A wind turbine coupling modeling and clustering analysis method for multiple disturbance scenarios

Abstract

The application provides a wind turbine coupling modeling and clustering analysis method for a multi-disturbance scene, and belongs to the technical field of offshore wind farm stability analysis, and solves the technical problem of lacking quantitative analysis of dynamic response modeling and power grid coupling relationship of the wind turbine under the multi-disturbance scene. The technical scheme comprises the following steps: S1, constructing an electromagnetic-mechanical hybrid model containing a direct-drive unit converter control loop; S2, based on the electromagnetic-mechanical hybrid model of the direct-drive unit converter control loop; S3, adopting a grouping aggregation algorithm based on disturbance energy integration; and S4, based on the clustering result. The application has the beneficial effect of improving the precision of wind farm modeling, facilitating the subsequent deployment of partition control and the design of key cluster priority regulation strategies, and providing a new modeling method and analysis tool for the safe and stable operation of a large-scale wind power grid-connected system.

Description

Technical Field

[0001] This invention relates to the field of offshore wind farm stability analysis technology, and in particular to a method for coupled modeling and clustering analysis of wind turbine generators for multi-disturbance scenarios. Background Technology

[0002] With the continuous increase in wind power penetration, large-scale wind farms are gradually becoming an important power source component in the power system. As the scale of offshore wind power continues to expand, the dynamic interaction between wind turbines and the power grid is becoming increasingly complex. The stability problem of wind farms under system disturbances is becoming increasingly prominent. In particular, under disturbance scenarios such as low voltage ride-through, voltage oscillation, and short circuit impact, wind farms may cause cascading failures due to asynchronous response or excessive coupling.

[0003] In traditional power systems, models based on synchronous generators exhibit good inertial response capabilities. However, wind turbines, relying on converter interfaces for grid connection, have significantly different inertial characteristics and control mechanisms, resulting in highly nonlinear dynamic behavior and inter-unit variations. Currently, two common modeling methods are employed in engineering: one is centralized modeling, which treats the entire wind farm as a single representative unit. While this method offers high model simplification and computational efficiency, it neglects the inconsistencies in dynamic response caused by differences in wind speed, geographical location, or control parameters among different units within the wind farm, leading to an inaccurate reflection of the wind farm's actual response to disturbances. The other method is refined multi-turbine modeling, which models each turbine separately and considers controller structure and parameters. However, due to the lack of effective clustering and identification mechanisms, all units are typically modeled and calculated uniformly, resulting in a large model size, high redundancy, and low simulation efficiency. Furthermore, some studies employ static aggregation methods, such as dividing wind farm clusters based on physical distance, electrical distance, or impedance. However, these methods are only applicable to steady-state or linear scenarios and cannot capture energy propagation paths under dynamic disturbance evolution, making it difficult to accurately identify the key turbine clusters or grid coupling paths most affected by disturbances. Therefore, a unified modeling method that can integrate multiple disturbance characteristics, turbine response characteristics, and grid coupling relationships is needed to support improved accuracy and optimized computational efficiency in the dynamic analysis of offshore wind farms. Summary of the Invention

[0004] The purpose of this invention is to provide a method for coupled modeling and clustering analysis of wind turbines in multi-disturbance scenarios. This method solves the technical problem of lack of quantitative analysis of the dynamic response modeling and grid coupling relationship of wind turbines in multi-disturbance scenarios, realizes dynamic grouping and coupling characteristic analysis of wind power clusters, and improves the accuracy and pertinence of wind farm stability assessment and control.

[0005] The inventive concept of this invention is as follows: This invention provides a method for coupled modeling and clustering analysis of wind turbines under multiple disturbance scenarios. This method constructs an electromagnetic-electromechanical hybrid model including the control loop of the direct-drive turbine converter; and based on this model, it builds a multi-disturbance joint simulation scenario library, including stability scenarios such as voltage stability, power oscillation, and short-circuit instability; it employs a clustering and aggregation algorithm based on disturbance energy integral to achieve clustering analysis of the dynamic response characteristics of wind turbines under different fault scenarios; based on the clustering results, a disturbance response coupling matrix is ​​constructed through the disturbance response center trajectory and the voltage disturbance response of grid nodes; and the coupling strength is quantitatively modeled using the disturbance energy transfer rate index. This method achieves a quantitative evaluation of the response capability of wind power clusters, providing theoretical support for subsequent regional control and fault diagnosis, and is applicable to the collaborative planning and operation optimization of large-scale wind power clusters and the power grid.

