine the state variables and input variables of the target wind turbine cluster based on the operating status of each wind turbine within the target wind turbine cluster.

[0102] The state variables of the target wind turbine cluster may include aggregated main shaft speed, and the input variables may include aggregated wind speed and aggregated pitch angle. The aggregated main shaft speed of the target wind turbine cluster is positively correlated with the main shaft speed of each wind turbine in its operating state, the aggregated wind speed is positively correlated with the terminal wind speed of each wind turbine in its operating state, and the aggregated pitch angle is positively correlated with the pitch angle of each wind turbine in its operating state. For example, the aggregated main shaft speed can satisfy the following formula (VII).

[0103]

[0104] in, Let m be the aggregate spindle speed of the i-th target wind turbine cluster. i w represents the number of wind turbines within the target wind turbine cluster i. r.j Let be the main shaft speed of wind turbine j within the target wind turbine cluster i.

[0105] The convergent wind speed can satisfy the following formula (VIII).

[0106]

[0107] in, Let v be the aggregate wind speed of the i-th target wind turbine cluster. w.j Let be the wind speed at the turbine terminal of wind turbine j within the target wind turbine cluster i.

[0108] The pitch angle of the polymer propeller can satisfy the following formula (IX).

[0109]

[0110] in, Let β be the aggregate pitch angle of the i-th target wind turbine cluster. j Let be the pitch angle of wind turbine j within the target wind turbine cluster i.

[0111] The input quantity may also include the aggregate reactive power reference value, which satisfies the following formula (x).

[0112]

[0113] in, The aggregate reactive power reference value, Q, for the i-th target wind turbine cluster. ref.j S is the reactive power reference value for wind turbine j within the target wind turbine cluster i. j This represents the rated capacity of the j-th wind turbine unit.

[0114] Step 304: Determine the aggregation parameters corresponding to the target wind turbine cluster based on the state variables and input variables of the target wind turbine cluster.

[0115] The aggregation parameters may include the aggregation wind energy utilization coefficient. The aggregation wind energy utilization coefficient can be calculated based on a function of the aggregation main shaft speed, aggregation wind speed, and aggregation blade pitch angle in the state variables. It should be noted that this application does not specifically limit the calculation method of the wind energy utilization coefficient. For example, the aggregation wind energy utilization coefficient can satisfy the following formula (XI).

[0116]

[0117] in, Let R be the aggregated wind energy utilization coefficient of the i-th target wind turbine cluster, and R be the radius of the wind turbine rotor.

[0118] Aggregated parameters may also include the active power coefficient for maximum power point tracking (MPPT) of the aggregated wind turbines, the aggregated primary frequency regulation coefficient, the aggregated virtual inertia coefficient, the aggregated main shaft rotational inertia, and the aggregated current limit. The aggregated parameters corresponding to the target wind turbine cluster can be calculated based on the model parameters of each wind turbine within the cluster. The model parameters for each wind turbine may include the active power coefficient for MPPT, the primary frequency regulation coefficient, the virtual inertia coefficient, the main shaft rotational inertia, and the current limit. These model parameters can be directly obtained from the power grid.

[0119] For example, the calculation of the aggregation parameter can satisfy the following formulas (12), (13), (14), (15), and (16).

[0120]

[0121]

[0122]

[0123]

[0124]

[0125] in, The active power coefficient for the aggregated wind turbine maximum power point tracking control of the i-th target wind turbine cluster. The aggregate primary frequency regulation coefficient of the i-th target wind turbine cluster, The aggregated virtual inertia coefficient of the i-th target wind turbine cluster, The aggregate principal shaft rotational inertia of the i-th target wind turbine cluster, Let i be the aggregate wind energy utilization coefficient of the i-th target wind turbine cluster. Let k be the aggregate current limit for the i-th target wind turbine cluster. opt.j Let k be the active power coefficient for the maximum power point tracking control of wind turbine j. d.j H is the primary frequency regulation coefficient of wind turbine j. j Let J be the virtual inertia coefficient of wind turbine j. ro. Let I be the moment of inertia of the main shaft of wind turbine j. lim.j This is the current limit for wind turbine j.

