Wind power plant model aggregation method and device for doubly-fed wind turbine generator and medium

Abstract

The invention discloses a doubly-fed wind turbine generator-oriented wind power plant model aggregation method and device and a medium. The method relates to the field of wind power generation, and solves the problem that wind power plant model aggregation cannot simultaneously guarantee analysis precision and reduce calculation burden requirements. Based on a hybrid wind power plant structure, a dynamic mathematical model of a single machine and a whole wind power plant is obtained, contribution of each WTG to power grid injection current is quantified into a dynamic weight coefficient, the coefficient is updated in real time along with operation conditions, and the limitation that only a single machine type is concerned or dynamic characteristics are simplified in a traditional method is broken through. The method comprises the following steps: constructing an equivalent model for electrical and control mechanical parameters of a wind driven generator, a wind energy capture characteristic of a turbine and an impedance distribution characteristic of a line, ensuring that an aggregation model retains a core physical mechanism of an original wind power plant, and replacing detailed modeling of dozens of to hundreds of WTGs with a single-unit equivalent model. And the problem of calculation burden of large-scale wind power plant modeling is solved on the premise of ensuring the precision.

Description

Technical Field

[0001] This application relates to the field of wind power generation, and in particular to a method, apparatus and medium for wind farm model aggregation for doubly fed wind turbines. Background Technology

[0002] With the increasing penetration of large-scale wind power in power systems, establishing accurate wind farm aggregation models has become a crucial step in formulating grid expansion plans, conducting grid connection studies, and performing dynamic and steady-state analyses of power systems. Since a wind farm typically comprises dozens to hundreds of wind turbine generators (WTGs), directly modeling each individual turbine in detail would drastically increase the computational burden of system analysis. Aggregating individual models allows for the comprehensive analysis of the entire wind farm.

[0003] However, existing wind farm aggregation methods have the following drawbacks: the full aggregation method assumes that all wind turbines have the same wind speed and parameters, and constructs an equivalent model by simply averaging the parameters. When there are large differences in wind speed in the region or the parameters of the wind turbines are not uniform, the accuracy drops significantly. The regional aggregation method divides the wind farm into multiple regions according to wind speed, and uses full aggregation for each region. Although it improves the accuracy, it increases the complexity of the model and is not suitable for large-scale wind farms. The semi-aggregation method keeps the mechanical parts intact and only aggregates the electrical parts. It requires detailed modeling of the mechanical parts of all wind turbines, which results in a large computational burden.

[0004] Therefore, providing an aggregation method that integrates the core physical information of wind farms and constructing an equivalent aggregation model that can both ensure analytical accuracy and significantly reduce computational burden is a technical problem that urgently needs to be solved by those in this field. Summary of the Invention

[0005] The purpose of this application is to provide a method, device and medium for wind farm model aggregation for doubly fed wind turbines, which solves the problem that wind farm model aggregation cannot simultaneously guarantee analysis accuracy and reduce computational burden.

[0006] To address the aforementioned technical problems, this application provides a method for wind farm model aggregation for doubly-fed induction generator (DFIG) wind turbines, comprising:

[0007] Based on the structure of a wind farm containing doubly fed induction motors and constant-speed induction motors, and the dynamic characteristics of the wind farm and wind turbine generators, a dynamic mathematical model of a single wind turbine generator and a wind farm is obtained.

[0008] Based on the dynamic mathematical model, combined with wind speed difference data and parameter difference data of each wind turbine, the contribution of each wind turbine to the grid injection current is quantified into a dynamic weighting coefficient; wherein, the dynamic weighting coefficient is dynamically updated according to the operating conditions.

[0009] Based on the dynamic weighting coefficients, the electrical, control, and mechanical parameters of each wind turbine are weighted and aggregated to construct an equivalent wind turbine model;

[0010] Based on wind speed distribution characteristics data, an equivalent turbine model is constructed according to the wind energy conversion principle and the aforementioned dynamic weighting coefficients.

[0011] Based on the line impedance distribution difference data and the power transmission mechanism, an equivalent line impedance model is constructed by combining the dynamic weighting coefficients.

[0012] The equivalent wind turbine model, the equivalent turbine model, and the equivalent line impedance model are coupled in multiple domains to form a wind farm aggregation model.