[0006] To achieve the aforementioned objectives, the present invention employs the following technical solution: a method for coupled modeling and clustering analysis of wind turbine generators in multi-disturbance scenarios, comprising the following steps:

[0007] S1. Construct an electromagnetic-electromechanical hybrid model that includes the control loop of the converter in the direct-drive unit, including the decoupling control equation of the current inner loop of the generator-side converter and the DC voltage outer loop control equation of the grid-side converter.

[0008] S2. Based on the electromagnetic-electromechanical hybrid model of the converter control loop of the drive unit, a multi-disturbance joint simulation scenario library is constructed, including stability scenarios such as voltage stability, power oscillation, and short-circuit instability.

[0009] S3. A clustering and aggregation algorithm based on disturbance energy integral is adopted to realize the clustering analysis of dynamic response characteristics of wind turbines under different fault scenarios.

[0010] S4. Based on the clustering results, a disturbance response coupling matrix is ​​constructed by using the disturbance response center trajectory and the voltage disturbance response of the power grid nodes. The coupling strength is then quantitatively modeled using the disturbance energy transfer rate index.

[0011] Furthermore, in S1, the electromagnetic-electromechanical hybrid model of the direct-drive unit converter control loop includes the current inner loop decoupling control equation of the generator-side converter and the DC voltage outer loop control equation of the grid-side converter.

[0012] In the synchronous rotating dq coordinate system, the inner loop controller for the current output of the machine-side converter is designed as a PI controller with decoupling compensation, and the control law is:

[0013]

[0014] In the formula i gd i represents the d-axis component of the machine-side current. gqThis represents the q-axis component of the machine-side current; This is the output reference value for the d-axis voltage of the machine-side converter. R is the output reference value for the q-axis voltage of the machine-side converter; s L is the stator equivalent resistance. s The stator equivalent inductance; ω s To synchronize the rotational angular frequency; For PI controller output compensation of d-axis error, The controller compensates for q-axis errors.

[0015] The outer loop of the DC voltage control system aims to stabilize the voltage, and sends the output current command to the inner loop. The voltage control structure of the outer loop is as follows:

[0016]

[0017] In the formula This serves as the current reference for the DC voltage control output. U is the reference value for the DC bus voltage. dc For actual measurement of DC bus voltage; K pv K is the proportional gain of the voltage outer loop PI controller. iv The integral gain of the outer-loop PI controller is given; the inner-loop current control structure is as follows:

[0018]

[0019] In the formula i cd i represents the d-axis component of the grid-side current. cq This represents the q-axis component of the grid-side current. This is for the PI controller to compensate for the d-axis current error. For compensation of q-axis current error by the PI controller; R f For the filter resistor, L f Let ω be the filter inductance and ω be the mains frequency.

[0020] Electromagnetic-electromechanical coupling is manifested in the speed feedback to the converter decoupling term and the current feedforward control:

[0021] ω s =N p ·ω m

[0022] Where N p For the extreme logarithm, ω m This represents the mechanical angular velocity of the generator rotor, reflecting the electromechanical coupling characteristics.

[0023] Furthermore, in S2, the voltage stability in the multi-disturbance joint simulation scenario library is used to simulate a sudden voltage drop at the wind farm bus, examining the voltage coupling control characteristics between the converter's DC voltage outer loop and the power grid, where a voltage step disturbance occurs at t = t0:

[0024] D voltage (t)=ΔU·[1-H(t-t0)·H(t0+T f -t)]

[0025] In the formula, ΔU is the voltage drop amplitude, t0 is the disturbance occurrence time, and T is the voltage drop amplitude. f Let H(t) be the duration of the disturbance, and H(t) be the unit step function.

[0026] Furthermore, in S2, the power oscillation in the multi-disturbance joint simulation scenario library is used to simulate low-frequency oscillations of the wind farm bus voltage or frequency, and to analyze the synchronous response capability of the wind turbine's grid-connected power.