[0126] Step 306: Determine the aggregated direct-axis current of the target wind turbine cluster at each time step based on the aggregated parameters corresponding to the target wind turbine cluster, the state variables and input variables of the target wind turbine cluster, and the preset frequency timing curve.

[0127] Specifically, the aggregation parameters, state variables, and input variables of the target wind turbine cluster can be substituted into the wind turbine dynamic model to obtain a wind farm aggregation model based on frequency variation. Then, the preset frequency time series curve is input into the wind farm aggregation model based on frequency variation to obtain the aggregated direct-axis current of the target wind turbine cluster at each time.

[0128] In this embodiment, the state variables, input variables, and aggregation parameters of multiple target wind turbine clusters after clustering are solved, thereby simplifying the detailed model of each wind turbine in the wind farm into an aggregation model, and obtaining the aggregated direct-axis current of the target wind turbine cluster at each time. This simplifies the frequency response characteristic analysis of the wind farm, reduces the amount of calculation and improves the calculation speed while realizing the frequency response characteristic processing of the entire field.

[0129] In one embodiment, such as Figure 4 As shown, in step 306, the aggregated direct-axis current of the target wind turbine cluster at each moment is determined based on the aggregated parameters corresponding to the target wind turbine cluster, the state variables and input variables of the target wind turbine cluster, and the preset frequency-time curve. This may include:

[0130] Step 402: Obtain the main shaft motion equation of the wind turbine and the active power reference value model of the wind turbine's generator-side converter.

[0131] The frequency response control of wind turbine generators is mainly achieved through modifications to the active power reference value calculation process of the generator-side converter. Therefore, only the change in active power at the generator terminal under the electromechanical time scale needs to be considered. The dynamics of the AC current and DC voltage of the generator-side converter over the time scale can be ignored, and it is assumed that the actual active power generated by the grid-side converter completely tracks the active power reference value of the generator-side converter. The active power reference value model of the generator-side converter of wind turbine generators can satisfy the following formula (XVII).

[0132]

[0133] Among them, P e k is the active power reference value for the generator-side converter. opt k is the active power coefficient for the maximum power point tracking control of the wind turbine. d f is the primary frequency modulation coefficient. n Here, f is the rated frequency of the power grid, f is the terminal frequency of the wind turbine, and H is the virtual inertia coefficient of the wind turbine. n The rated frequency of the power grid is generally 50 Hz, and the terminal frequency f can be determined by a preset frequency timing curve. r The main shaft speed varies with time. The main shaft speed at the initial time t0 can be obtained directly, and the main shaft speed after time t0 can be calculated using the main shaft motion equation. The main shaft motion equation of the wind turbine can satisfy the following formula (XVIII).

[0134]

[0135] Among them, J ro P is the moment of inertia of the wind turbine's main shaft. m For mechanical power.

[0136] Step 404: Based on the main shaft motion equation and the active power reference value model, process the state variables, input variables, aggregate parameters, and preset frequency time series curves corresponding to the target wind turbine cluster to obtain the active power reference values ​​of the target wind turbine cluster at each time.

[0137] Specifically, for any target wind turbine cluster i, the state variables, input variables, and aggregate parameters of the target wind turbine cluster i can be substituted into the main shaft motion equation to obtain the aggregated main shaft wind speed at each time. Then, the aggregate parameters, the aggregated main shaft wind speed at each time, and the preset frequency time series curve are substituted into the active power reference value model to obtain the active power reference value of the target wind turbine cluster i at each time.

[0138] Step 406: Based on the active power reference value, input quantity, aggregate parameters, and direct-axis component of the terminal voltage of the target wind turbine cluster at each time, determine the aggregated direct-axis current of the target wind turbine cluster at different times.

[0139] This involves obtaining the current model of the grid-side converter and substituting the active power reference value, input quantity, aggregate parameters, and direct-axis component of the terminal voltage of the target wind turbine cluster at various times into the current model of the grid-side converter to obtain the aggregated direct-axis current of the target wind turbine cluster at different times.