[0013] As an alternative, the above-mentioned wind farm model aggregation method for doubly-fed induction generators (DFIGs) derives a dynamic mathematical model of a single wind turbine and the wind farm based on the wind farm structure containing DFIGs and constant-speed induction generators, and the dynamic characteristics of the wind farm and wind turbines. This model includes:

[0014] Dynamic mathematical models of a single wind turbine generator are established in the aerodynamic, mechanical, electromagnetic, and control domains. The aerodynamic dynamic mathematical model is used to describe the conversion relationship between wind energy and mechanical power, the mechanical dynamic mathematical model is used to describe the speed and torque transmission characteristics, the electromagnetic dynamic mathematical model is used to describe the dynamic correlation of current, voltage, and magnetic flux, and the control dynamic mathematical model is used to characterize the power closed-loop control logic.

[0015] Based on the parallel topology of multiple wind turbines in a wind farm, the dynamic mathematical models of individual wind turbines are extended into a dynamic mathematical model of the entire wind farm.

[0016] As an optional approach, in the above-mentioned wind farm model aggregation method for doubly-fed induction generator (DFIG) wind turbines, based on the dynamic mathematical model and combined with wind speed difference data and parameter difference data of each wind turbine, the contribution of each wind turbine to the grid injection current is quantified into a dynamic weighting coefficient, including:

[0017] Based on the electromagnetic domain dynamic mathematical model in the dynamic mathematical model, the three-phase current is converted to the synchronous rotating dq coordinate system to obtain the active and reactive components.

[0018] Rotate the dq coordinate system to the d'-q' coordinate system so that the q'-axis current component of the total injected current is 0, while retaining the d'-axis current component;

[0019] The proportion of the d'-axis current component of each wind turbine to the total d'-axis current component of the wind farm is used as the dynamic weighting coefficient.

[0020] When the wind speed change exceeds the preset threshold or the control method changes, the dynamic weighting coefficient is recalculated and updated.

[0021] As an optional approach, the above-mentioned wind farm model aggregation method for doubly-fed induction generator (DFIG) wind turbines involves weighted aggregation of the electrical, control, and mechanical parameters of each wind turbine based on the dynamic weighting coefficients to construct an equivalent wind turbine model, including:

[0022] Extract the electrical parameters of each wind turbine, including magnetizing inductance and stator resistance;

[0023] Extract the control parameters of each wind turbine, including the proportional and integral coefficients of the PI controller;

[0024] Extract the mechanical parameters of each wind turbine, including the damping coefficient;

[0025] The electrical parameters, control parameters, and mechanical parameters are weighted and summed according to the dynamic weighting coefficients to obtain the equivalent electrical parameters, equivalent control parameters, and equivalent mechanical parameters.

[0026] An equivalent wind turbine model is constructed based on the equivalent electrical parameters, the equivalent control parameters, and the equivalent mechanical parameters.

[0027] As an optional approach, the above-mentioned wind farm model aggregation method for doubly-fed induction generator (DFIG) wind turbines constructs an equivalent turbine model based on wind speed distribution characteristics data, the wind energy conversion principle, and the aforementioned dynamic weighting coefficients, including:

[0028] Collect real-time wind speed data and corresponding swept area parameters of wind turbines in different areas of the wind farm;

[0029] Based on the principle of wind energy conversion, the equivalent wind speed is obtained by combining the dynamic weighting coefficient.

[0030] The optimal power coefficient and optimal tip speed ratio of each wind turbine are weighted and aggregated according to the dynamic weighting coefficient to obtain the equivalent optimal power coefficient and equivalent optimal tip speed ratio.

[0031] An equivalent turbine model is constructed by integrating the equivalent wind speed, the equivalent optimal power coefficient, and the equivalent optimal tip speed ratio.

[0032] As an alternative, the above-mentioned wind farm model aggregation method for doubly-fed induction generator (DFIG) wind turbines constructs an equivalent line impedance model based on line impedance distribution difference data and power transmission mechanisms, combined with the dynamic weighting coefficients, including:

[0033] Obtain the per-unit value of the line impedance from each wind turbine to the common node of the power grid;

[0034] The equivalent line impedance is obtained by weighting and aggregating the per-unit values ​​of each line impedance according to the dynamic weighting coefficient.

[0035] Construct an equivalent line impedance model based on the equivalent line impedance.