[0027] Define the perturbation function:

[0028] D osc (t)=A osc ·sin(2πf osc t+φ)

[0029] In the formula, A osc f is the disturbance amplitude. osc φ is the disturbance frequency and φ is the initial phase.

[0030] To analyze the disturbance propagation path, the disturbance response function of the wind turbine output power is:

[0031] ΔP (i) (t)=α i ·D osc (t-τ i )+ε i (t)

[0032] In the formula, ΔP (i) (t) represents the active power disturbance response of the i-th wind turbine, α i Let τ be the sensitivity coefficient of the i-th wind turbine to oscillation disturbances. i For the disturbance propagation time delay, ε i (t) represents the residual of local disturbance of the wind turbine.

[0033] Furthermore, in S2, the short-circuit instability in the multi-disturbance joint simulation scenario library simulates short-circuit faults in the collector line or main transformer, examining the transient instability or non-recovery phenomena of the system under high current and rapid voltage changes:

[0034] The node voltage during a short circuit can be approximated as follows:

[0035]

[0036] In the formula, U sc (t) represents the node voltage during the short circuit, U pre R is the bus voltage before the fault. i For fault impedance, Z th The equivalent impedance of the node before the fault.

[0037] The peak value of the short-circuit current is expressed as:

[0038]

[0039] Furthermore, in S3, the clustering and aggregation algorithm based on disturbance energy integration obtains the disturbance intensity characterization index by calculating the integral energy of the disturbance generated by the main electrical quantities of each wind turbine over a period of time during a disturbance event.

[0040] Let the disturbance of a certain electrical quantity (such as current, active power, etc.) of the i-th wind turbine unit under the k-th disturbance scenario be:

[0041]

[0042] In the formula, Let be the dynamic response of the i-th wind turbine under disturbance scenario k; This is the reference response of the i-th wind turbine under normal operating conditions.

[0043] The integral of the perturbation energy is constructed as follows:

[0044]

[0045] In the formula, ω y (t) is the time-weighted function, T k This is the simulation window for scene k.

[0046] Suppose there are N wind turbines in a wind farm, and the disturbance scenario library contains K scenarios. Then the disturbance energy feature vector of each wind turbine is defined as:

[0047]

[0048] Combine the energy vectors of all wind turbines to form an energy characteristic matrix:

[0049]

[0050] Furthermore, in step S3, the clustering and aggregation algorithm based on perturbation energy integral uses the K-means clustering algorithm to obtain the clustering label set:

[0051] To eliminate the differences in the dimensions and scales of perturbation energy across different scenarios, Z-score normalization is used to process each column of the matrix:

[0052]

[0053] In the formula μ k and σ k These are the mean and standard deviation of the perturbation energy, respectively. The standardized matrix is ​​then obtained.

[0054] Randomly select G sample points from the dataset as initial cluster centers: For each wind turbine i, calculate its Euclidean distance to each cluster center and assign it to the nearest cluster:

[0055]

[0056] Recalculate the new centroid for each class and stop when the class assignment no longer changes:

[0057]

[0058] After clustering, the N wind turbines in the wind farm are divided into G clusters. Each wind turbine is assigned to a cluster, and the wind turbines in each cluster have similar disturbance energy characteristic responses under multiple disturbance scenarios.

[0059] Furthermore, in S4, the disturbance response center trajectory and the grid voltage node voltage disturbance response are obtained by extracting the dynamic characteristics of the wind turbine cluster's impact on the grid disturbance, thereby quantifying the dynamic impact of each wind turbine cluster on different grid nodes under different disturbance scenarios.

[0060] For the j-th unit cluster C j Its average disturbance response trajectory is:

[0061]

[0062] In the formula, |C j | represents the number of wind turbines in the cluster. The disturbance response of the voltage at the m-th critical node of the power grid is:

[0063]

[0064] In the formula, Let m be the voltage value of the grid node under disturbance k. Let be the steady-state value of node m.

[0065] Furthermore, in S4, the disturbance response coupling matrix and the disturbance energy transfer rate index are used to quantify and compare the comprehensive impact of different clusters on system stability under disturbance conditions, and to quantify the coupling strength.