[0140] This embodiment of the invention determines the aggregate direct-axis current of the target wind turbine cluster at different times based on the principal axis motion equation and the active power reference value model, thereby simplifying the analysis of the frequency response characteristics of the wind farm. While realizing the frequency response characteristics processing of the entire field, it reduces the amount of calculation and improves the calculation speed.

[0141] In one embodiment, such as Figure 5 As shown, in step 404, based on the main shaft motion equation and the active power reference value model, the state variables, input variables, aggregate parameters, and preset frequency time series curves corresponding to the target wind turbine cluster are processed to obtain the active power reference values ​​of the target wind turbine cluster at various times, which may include:

[0142] Step 502: Determine the mechanical power of the target wind turbine cluster based on the calculation coefficient of the mechanical power, the state variables corresponding to the target wind turbine cluster, and the input variables.

[0143] Among these factors, the mechanical power is positively correlated with its calculation coefficient, the state variables corresponding to the target wind turbine cluster, and the input variables. The calculation coefficient for mechanical power is generally taken as 7000–12000. The mechanical power of the target wind turbine cluster can satisfy the following formula (XIX).

[0144]

[0145] Among them, P m For mechanical power, k pm The coefficient for calculating mechanical power is k for all wind turbine units in the field. pm All coefficients are equal, C P The wind energy utilization coefficient is a function of the tip speed ratio and the blade pitch angle. w For the wind speed at the machine end, w r β is the main shaft speed, and β is the pitch angle. For any target wind turbine cluster, the aggregated main shaft speed corresponding to the target wind turbine cluster is... Aggregate wind energy utilization coefficient Aggregate wind speed Substituting into formula (19), the mechanical power of the target wind turbine cluster can be obtained.

[0146] Step 504: Based on the main shaft motion equation and active power reference value model, process the state variables, aggregate parameters, mechanical power, and preset frequency time series curves corresponding to the target wind turbine cluster to obtain the active power reference values ​​of the target wind turbine cluster at each time.

[0147] After obtaining the mechanical power of the target wind turbine cluster, the mechanical power can be substituted into the main shaft motion equation, i.e., formula (18), and combined with the state variables, aggregate parameters, preset frequency time sequence curves and formula (17) corresponding to the target wind turbine cluster, the active power reference value of the target wind turbine cluster at each time can be calculated.

[0148] In this embodiment, the mechanical power is determined based on the calculation coefficient of mechanical power, the state variables and input variables corresponding to the target wind turbine cluster, and then the active power reference value of the target wind turbine cluster at each time is obtained according to the mechanical power, the main shaft motion equation and the active power reference value model, thus simplifying the analysis of the frequency response characteristics of the wind farm.

[0149] In one embodiment, the input quantity includes a aggregated reactive power reference value, and the aggregated parameter includes an aggregated current limit value, such as... Figure 6 As shown, in step 406, the aggregated direct-axis current of the target wind turbine cluster at different times is determined based on the active power reference value, input quantity, aggregate parameters, and direct-axis component of the terminal voltage at each time point. This may include:

[0150] Step 602: Determine the direct-axis current of the target wind turbine cluster at each moment based on the active power reference value of the target wind turbine cluster at each moment and the direct-axis component of the terminal voltage.

[0151] The direct-axis current refers to the d-axis current without considering the current limiting mechanism. Based on the active power reference value and the direct-axis component of the generator terminal voltage, the d-axis current without considering the current limiting mechanism can be determined. The direct-axis current is positively correlated with the active power reference value and negatively correlated with the direct-axis component of the generator terminal voltage.

[0152] Step 604: Determine the quadrature-axis current based on the aggregated reactive power reference value and the direct-axis component of the generator terminal voltage.

[0153] Wherein, the quadrature axis current is the q-axis current without considering the current limiting element. Based on the reactive power reference value and the direct-axis component of the generator terminal voltage, the q-axis current without considering the current limiting element can be determined. For example, for a grid-side converter, the dynamic process of its inner current loop can be ignored, but due to the current limiting element, its d-axis and q-axis current limiting models need to be established. Calculated under the assumption of unrestricted power, the d-axis and q-axis currents of the grid-side converter without considering the current limiting element can satisfy the following formula (XX).