[0036] As an optional approach, the above-mentioned wind farm model aggregation method for doubly-fed induction generator (DFIG) wind turbines involves multi-domain coupling of the equivalent wind turbine model, the equivalent turbine model, and the equivalent line impedance model to form a wind farm aggregated model, including:

[0037] Establish the mechanical torque transmission link between the equivalent turbine model and the equivalent wind turbine model;

[0038] Establish the electrical connection relationship between the equivalent wind turbine model and the equivalent line impedance model;

[0039] Based on the multi-domain coupling logic of the overall dynamic mathematical model of the wind farm, the equivalent wind turbine model, the equivalent turbine model, and the equivalent line impedance model are integrated to form a wind farm aggregate model.

[0040] To address the aforementioned technical problems, this application also provides a wind farm model aggregation device for doubly-fed induction generator (DFIG) wind turbines, comprising:

[0041] The mathematical model building module is used to obtain dynamic mathematical models of a single wind turbine and a wind farm based on the structure of a wind farm containing doubly fed induction motors and constant speed induction motors, and the dynamic characteristics of the wind farm and wind turbine.

[0042] The weighting conversion module is used to quantify the contribution of each wind turbine to the grid injection current into a dynamic weighting coefficient based on the dynamic mathematical model, combined with wind speed difference data and parameter difference data of each wind turbine; wherein the dynamic weighting coefficient is dynamically updated according to the operating conditions.

[0043] The first aggregation module is used to perform weighted aggregation of the electrical parameters, control parameters and mechanical parameters of each wind turbine according to the dynamic weighting coefficient, and to construct an equivalent wind turbine model.

[0044] The second aggregation module is used to construct an equivalent turbine model based on wind speed distribution characteristic data, wind energy conversion principle and the dynamic weighting coefficients.

[0045] The third aggregation module is used to construct an equivalent line impedance model based on the line impedance distribution difference data and the power transmission mechanism, combined with the dynamic weighting coefficients.

[0046] The coupling module is used to couple the equivalent wind turbine model, the equivalent turbine model, and the equivalent line impedance model into a wind farm aggregate model through multi-domain coupling.

[0047] To address the aforementioned technical problems, this application also provides a wind farm model aggregation device for doubly-fed induction generator (DFIG) wind turbines, comprising:

[0048] Memory, used to store computer programs;

[0049] A processor is used to implement the steps of the above-described method for aggregating wind farm models for doubly-fed wind turbines when executing the computer program.

[0050] To address the aforementioned technical problems, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for wind farm model aggregation for doubly-fed induction generator (DFIG) wind turbines.

[0051] The wind farm model aggregation method for doubly-fed induction generators (DFIGs) provided in this application is based on a hybrid wind farm structure of DFIGs and constant-speed induction generators. It considers the dynamic characteristics of both the wind farm and individual WTGs, deriving dynamic mathematical models for both a single WTG and the entire wind farm. This method overcomes the limitations of traditional methods that focus only on a single turbine type or simplify dynamic characteristics. By combining wind speed and parameter difference data, the contribution of each WTG to the grid-injected current is quantified as a dynamic weighting coefficient, which is updated in real time according to operating conditions (such as wind speed changes and control method changes), solving the problem that traditional static weights cannot adapt to fluctuations in wind farm operating conditions. Equivalent models are constructed for the electrical and control mechanical parameters of the wind turbine, the wind energy capture characteristics of the turbine, and the impedance distribution characteristics of the line, covering the core elements of the entire wind farm chain from wind energy input to power output, avoiding the local simplification defects of regional aggregation or semi-aggregation methods. By coupling the mechanical, electromagnetic, and control domains to integrate the three equivalent models, the aggregated model ensures that the core physical mechanisms of the original wind farm are preserved, rather than simply superimposed parameters. By replacing the detailed modeling of dozens to hundreds of WTGs with an equivalent model of a single unit, and avoiding the multi-region splitting of the regional aggregation method, the computational burden problem of large-scale wind farm modeling is solved while ensuring accuracy.

[0052] In addition, this application also provides an apparatus and medium that correspond to the above-mentioned wind farm model aggregation method for doubly fed wind turbines, with the same effect. Attached Figure Description

[0053] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1A flowchart illustrating a wind farm model aggregation method for doubly-fed induction generator (DFIG) wind turbines is provided in this application embodiment.