[0066] By calculating the inner product integral of the output disturbance of the wind turbine cluster and the voltage disturbance of the critical node of the power grid, the relationship between the power grid node m and the wind turbine cluster C can be obtained. j The coupled perturbation energy under perturbation k is:

[0067]

[0068] Construct the overall coupling strength matrix:

[0069]

[0070] Where G represents the number of clusters and M represents the number of critical grid nodes. The larger each element in the matrix, the stronger the wind turbine cluster C under disturbance k. j The stronger the coupling with the power grid node m.

[0071] Furthermore, the calculation of wind turbine cluster C j The perturbation transmissibility is:

[0072]

[0073] Clusters with large values ​​are highly coupled clusters, which are sensitive to power grid disturbances and have strong coupling characteristics, requiring enhanced dynamic support; clusters with small values ​​are weakly coupled clusters, which have a weaker effect on disturbance propagation and limited dynamic impact, but may have a higher control margin and are suitable for deploying coordinated scheduling strategies.

[0074] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0075] 1. This invention constructs an electromagnetic-electromechanical hybrid dynamic model that includes the control loop of the direct-drive wind turbine converter, and on this basis, constructs a simulation library under multiple disturbance scenarios and defines the disturbance energy transfer rate index. This enables the clustering of the dynamic behavior of wind turbines under different fault scenarios, and further establishes the disturbance response vector and coupling matrix between the wind power cluster and the key nodes of the power grid, effectively identifying highly coupled and weakly coupled unit clusters.

[0076] 2. The method of the present invention not only overcomes the problem of ignoring the differences between units and the characteristics of disturbance evolution in the prior art, but also improves the accuracy of wind farm modeling, which facilitates the deployment of subsequent zonal control and the design of key cluster priority control strategies, and provides new modeling means and analysis tools for the safe and stable operation of large-scale wind power grid-connected systems. Attached Figure Description

[0077] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0078] Figure 1This is a flowchart of the wind turbine coupling modeling and clustering analysis method for multi-disturbance scenarios in this invention. Detailed Implementation

[0079] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. Of course, the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0080] Example 1

[0081] See Figure 1 This embodiment provides the following technical solution: a method for coupled modeling and clustering analysis of wind turbine units in multi-disturbance scenarios, comprising the following steps:

[0082] S1. Construct an electromagnetic-electromechanical hybrid model that includes the control loop of the converter in the direct-drive unit, including the decoupling control equation of the current inner loop of the generator-side converter and the DC voltage outer loop control equation of the grid-side converter.

[0083] S2. Based on the electromagnetic-electromechanical hybrid model of the converter control loop of the drive unit, a multi-disturbance joint simulation scenario library is constructed, including stability scenarios such as voltage stability, power oscillation, and short-circuit instability.

[0084] S3. A clustering and aggregation algorithm based on disturbance energy integral is adopted to realize the clustering analysis of dynamic response characteristics of wind turbines under different fault scenarios.

[0085] S4. Based on the clustering results, a disturbance response coupling matrix is ​​constructed by using the disturbance response center trajectory and the voltage disturbance response of the power grid nodes. The coupling strength is then quantitatively modeled using the disturbance energy transfer rate index.

[0086] Furthermore, in S1, the electromagnetic-electromechanical hybrid model of the control loop of the direct-drive unit converter includes the decoupling control equation of the current inner loop of the generator-side converter and the DC voltage outer loop control equation of the grid-side converter.

[0087] In the synchronous rotating dq coordinate system, the inner loop controller for the current output of the machine-side converter is designed as a PI controller with decoupling compensation, and the control law is:

[0088]

[0089] In the formula i gd i represents the d-axis component of the machine-side current. gq This represents the q-axis component of the machine-side current; This is the output reference value for the d-axis voltage of the machine-side converter. R is the output reference value for the q-axis voltage of the machine-side converter; s L is the stator equivalent resistance.s The stator equivalent inductance; ω s To synchronize the rotational angular frequency; For PI controller output compensation of d-axis error, The controller compensates for q-axis errors.