[0154]

[0155] Among them, i d.f i q.f These are the d-axis and q-axis currents, respectively, without considering the current limiting circuit.

[0156] Step 606: Determine the aggregated direct-axis current of the target wind turbine cluster at each time step based on the quadrature-axis current, the aggregated current limit, and the direct-axis current of the target wind turbine cluster at each time step.

[0157] The aggregated direct-axis current of the target wind turbine cluster can be determined based on the d-axis current (without considering current limiting), the q-axis current (without considering current limiting), and the aggregated current limit. If the sum of the square of the direct-axis current and the square of the quadrature-axis current is less than or equal to the square of the aggregated current limit, then the aggregated direct-axis current is equal to the direct-axis current. For example, the aggregated direct-axis current of the target wind turbine cluster at various times can satisfy the following formula (XXI).

[0158]

[0159] Among them, i d I is the direct-axis current of the target wind turbine cluster. lim This refers to the current limit. It should be noted that the aggregate current limit must be used during the calculations in formulas (XX) and (XXI). Substitute I lim Polymer terminal voltage u d.agg Substitute u d Aggregate reactive power reference value Substitute Q ref .

[0160] q-axis current i of the target wind turbine cluster q Formula (22) can be satisfied.

[0161]

[0162] In this embodiment, based on the d-axis current and q-axis current and current limit value without considering the current limiting stage, the aggregated direct-axis current of the target wind turbine cluster at each moment is further determined, so that the actual power generation curve can be obtained based on the aggregated direct-axis current at each moment and the preset frequency timing curve, thereby realizing the frequency response characteristic processing of the entire field.

[0163] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0164] For a better understanding of the embodiments of this application, see [link to relevant documentation]. Figure 7 and Figure 8 This application provides a most complete embodiment. Wind turbine units are clustered according to their operating states. State variables such as turbine terminal wind speed and main shaft speed are obtained from the wind farm's main control station. Hierarchical clustering is used to cluster the units according to a pre-set number of clusters. A multi-stage dynamic model of the wind turbine units suitable for frequency response characteristic analysis is constructed. The dynamic processes of the turbine-side converter, main shaft, and grid-side converter are analyzed separately, and state variables related to the time scale of the frequency process are extracted to construct a dynamic model of the wind turbine unit's frequency response characteristics. Aggregate numerical solutions are performed on the state variables and input variables of equivalent wind turbine units within each cluster. The state variables to be determined include the main shaft speed, and the input variables include wind speed, pitch angle, turbine terminal voltage, and reactive power setpoint. Aggregate numerical solutions are performed on the parameters of equivalent wind turbine units within each cluster. The parameters to be determined include the active power coefficient, primary frequency regulation coefficient, virtual inertia coefficient, main shaft rotational inertia, wind energy utilization coefficient, and converter current limit for the wind turbine unit's maximum power point tracking control. By setting different frequency drop events and inputting frequency time-series curves into a wind farm aggregation model, the active power of the entire wind farm is calculated, thus obtaining the frequency response characteristics of the wind farm. This embodiment simplifies the analysis of wind farm frequency response characteristics by grouping wind turbines within the wind farm according to their operating states. The proposed turbine clustering and dynamic model can reflect the active power characteristics of the wind farm under different turbine terminal frequency fluctuation conditions, achieving frequency response characteristic analysis of the entire wind farm.

[0165] This application discloses an aggregated modeling method for analyzing the frequency response characteristics of wind farms. This method can provide a real-time evaluation modeling method for the frequency support characteristics of the entire wind farm by dynamically modeling the wind farm, providing the wind farm frequency response capability curve for the power grid operation department, and thus providing a basic model for system frequency stability analysis. This application first performs cluster analysis based on physical quantities such as terminal wind speed, main shaft speed, and active power output of each wind turbine in the wind farm to group turbines with similar operating states. Second, for each group of multiple turbines, a single equivalent wind turbine is used to model the dynamic model of the equivalent turbine, obtaining the state variables and input variables of the dynamic model. The state variables include the main shaft speed, and the input variables to be determined include wind speed, pitch angle, terminal voltage, and reactive power setpoint. Then, based on the control structure and control parameters of each turbine, the control structure of the equivalent turbine is derived, and the frequency response control parameters of each group of equivalent turbines are calculated. Finally, combining the established wind farm aggregated dynamic model, the active power response curve of the entire farm is calculated based on the frequency time-series curves of different external grid disturbance events, obtaining the frequency response characteristics of the wind farm.