[0055] Figure 2 An equivalent dq-axis current model and a d'-q'-axis schematic diagram of a wind farm are provided for embodiments of this application;

[0056] Figure 3 A structural diagram of a wind farm model aggregation device for doubly-fed wind turbines provided in this application embodiment;

[0057] Figure 4 This is a structural diagram of another wind farm model aggregation device for doubly fed wind turbines provided in an embodiment of this application. Detailed Implementation

[0058] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of this application.

[0059] The core of this application is to provide a method, device, and medium for wind farm model aggregation for doubly fed wind turbines.

[0060] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0061] This application provides a method for wind farm model aggregation for doubly-fed induction generator (DFIG) wind turbines, such as... Figure 1 As shown, it includes:

[0062] S11: Based on the structure of a wind farm containing a doubly fed induction motor and a constant speed induction motor, and the dynamic characteristics of the wind farm and wind turbine, a dynamic mathematical model of a single wind turbine and a wind farm is obtained.

[0063] S12: Based on the dynamic mathematical model, combined with wind speed difference data and parameter difference data of each wind turbine, the contribution of each wind turbine to the grid injection current is quantified into a dynamic weighting coefficient; wherein, the dynamic weighting coefficient is dynamically updated according to the operating conditions.

[0064] S13: Based on the dynamic weighting coefficients, the electrical parameters, control parameters and mechanical parameters of each wind turbine are weighted and aggregated to construct an equivalent wind turbine model;

[0065] S14: Based on wind speed distribution data, construct an equivalent turbine model according to the wind energy conversion principle and dynamic weighting coefficients;

[0066] S15: Based on the line impedance distribution difference data and power transmission mechanism, an equivalent line impedance model is constructed by combining dynamic weighting coefficients.

[0067] S16: The equivalent wind turbine model, equivalent turbine model, and equivalent line impedance model are coupled in multiple domains to form a wind farm aggregation model.

[0068] In step S11, firstly, this embodiment obtains a dynamic mathematical model of a single wind turbine and the wind farm based on the wind farm structure containing doubly-fed induction motors and constant-speed induction motors, and the dynamic characteristics of the wind farm and wind turbines. This model covers the two common core unit types in wind farms: doubly-fed induction motors and constant-speed induction motors. Since actual wind farms often mix and configure different types of units to adapt to diverse operational needs, this model construction method can more realistically reflect the compositional characteristics of wind farms.

[0069] Secondly, a dynamic mathematical model of a single WTG unit and a dynamic mathematical model of the wind farm as a whole were constructed to understand both the physical characteristics of individual units (such as wind energy conversion and electromagnetic response) and the coupling relationships when multiple units are connected in parallel (such as current superposition and line interaction). Together, these models provide a complete physical mechanism support for subsequent aggregation.

[0070] Step S12, based on the dynamic mathematical model and combining wind speed difference data and parameter difference data of each wind turbine, quantifies the contribution of each wind turbine to the grid injection current as a dynamic weighting coefficient. The contribution quantification is based on the actual impact on the grid injection current. Since wind speed differences lead to different unit output power, and parameter differences lead to different current characteristics (such as phase and amplitude), weighting based solely on capacity allocation cannot reflect the true contribution. The quantification method based on current contribution is more in line with the characteristics of wind farms in the grid.

[0071] Furthermore, the dynamic weighting coefficients are updated dynamically according to the operating conditions. Since changes in operating conditions (such as wind speed fluctuations and control mode switching) directly change the current output of each unit, if the weights remain unchanged, the aggregation accuracy will inevitably decrease. Based on the above principle, the dynamic update mechanism ensures that the weighting coefficients always match the actual contributions. This does not mean that the coefficients are fixed or updated periodically. The update frequency can be adjusted according to the degree of change in operating conditions, and this embodiment does not impose strict limitations on it.

[0072] Step S13 performs weighted aggregation of the electrical, control, and mechanical parameters of each wind turbine generator based on dynamic weight coefficients. This aggregation is a weighted summation with dynamic weights. Since electrical parameters (such as excitation inductance) affect electromagnetic response, control parameters (such as proportional-integral (PI) coefficients) affect regulation characteristics, and mechanical parameters (such as damping coefficients) affect torque transmission, aggregating the three types of parameters separately ensures that the equivalent model is consistent with the original wind farm characteristics in the electromagnetic, control, and mechanical dimensions. This does not mean that only one type of parameter needs to be aggregated.