[0090] The outer loop of the DC voltage control system aims to stabilize the voltage, and sends the output current command to the inner loop. The voltage control structure of the outer loop is as follows:

[0091]

[0092] In the formula This serves as the current reference for the DC voltage control output. U is the reference value for the DC bus voltage. dc For actual measurement of DC bus voltage; K pv K is the proportional gain of the voltage outer loop PI controller. iv The integral gain of the outer-loop PI controller is given; the inner-loop current control structure is as follows:

[0093]

[0094] In the formula i cd i represents the d-axis component of the grid-side current. cq This represents the q-axis component of the grid-side current. This is for the PI controller to compensate for the d-axis current error. For compensation of q-axis current error by the PI controller; R f For the filter resistor, L f ω is the filter inductance, and ω is the mains frequency.

[0095] Electromagnetic-electromechanical coupling is manifested in the speed feedback to the converter decoupling term and the current feedforward control:

[0096] ω s =N p ·ω m

[0097] Where N p For the extreme logarithm, ω m This represents the mechanical angular velocity of the generator rotor, reflecting the electromechanical coupling characteristics.

[0098] Furthermore, in S2, the voltage stability scenario in the multi-disturbance co-simulation scenario library is used to simulate a sudden voltage drop at the wind farm bus, examining the voltage coupling control characteristics between the converter's DC voltage outer loop and the grid, where a voltage step disturbance occurs at t = t0:

[0099] D voltage (t)=ΔU·[1-H(t-t0)·H(t0+T f -t)]

[0100] In the formula, ΔU is the voltage drop amplitude, t0 is the disturbance occurrence time, and T is the voltage drop amplitude. f Let H(t) be the duration of the disturbance, and H(t) be the unit step function.

[0101] Furthermore, in S2, the power oscillation in the multi-disturbance co-simulation scenario library is used to simulate low-frequency oscillations of wind farm bus voltage or frequency, and to analyze the synchronous response capability of wind turbine grid-connected power:

[0102] Define the perturbation function:

[0103] D osc (t)=A osc ·sin(2πf osc t+φ)

[0104] In the formula, A osc f is the disturbance amplitude. osc φ is the disturbance frequency and φ is the initial phase.

[0105] To analyze the disturbance propagation path, the disturbance response function of the wind turbine output power is:

[0106] ΔP (i) (t)=α i ·D osc (t-τ i )+ε i (t)

[0107] In the formula, ΔP (i) (t) represents the active power disturbance response of the i-th wind turbine, α i Let τ be the sensitivity coefficient of the i-th wind turbine to oscillation disturbances. i For the disturbance propagation time delay, ε i (t) represents the residual of local disturbance of the wind turbine.

[0108] Furthermore, in S2, short-circuit instability in the multi-disturbance co-simulation scenario library simulates short-circuit faults in collector lines or main transformers, examining transient instability or non-recovery phenomena of the system under high current and rapid voltage changes:

[0109] The node voltage during a short circuit can be approximated as follows:

[0110]

[0111] In the formula, U sc (t) represents the node voltage during the short circuit, U pre R is the bus voltage before the fault. i For fault impedance, Z th The equivalent impedance of the node before the fault.

[0112] The peak value of the short-circuit current is expressed as:

[0113]

[0114] Furthermore, in S3, the clustering and aggregation algorithm based on disturbance energy integration calculates the integral energy of the disturbance generated by the main electrical quantities of each wind turbine over a period of time during a disturbance event, thereby obtaining the disturbance intensity characterization index:

[0115] Let the disturbance of a certain electrical quantity (such as current, active power, etc.) of the i-th wind turbine unit under the k-th disturbance scenario be:

[0116]

[0117] In the formula, Let be the dynamic response of the i-th wind turbine under disturbance scenario k; This is the reference response of the i-th wind turbine under normal operating conditions.

[0118] The integral of the perturbation energy is constructed as follows:

[0119]

[0120] In the formula, ω y (t) is the time-weighted function, T k This is the simulation window for scene k.

[0121] Suppose there are N wind turbines in a wind farm, and the disturbance scenario library contains K scenarios. Then the disturbance energy feature vector of each wind turbine is defined as:

[0122]

[0123] Combine the energy vectors of all wind turbines to form an energy characteristic matrix:

[0124]

[0125] Furthermore, in S3, the clustering and aggregation algorithm based on perturbation energy integral uses the K-means clustering algorithm to obtain the cluster label set:

[0126] To eliminate the differences in the dimensions and scales of perturbation energy across different scenarios, Z-score normalization is used to process each column of the matrix:

[0127]

[0128] In the formula μ k and σ k These are the mean and standard deviation of the perturbation energy, respectively. The standardized matrix is ​​then obtained.