[0166] The frequency response characteristic processing method provided in this application embodiment is applied to the frequency response characteristic analysis of an actual wind farm. The specific application method is as follows: collect the operating status data of 60 wind turbine units in a wind farm in a certain region of China at a certain moment, including the turbine terminal wind speed, main shaft speed, blade pitch angle, active power, and reactive power. The specific results are shown in Table 1.

[0167] Table 1 Wind Turbine Unit Operating Status Table

[0168]

[0169] Based on their operating status data, the 60 wind turbine units in the wind farm were clustered using a hierarchical clustering method. The number of clusters was set to 4, and the wind turbine unit numbers in each cluster were obtained. The specific results are shown in Table 2.

[0170] Table 2 Clustering results of wind turbine components

[0171]

[0172] A transient simulation model of the wind farm was built in MATLAB Simulink software. Predicted frequency event curves were set for each wind turbine, and the simulated frequency signals were as follows: Figure 9 As shown in the figure. Through simulation, the active power curves at the turbine terminals of wind turbines within the four clusters were obtained, as follows. Figure 10 As shown. Following the frequency response characteristic processing method proposed in this application, equivalent wind turbine units within each cluster are obtained. Simulations are performed under the same simulated frequency signal to obtain the active power output of the wind farm aggregation model, which is then compared with the actual value, as shown. Figure 11As shown. Under the per-unit system, the mean square error of active power solved by the aggregation model is 1.6118 × 10⁻⁶. -7 The results show that the calculation error of the frequency response characteristic processing method proposed in this application is within the acceptable range for engineering applications.

[0173] Based on the same inventive concept, this application also provides a frequency response characteristic processing apparatus for implementing the frequency response characteristic processing method described above. The solution provided by this apparatus is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more frequency response characteristic processing apparatus embodiments provided below can be found in the limitations of the frequency response characteristic processing method described above, and will not be repeated here.

[0174] In one embodiment, see Figure 12 A frequency response characteristic processing device 1200 is provided. The frequency response characteristic processing device 1200 includes:

[0175] The status acquisition module 1202 is used to acquire the operating status of each wind turbine unit;

[0176] Clustering module 1204 is used to cluster each wind turbine according to its operating status to obtain multiple target wind turbine clusters.

[0177] The current calculation module 1206 is used to determine the aggregate direct-axis current of any target wind turbine cluster at each moment, based on the operating status of each wind turbine in the target wind turbine cluster and the preset frequency timing curve.

[0178] The power calculation module 1208 is used to determine the actual active power curve of any target wind turbine cluster based on the direct-axis component of the terminal voltage and the aggregated direct-axis current of the target wind turbine cluster at various times.

[0179] The aforementioned frequency response characteristic processing device acquires the operating status of each wind turbine; based on the operating status of each wind turbine, it clusters the wind turbines to obtain multiple target wind turbine clusters; for any target wind turbine cluster, it determines the aggregate direct-axis current of the target wind turbine cluster at each moment based on the operating status of each wind turbine within the target wind turbine cluster and the preset frequency time-series curve; for any target wind turbine cluster, it determines the actual active power curve of the target wind turbine cluster based on the direct-axis component of the terminal voltage and the aggregate direct-axis current of the target wind turbine cluster at each moment. Compared to traditional technologies that perform system frequency analysis using detailed models of multiple control links, the frequency response characteristic processing device provided in this application groups wind turbine units according to their operating states to obtain multiple target wind turbine clusters. Based on the operating states of the target wind turbine clusters and preset frequency timing curves, it determines the actual active power curve of the target wind turbine clusters, thus simplifying the frequency response characteristic analysis of wind farms. It aggregates and models the frequency response control links, thereby enabling the overall frequency response characteristic analysis of multiple wind turbine units under different control parameters and operating states within the wind farm, reducing the computational load and improving the frequency response characteristic processing speed.