[0073] Step S14 involves constructing an equivalent turbine model based on wind speed distribution data, the wind energy conversion principle, and dynamic weighting coefficients. The wind energy conversion principle determines the nonlinear relationship between wind speed, swept area, and captured power. Therefore, the construction of the equivalent turbine model must follow this physical law, rather than directly weighting the wind speed. The introduction of dynamic weights ensures that the contribution of wind speed in different areas to the overall wind energy capture is reasonably allocated. At this point, the equivalent model can reflect the overall wind energy input characteristics of the wind farm.

[0074] Step S15 constructs an equivalent line impedance model based on the line impedance distribution difference data and the power transmission mechanism, combined with dynamic weighting coefficients. The power transmission mechanism determines that line impedance will affect voltage drop and power loss. Since the actual impact of the line impedance of different units on the power grid is related to their current contribution (dynamic weighting coefficients), the aggregation method based on dynamic weights is more accurate than simple averaging.

[0075] Step S16 couples the equivalent wind turbine model, equivalent turbine model, and equivalent line impedance model across multiple domains. The energy flow of a wind farm is a continuous process of wind energy → mechanical energy → electrical energy → power grid, involving cross-domain interactions between the aerodynamic, mechanical, and electromagnetic domains. Therefore, multi-domain coupling is not simply piecing together models, but rather establishing the mechanical torque transmission between the turbine and generator (mechanical energy coupling) and the electrical connection between the generator and the line (electrical energy coupling), and achieving coordinated response through the physical laws (such as torque balance and power conservation) in the dynamic mathematical model of the wind farm.

[0076] The wind farm model aggregation method for doubly-fed induction generators (DFIGs) provided in this application is based on a hybrid wind farm structure of DFIGs and constant-speed induction generators. It considers the dynamic characteristics of both the wind farm and individual WTGs, deriving dynamic mathematical models for both a single WTG and the entire wind farm. This method overcomes the limitations of traditional methods that focus only on a single turbine type or simplify dynamic characteristics. By combining wind speed and parameter difference data, the contribution of each WTG to the grid-injected current is quantified as a dynamic weighting coefficient, which is updated in real time according to operating conditions (such as wind speed changes and control method changes), solving the problem that traditional static weights cannot adapt to fluctuations in wind farm operating conditions. Equivalent models are constructed for the electrical and control mechanical parameters of the wind turbine, the wind energy capture characteristics of the turbine, and the impedance distribution characteristics of the line, covering the core elements of the entire wind farm chain from wind energy input to power output, avoiding the local simplification defects of regional aggregation or semi-aggregation methods. By coupling the mechanical, electromagnetic, and control domains to integrate the three equivalent models, the aggregated model ensures that the core physical mechanisms of the original wind farm are preserved, rather than simply superimposed parameters. By replacing the detailed modeling of dozens to hundreds of WTGs with an equivalent model of a single unit, and avoiding the multi-region splitting of the regional aggregation method, the computational burden problem of large-scale wind farm modeling is solved while ensuring accuracy.

[0077] According to the above embodiments, in a further specific embodiment, a dynamic mathematical model of a single wind turbine and a wind farm is obtained based on the wind farm structure containing a doubly-fed induction motor and a constant-speed induction motor, and the dynamic characteristics of the wind farm and wind turbine, including:

[0078] Dynamic mathematical models of a single wind turbine generator are established in the aerodynamic, mechanical, electromagnetic, and control domains. The aerodynamic dynamic mathematical model is used to describe the conversion relationship between wind energy and mechanical power, the mechanical dynamic mathematical model is used to describe the speed and torque transmission characteristics, the electromagnetic dynamic mathematical model is used to describe the dynamic correlation of current, voltage, and magnetic flux, and the control dynamic mathematical model is used to characterize the power closed-loop control logic.

[0079] Based on the parallel topology of multiple wind turbines in a wind farm, the dynamic mathematical models of individual wind turbines are extended into a dynamic mathematical model of the entire wind farm.

[0080] In this embodiment, the steps of constructing a dynamic mathematical model based on the hybrid unit structure and dynamic characteristics are refined into two parts: the construction of a multi-domain sub-model for a single WTG and the expansion of the overall wind farm model.