[0129] Randomly select G sample points from the dataset as initial cluster centers: For each wind turbine i, calculate its Euclidean distance to each cluster center and assign it to the nearest cluster:

[0130]

[0131] Recalculate the new centroid for each class and stop when the class assignment no longer changes:

[0132]

[0133] After clustering, the N wind turbines in the wind farm are divided into G clusters. Each wind turbine is assigned to a cluster, and the wind turbines in each cluster have similar disturbance energy characteristic responses under multiple disturbance scenarios.

[0134] Furthermore, in S4, the disturbance response center trajectory and the voltage disturbance response of the grid voltage nodes are extracted to quantify the dynamic impact of each wind turbine cluster on different grid nodes under different disturbance scenarios by extracting the dynamic characteristics of the wind turbine cluster on the grid disturbance.

[0135] For the j-th unit cluster C j Its average disturbance response trajectory is:

[0136]

[0137] In the formula, |C j | represents the number of wind turbines in the cluster. The disturbance response of the voltage at the m-th critical node of the power grid is:

[0138]

[0139] In the formula, Let m be the voltage value of the grid node under disturbance k. Let be the steady-state value of node m.

[0140] Furthermore, in S4, the perturbation response coupling matrix and the perturbation energy transfer rate index are used to quantify and compare the comprehensive impact of different clusters on system stability under perturbation conditions, and to quantify the coupling strength:

[0141] By calculating the inner product integral of the output disturbance of the wind turbine cluster and the voltage disturbance of the critical node of the power grid, the relationship between the power grid node m and the wind turbine cluster C can be obtained. j The coupled perturbation energy under perturbation k is:

[0142]

[0143] Construct the overall coupling strength matrix:

[0144]

[0145] Where G represents the number of clusters and M represents the number of critical grid nodes. The larger each element in the matrix, the stronger the wind turbine cluster C under disturbance k. j The stronger the coupling with the power grid node m.

[0146] Furthermore, the calculation of wind turbine cluster C j The perturbation transmissibility is:

[0147]

[0148] Clusters with large values ​​are highly coupled clusters, which are sensitive to power grid disturbances and have strong coupling characteristics, requiring enhanced dynamic support; clusters with small values ​​are weakly coupled clusters, which have a weaker effect on disturbance propagation and limited dynamic impact, but may have a higher control margin and are suitable for deploying coordinated scheduling strategies.

[0149] Example 2

[0150] To verify the effectiveness of the clustering and aggregation method based on disturbance energy integral described in this invention, this embodiment selects the same wind farm model and multiple disturbance scenarios, uses the traditional method based on static feature quantities to cluster wind turbines, and compares it with the method of this invention.

[0151] The wind farm was set to include 50 direct-drive wind turbines. The disturbance type was a combination of voltage sag (30% amplitude, duration 0.3s) and power oscillation (0.5Hz, amplitude 10%). The number of clusters was set to 3 for all clusters. The variance of intra-cluster disturbance energy and the mean coupling strength with grid nodes were compared between the two methods. The comparison results are shown in Table 1 below:

[0152] Table 1 Comparison of results between the two methods

[0153]

[0154] The method of this invention yields higher consistency of intra-cluster perturbations, better stability of clustering results under multiple perturbation scenarios, and more significant dynamic coupling with key nodes of the power grid, enabling more accurate identification of unit clusters that have a significant impact on system dynamics.

[0155] Example 3

[0156] To verify that the disturbance response coupling matrix and energy transfer rate index constructed in this invention can effectively guide the optimization of control strategies, this embodiment selects two schemes, A and B, for a simple 3-node power grid to verify the role of the coupling modeling index in optimizing the control strategy. Scheme A: Based on the high-coupling cluster identification proposed in this invention, clusters with an energy transfer rate > 0.65 are selected to deploy the master control scheduling strategy; Scheme B: The coupling strength is not considered, and the control weights are evenly distributed. Both schemes enable reactive power support within 0.3s after a voltage drop disturbance and compare the average voltage recovery rate (unit: pu / s) of key nodes in the power grid 2s after the disturbance.