[0180] In one embodiment, the clustering module 1204 is also used to normalize the operating status of each wind turbine to obtain the normalized operating status of each wind turbine; and to use hierarchical clustering to cluster each wind turbine based on the normalized operating status of each wind turbine to obtain multiple target wind turbine clusters.

[0181] In one embodiment, the current calculation module 1206 is further configured to determine the state variables and input variables of the target wind turbine cluster based on the operating status of each wind turbine in the target wind turbine cluster; determine the aggregate parameters corresponding to the target wind turbine cluster based on the state variables and input variables of the target wind turbine cluster; and determine the aggregated direct-axis current of the target wind turbine cluster at each time step based on the aggregate parameters corresponding to the target wind turbine cluster, the state variables and input variables of the target wind turbine cluster, and the preset frequency timing curve.

[0182] In one embodiment, the current calculation module 1206 is further used to obtain the main shaft motion equation of the wind turbine and the active power reference value model of the turbine-side converter; based on the main shaft motion equation and the active power reference value model, the state variables, input variables, aggregate parameters, and preset frequency time series curves corresponding to the target wind turbine cluster are processed to obtain the active power reference value of the target wind turbine cluster at each time; based on the active power reference value, input variables, aggregate parameters, and the direct-axis component of the terminal voltage of the target wind turbine cluster at each time, the aggregated direct-axis current of the target wind turbine cluster at different times is determined.

[0183] In one embodiment, the current calculation module 1206 is further used to determine the mechanical power of the target wind turbine cluster based on the calculation coefficient of the mechanical power, the state variables corresponding to the target wind turbine cluster, and the input variables; and to process the state variables, aggregate parameters, mechanical power, and preset frequency time series curves corresponding to the target wind turbine cluster based on the main shaft motion equation and the active power reference value model to obtain the active power reference value of the target wind turbine cluster at each time.

[0184] In one embodiment, the input quantities include aggregated reactive power reference values, and the aggregation parameters include aggregated current limits. The current calculation module 1206 is further configured to determine the direct-axis current of the target wind turbine cluster at each moment based on the active power reference values ​​of the target wind turbine cluster at each moment and the direct-axis component of the terminal voltage; determine the quadrature-axis current based on the aggregated reactive power reference values ​​and the direct-axis component of the terminal voltage; and determine the aggregated direct-axis current of the target wind turbine cluster at each moment based on the quadrature-axis current, the aggregated current limits, and the direct-axis current of the target wind turbine cluster at each moment.

[0185] Each module in the aforementioned frequency response characteristic processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0186] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 13 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a frequency response characteristic processing method.

[0187] Those skilled in the art will understand that Figure 13 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0188] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0189] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0190] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0191] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0192] 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 can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0193] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0194] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for processing frequency response characteristics, characterized in that, The method includes: Obtain the operating status of each wind turbine unit; Based on the operating status of each wind turbine, the wind turbines are clustered to obtain multiple target wind turbine clusters; For any of the target wind turbine clusters, the state variables and input variables of the target wind turbine clusters are determined based on the operating states of each wind turbine within the target wind turbine cluster. Based on the state variables and input variables of the target wind turbine cluster, determine the aggregation parameters corresponding to the target wind turbine cluster; Obtain the main shaft motion equation of the wind turbine and the active power reference value model of the turbine-side converter of the wind turbine; Based on the main shaft motion equation and the active power reference value model, the state variables, input variables, aggregated parameters, and preset frequency time series curves corresponding to the target wind turbine cluster are processed to obtain the active power reference values ​​of the target wind turbine cluster at each time. The input variables include aggregated reactive power reference values, and the aggregated parameters include aggregated current limits. Based on the active power reference value of the target wind turbine cluster at each time and the direct-axis component of the terminal voltage, the direct-axis current of the target wind turbine cluster at each time is determined. The quadrature-axis current is determined based on the aggregated reactive power reference value and the direct-axis component of the terminal voltage. Based on the quadrature-axis current, the aggregate current limit, and the direct-axis current of the target wind turbine cluster at each time, determine the aggregate direct-axis current of the target wind turbine cluster at each time. For any of the target wind turbine clusters, the actual active power curve of the target wind turbine cluster is determined based on the direct-axis component of the terminal voltage and the aggregated direct-axis current of the target wind turbine cluster at each time.