[0081] The purpose of domain-based modeling is to accurately match the actual physical links of WTG energy conversion. The energy conversion process of WTG is essentially a cross-domain transfer of wind energy → mechanical energy → electrical energy, and each link has unique dynamic laws. Therefore, this embodiment divides a single model into four subdomains to ensure that each physical process has a corresponding mathematical expression to support it.

[0082] The aerodynamic sub-model focuses on the wind energy capture process, quantifying the impact of wind speed fluctuations on mechanical input by describing the conversion relationship between wind energy and mechanical power (such as aerodynamic equations based on tip speed ratio and power coefficient).

[0083] The mechanical domain sub-model takes over the mechanical power output of the aerodynamic domain. By describing the characteristics of rotational speed and torque transmission (such as torque balance equations that include rotational inertia and damping coefficient), it simulates the dynamic response of the main shaft and gearbox (such as the rotational speed lag characteristics when wind speed changes abruptly).

[0084] The electromagnetic domain sub-model targets the core aspects of power generation. By describing the dynamic relationship between current, voltage, and magnetic flux (such as the dq-axis differential equation of a doubly fed induction motor), it accurately reflects the electromagnetic transient characteristics of WTG (such as rotor current fluctuations when the grid voltage drops).

[0085] The control domain sub-model takes power regulation as the target, and simulates the converter's active control of active / reactive power (such as power frequency regulation during frequency fluctuations) by characterizing the power closed-loop control logic (such as the error regulation equation of the PI controller), ensuring that the control characteristics of a single WTG are incorporated into the aggregation.

[0086] This embodiment extends the overall model based on parallel topology relationships. That is, it realizes the overall quantification of multi-machine coupling characteristics through topology-level association. When extending, it is necessary to use the actual parallel topology of the wind farm as the basis (such as each WTG being connected to a common bus through a collector line and then connected to the power grid). The electromagnetic domain sub-model (current and voltage output) of each WTG is associated with the line impedance model, and the mechanical domain sub-model (speed and torque) is associated with the wind speed distribution to ensure the structural authenticity of the overall model and avoid the distortion of coupling characteristics caused by topology simplification.

[0087] In the overall model, the output current of the electromagnetic domain sub-model of each individual WTG is superimposed at the common bus to form the total injected current of the wind farm; the mechanical domain sub-model of each individual unit is affected by the wind speed in the corresponding area, forming differentiated mechanical power input, which not only preserves the dynamic accuracy of the individual model, but also realizes the overall characterization of the multi-machine coupling characteristics (such as when the wind speed in a certain area increases, the corresponding WTG power increases, which in turn affects the total output of the wind farm).

[0088] By integrating the local characteristics of a single wind turbine model into the global characteristics of the wind farm through topological relationships, the huge computational burden of directly modeling hundreds of wind turbines is avoided.

[0089] According to the above embodiments, in a further specific embodiment, based on a dynamic mathematical model and combining wind speed difference data and parameter difference data of each wind turbine, the contribution of each wind turbine to the grid injection current is quantified into a dynamic weighting coefficient, including:

[0090] Based on the electromagnetic domain dynamic mathematical model in the dynamic mathematical model, the three-phase current is converted to the synchronous rotating dq coordinate system to obtain the active and reactive components.

[0091] Rotate the dq coordinate system to the d'-q' coordinate system so that the q'-axis current component of the total injected current is 0, while retaining the d'-axis current component;

[0092] The proportion of the d'-axis current component of each wind turbine to the total d'-axis current component of the wind farm is used as the dynamic weighting coefficient.

[0093] When the wind speed change exceeds the preset threshold or the control method changes, the dynamic weighting coefficient is recalculated and updated.

[0094] The first step is to convert the three-phase current to the dq coordinate system, which is to convert the AC quantity to DC. Based on the electromagnetic domain sub-model in the dynamic mathematical model, the three-phase current of each WTG is converted to the synchronous rotating dq coordinate system. Essentially, this is to use the Park transformation, which is commonly used in power system analysis, to convert the time-varying three-phase AC current (including the fundamental and harmonic components) into stationary DC quantities (d-axis and q-axis components).

[0095] Among them, the d-axis component corresponds to the active component of the current (directly affecting active power output), and the q-axis component corresponds to the reactive component (mainly affecting voltage support). This transforms the complex dynamic analysis of AC quantities into a simple DC analysis, facilitating subsequent quantification of the active / reactive contributions of each WTG.