[0157] Table 2. Average voltage recovery rate at critical nodes of the power grid for the two schemes.

[0158]

[0159] By using the method of this invention to identify highly coupled clusters, it is possible to respond to disturbances more effectively, achieve faster voltage recovery, and achieve better regulation efficiency than the average allocation strategy. This demonstrates that the coupling modeling index proposed in this invention is suitable for the optimal allocation of dynamically regulated resources.

[0160] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for coupled modeling and cluster analysis of wind turbine units in multi-disturbance scenarios, characterized in that, Includes the following steps: S1. Construct an electromagnetic-electromechanical hybrid model that includes the control loop of the converter in the direct-drive unit, including the decoupling control equation of the current inner loop of the generator-side converter and the DC voltage outer loop control equation of the grid-side converter. S2. Based on the electromagnetic-electromechanical hybrid model of the converter control loop of the generator set, a multi-disturbance joint simulation scenario library is constructed, including voltage stability, short-circuit instability, and power oscillation scenarios. S3. A clustering and aggregation algorithm based on disturbance energy integral is adopted to realize the clustering analysis of dynamic response characteristics of wind turbines under different fault scenarios. S4. Based on the clustering results, a disturbance response coupling matrix is ​​constructed by using the disturbance response center trajectory and the voltage disturbance response of the power grid nodes. The coupling strength is then quantitatively modeled using the disturbance energy transfer rate index. In S4, the disturbance response center trajectory and the voltage disturbance response of the grid voltage node are obtained by extracting the dynamic characteristics of the wind power cluster's impact on the grid disturbance, and quantifying the dynamic impact of each wind turbine cluster on different grid nodes under different disturbance scenarios. For the j-th unit cluster Its average disturbance response trajectory is: ； In the formula, The dynamic response of the i-th wind turbine under disturbance scenario k is given. Let be the number of wind turbines in this cluster. The disturbance response of the voltage at the m-th critical node of the power grid is: ； In the formula, Let m be the voltage value of the grid node under disturbance k. Let m be the steady-state value of node m; In step S4, the disturbance response coupling matrix and the disturbance energy transfer rate index are used to quantify and compare the comprehensive impact of different clusters on system stability under disturbance conditions, and to quantify the coupling strength. By calculating the inner product integral of the output disturbance of the wind turbine cluster and the voltage disturbance of the critical node of the power grid, the relationship between the power grid node m and the wind turbine cluster can be obtained. The coupled perturbation energy under perturbation k is: ； Construct the overall coupling strength matrix: ； Where G is the number of clusters and M is the number of critical power grid nodes; Computational wind turbine cluster The perturbation transmissibility is: 。 2. The method for coupled modeling and clustering analysis of wind turbine units in multi-disturbance scenarios according to claim 1, characterized in that, In S1, the electromagnetic-electromechanical hybrid model of the control loop of the direct-drive unit converter includes the decoupling control equation of the current inner loop of the generator-side converter and the DC voltage outer loop control equation of the grid-side converter. In synchronous rotation In the coordinate system, the inner loop controller for the current output of the machine-side converter is designed as a PI controller with decoupling compensation, and the control law is: ； In the formula This represents the d-axis component of the machine-side current. This represents the q-axis component of the machine-side current. This is the output reference value for the d-axis voltage of the machine-side converter. This is the output reference value for the q-axis voltage of the machine-side converter; The stator equivalent resistance, The stator equivalent inductance; To synchronize the rotational angular frequency; For PI controller output compensation of d-axis error, PI controller output compensation for q-axis error; The outer loop of the DC voltage control system aims to stabilize the voltage, and sends the output current command to the inner loop. The voltage control structure of the outer loop is as follows: ； In the formula This serves as the current reference for the DC voltage control output. This is a reference value for the DC bus voltage. For actual measurement of DC bus voltage; The proportional gain of the voltage outer loop PI controller. The integral gain of the outer-loop PI controller is given; the inner-loop current control structure is as follows: ； In the formula The d-axis component of the grid-side current. This represents the q-axis component of the grid-side current. This is for the PI controller to compensate for the d-axis current error. This is for the PI controller to compensate for the q-axis current error; For filter resistors, For filter inductance, The power grid frequency; Electromagnetic-electromechanical coupling is manifested in the speed feedback to the converter decoupling term and the current feedforward control: ； in For extreme logarithms, This represents the mechanical angular velocity of the generator rotor, reflecting the electromechanical coupling characteristics.