2. The method according to claim 1, characterized in that, Based on the operating status of each wind turbine, the wind turbines are clustered to obtain multiple target wind turbine clusters, including: The operating status of each wind turbine is normalized to obtain the normalized operating status of each wind turbine. Hierarchical clustering is used to cluster the wind turbines based on their normalized operating states, resulting in multiple target wind turbine clusters.

3. The method according to claim 1, characterized in that, The operating status includes turbine end wind speed, main shaft speed, blade pitch angle, active power, and reactive power.

4. The method according to claim 1, characterized in that, The process, based on the main shaft motion equation and the active power reference value model, processes the state variables, input variables, aggregate parameters, and preset frequency time-series curves corresponding to the target wind turbine cluster to obtain the active power reference values ​​of the target wind turbine cluster at various times, including: The mechanical power of the target wind turbine cluster is determined based on the calculation coefficient of the mechanical power, the state quantity corresponding to the target wind turbine cluster, and the input quantity. Based on the main shaft motion equation and the active power reference value model, the state variables, aggregate parameters, mechanical power, and preset frequency time series curves corresponding to the target wind turbine cluster are processed to obtain the active power reference values ​​of the target wind turbine cluster at each time.

5. A frequency response characteristic processing device, characterized in that, The device includes: The status acquisition module is used to acquire the operating status of each wind turbine unit; The clustering module is used to cluster the wind turbines according to their operating status to obtain multiple target wind turbine clusters. The current calculation module is used to determine the state variables and input variables of any target wind turbine cluster based on the operating status of each wind turbine within the cluster; determine the aggregation parameters corresponding to the target wind turbine cluster based on the state variables and input variables; obtain the main shaft motion equation of the wind turbine and the active power reference value model of the turbine-side converter; and, based on the main shaft motion equation and the active power reference value model, calculate the state variables, input variables, aggregation parameters, and a preset frequency timing curve corresponding to the target wind turbine cluster. The line is processed to obtain the active power reference value of the target wind turbine cluster at each time moment; the input quantity includes the aggregated reactive power reference value, and the aggregation parameter includes the aggregated current limit value; based on the active power reference value of the target wind turbine cluster at each time moment and the direct-axis component of the terminal voltage, the direct-axis current of the target wind turbine cluster at each time moment is determined; based on the aggregated reactive power reference value and the direct-axis component of the terminal voltage, the quadrature-axis current is determined; based on the quadrature-axis current, the aggregated current limit value, and the direct-axis current of the target wind turbine cluster at each time moment, the aggregated direct-axis current of the target wind turbine cluster at each time moment is determined; The power calculation module is used to determine the actual active power curve of any target wind turbine cluster based on the direct-axis component of the terminal voltage and the aggregated direct-axis current of the target wind turbine cluster at various times.

6. The apparatus according to claim 5, characterized in that, The clustering module is also used to normalize the operating status of each wind turbine to obtain the normalized operating status of each wind turbine; and to use hierarchical clustering to cluster each wind turbine based on the normalized operating status of each wind turbine to obtain multiple target wind turbine clusters.

7. The apparatus according to claim 5, characterized in that, The current calculation module is further configured to determine the mechanical power of the target wind turbine cluster based on the calculation coefficient of the mechanical power, the state quantity corresponding to the target wind turbine cluster, and the input quantity; and to process the state quantity, the aggregation parameter, the mechanical power, and the preset frequency time series curve corresponding to the target wind turbine cluster based on the main shaft motion equation and the active power reference value model to obtain the active power reference value of the target wind turbine cluster at each time.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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