[0096] The rotation from the dq coordinate system to the d'-q' coordinate system is to eliminate reactive power interference. Figure 2 An equivalent dq-axis current model and a d'-q'-axis schematic diagram of a wind farm are provided for embodiments of this application, as shown below. Figure 2 As shown, the dq coordinate system is rotated to the d'-q' coordinate system, and the q'-axis component of the total injected current is set to 0. By adjusting the rotation angle, the total injected current of all WTGs in the wind farm falls exactly on the new d' axis, at which point the q'-axis component is naturally 0. The core requirement of the power grid for wind farms is stable active power output; reactive power components affect voltage more than energy transmission. Therefore, after removing the q'-axis component, the remaining d'-axis current component can purely reflect the actual contribution of each WTG to the total active current, avoiding the misjudgment of contribution caused by the mixed calculation of active and reactive components in traditional methods.

[0097] Using the proportion of the d'-axis current component of each WTG to the total d'-axis current component as the dynamic weighting coefficient, the dynamic weighting coefficient of the k-th wind turbine is then: ;

[0098] in, This represents the dynamic weighting coefficient of the k-th wind turbine. Indicates that the k-th wind turbine is in The current on the shaft, where n represents the total number of wind turbines in the wind farm.

[0099] When the wind speed change exceeds the preset threshold or the control method changes, the weights are recalculated. Wind speed is the core factor affecting the output power of WTG. Sudden changes in wind speed (such as gusts) will cause the d' axis current of each WTG to be redistributed. Changes in the control method will change the current output characteristics of the WTG, which will also cause changes in the contribution ratio. This trigger-based update can ensure the accuracy of the weights and avoid unnecessary calculation overhead.

[0100] According to the above embodiments, in a further specific embodiment, the electrical parameters, control parameters, and mechanical parameters of each wind turbine are weighted and aggregated according to dynamic weighting coefficients to construct an equivalent wind turbine model, including:

[0101] Extract the electrical parameters of each wind turbine, including magnetizing inductance and stator resistance;

[0102] Extract the control parameters of each wind turbine, including the proportional and integral coefficients of the PI controller;

[0103] Extract the mechanical parameters of each wind turbine, including the damping coefficient;

[0104] The electrical parameters, control parameters, and mechanical parameters are weighted and summed according to the dynamic weighting coefficients to obtain the equivalent electrical parameters, equivalent control parameters, and equivalent mechanical parameters.

[0105] An equivalent wind turbine model is constructed based on equivalent electrical parameters, equivalent control parameters, and equivalent mechanical parameters.

[0106] This embodiment clearly extracts three core parameters: electrical parameters, control parameters, and mechanical parameters, which directly determine the dynamic response capability of WTG.

[0107] Electrical parameters (magnetizing inductance) Stator resistance The magnetizing inductance is the core determinant of the dynamic characteristics in the electromagnetic domain. The magnetizing inductance directly affects the flux linkage build-up speed and transient current response of the doubly-fed induction motor; the stator resistance affects the copper loss and voltage drop during power transmission.

[0108] Control parameters (PI controller proportional coefficient) Integral coefficient This determines the power regulation accuracy and dynamic response speed of the WTG. The larger the value, the faster the power deviation adjustment speed. The larger the value, the smaller the static power error.

[0109] Mechanical parameters (damping coefficient D) reflect the disturbance rejection capability of a mechanical transmission system. The larger the damping coefficient, the smoother the response of the rotational speed to torque fluctuations.

[0110] Then, the three types of parameters are weighted and summed according to the dynamic weight coefficients, as follows:

[0111] Equivalent electrical parameters include: and ;

[0112] In the formula, Represents the equivalent magnetizing inductance. Let be the per-unit value of the excitation inductance of the k-th wind turbine. This represents the equivalent stator resistance. Let be the per-unit value of the stator resistance of the k-th wind turbine.

[0113] Equivalent control parameters include: and ;

[0114] In the formula, and These are the equivalent proportional coefficient and the equivalent integral coefficient, respectively. and These are the proportional coefficient and integral coefficient of the controller for the k-th wind turbine, respectively.

[0115] Equivalent mechanical parameters include: equivalent damping coefficient ;

[0116] In the formula, Indicates the equivalent damping coefficient; Let be the damping coefficient of the k-th wind turbine.

[0117] The core objective of constructing an equivalent wind turbine model based on equivalent electrical, control, and mechanical parameters is to replace the detailed models of dozens to hundreds of WTGs with a single unit model, thereby reducing the computational burden while ensuring accuracy.