3. The method for coupled modeling and clustering analysis of wind turbine units in multi-disturbance scenarios according to claim 1, characterized in that, In S2, the voltage stability function from the multi-disturbance joint simulation scenario library is used to simulate a sudden voltage drop at the wind farm bus, examining the voltage coupling control characteristics between the converter's DC voltage outer loop and the power grid. Voltage step disturbance occurs: ； In the formula, This refers to the voltage drop. The time of the disturbance The duration of the disturbance. It is a unit step function.

4. The method for coupled modeling and clustering analysis of wind turbine units in multi-disturbance scenarios according to claim 1, characterized in that, In step S2, the power oscillation in the multi-disturbance joint simulation scenario library is used to simulate low-frequency oscillations of the wind farm bus voltage or frequency, and to analyze the synchronous response capability of the wind turbine grid-connected power. Define the perturbation function: ； In the formula, For the disturbance amplitude, For the perturbation frequency, This is the initial phase; To analyze the disturbance propagation path, the disturbance response function of the wind turbine output power is: ； In the formula, Let be the active power disturbance response of the i-th wind turbine. Let be the sensitivity coefficient of the i-th wind turbine to oscillation disturbances. For the disturbance propagation time delay, This refers to the residual of local disturbances in the wind turbine.

5. The method for coupled modeling and clustering analysis of wind turbine units in multi-disturbance scenarios according to claim 1, characterized in that, In S2, the short-circuit instability in the multi-disturbance joint simulation scenario library simulates short-circuit faults in the collector line or main transformer, simulating transient instability or non-recovery phenomena of the system under rapid changes in high current and voltage: The node voltage during a short circuit is expressed as follows: ； In the formula, This refers to the node voltage during the short circuit. This refers to the bus voltage before the fault. For fault impedance, The node's equivalent impedance before the fault; The peak value of the short-circuit current is expressed as: 。 6. The method for coupled modeling and clustering analysis of wind turbine units in multi-disturbance scenarios according to claim 1, characterized in that, In S3, the clustering and aggregation algorithm based on disturbance energy integration obtains the disturbance intensity characterization index by calculating the integral energy of the disturbance generated by the main electrical quantities of each wind turbine over a period of time during a disturbance event. Let the disturbance of a certain electrical quantity of the i-th wind turbine be given by the k-th disturbance scenario: ； In the formula, Let be the dynamic response of the i-th wind turbine under disturbance scenario k; This is the reference response of the i-th fan under normal operating conditions; The integral of the perturbation energy is constructed as follows: ； In the formula, For time-weighted functions, For the simulation time window of scene k; Suppose there are N wind turbines in a wind farm, and the disturbance scenario library contains K scenarios. Then the disturbance energy feature vector of each wind turbine is defined as: ； Combine the energy vectors of all wind turbines to form an energy characteristic matrix: 。 7. The method for coupled modeling and clustering analysis of wind turbine units in multi-disturbance scenarios according to claim 6, characterized in that, In step S3, the clustering and aggregation algorithm based on perturbation energy integral uses the K-means clustering algorithm to obtain the clustering label set: Each column of the matrix is ​​processed using Z-score normalization: ； In the formula Let be the average value of the perturbation energy. Let be the standard deviation of the perturbation energy, and then obtain the standardized matrix. ; Randomly select G sample points from the dataset as initial cluster centers: For each wind turbine i, calculate its Euclidean distance to each cluster center and assign it to the nearest cluster: ； Recalculate the new centroid for each class and stop when the class assignment no longer changes: ； After clustering, the N wind turbines in the wind farm are divided into G clusters. Each wind turbine is assigned to a cluster, and the wind turbines in each cluster have similar disturbance energy characteristic responses under multiple disturbance scenarios.

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