[0118] According to the above embodiments, in a further specific embodiment, based on wind speed distribution characteristic data, an equivalent turbine model is constructed according to the wind energy conversion principle and dynamic weighting coefficients, including:

[0119] Collect real-time wind speed data and corresponding swept area parameters of wind turbines in different areas of the wind farm;

[0120] Based on the principle of wind energy conversion, the equivalent wind speed is obtained by combining dynamic weighting coefficients;

[0121] The optimal power coefficient and optimal tip speed ratio of each wind turbine are weighted and aggregated according to the dynamic weight coefficient to obtain the equivalent optimal power coefficient and equivalent optimal tip speed ratio.

[0122] An equivalent turbine model is constructed by integrating the equivalent wind speed, the equivalent optimal power coefficient, and the equivalent optimal tip speed ratio.

[0123] This embodiment collects real-time wind speed data and corresponding swept area parameters of different areas of the wind farm. The core purpose of this data collection design is to restore the spatial differences in wind energy distribution in the wind farm, rather than using a simplified method of average wind speed across the entire field.

[0124] The core input for wind energy conversion is wind speed, which exhibits significant regional differences. Using average wind speed instead of real-time regional wind speed would mask the differences in wind energy input between different regions, causing the equivalent model to fail to reflect the actual situation where "wind speeds are high in some areas and low in others."

[0125] The swept area determines the upper limit of wind energy capture by a wind turbine generator (at the same wind speed, the larger the swept area, the more wind energy is captured). Therefore, collecting the swept area of ​​the corresponding wind turbine generator is to combine wind speed differences with differences in turbine capture capacity, avoiding the allocation of wind energy weights solely based on wind speed. It should be noted that this embodiment does not limit the specific number of areas to be divided, and can be flexibly adjusted according to the actual scale of the wind farm and the uniformity of wind speed distribution.

[0126] Based on the principle of wind energy conversion and combined with dynamic weighting coefficients, the equivalent wind speed is obtained. The specific calculation formula is as follows: ;

[0127] In the formula, Indicates the equivalent wind speed. This represents the wind speed of the k-th wind turbine. and Let be the swept area of ​​the k-th wind turbine and the equivalent swept area of ​​all wind turbines, respectively. .

[0128] The optimal power coefficient and optimal tip speed ratio of each WTG are weighted and aggregated according to the dynamic weight coefficient, with the aim of converting the wind energy conversion efficiency characteristics of multiple WTGs into the efficiency characteristics of a single turbine.

[0129] The optimal power factor is a core indicator for measuring the wind energy conversion efficiency of a WTG (Wind Power Generation Gear), representing the maximum proportion of wind energy that a WTG can convert into mechanical power. The optimal tip speed ratio is a key operating parameter for WTGs to achieve wind energy conversion.

[0130] Specifically, the optimal power coefficient and optimal tip speed ratio of each wind turbine are weighted and aggregated according to the dynamic weighting coefficient to obtain the equivalent optimal power coefficient and equivalent optimal tip speed ratio, which are calculated using the following formula:

[0131] The equivalent optimal power factor is: ;

[0132] In the formula, This represents the equivalent optimal power coefficient. Let be the optimal mechanical power coefficient of the k-th wind turbine.

[0133] The equivalent optimal tip speed ratio is: ;

[0134] In the formula, This represents the equivalent optimal tip speed ratio. Let be the optimal tip speed ratio of the k-th wind turbine.

[0135] By integrating the equivalent wind speed, the equivalent optimal power coefficient, and the equivalent optimal tip speed ratio, an equivalent turbine model is constructed. From this, the equivalent turbine output mechanical power can be obtained as follows: ;

[0136] In the formula, This represents the equivalent mechanical power output of the turbine. , and These are the equivalent wind energy utilization coefficient and the maximum equivalent wind energy utilization coefficient, respectively. air density, Let be the blade radius of the equivalent turbine, and , Let be the blade radius of the k-th turbine.

[0137] According to the above embodiments, in a further specific embodiment, based on the line impedance distribution difference data and the power transmission mechanism, an equivalent line impedance model is constructed by combining dynamic weighting coefficients, including:

[0138] Obtain the per-unit value of the line impedance from each wind turbine to the common node of the power grid;

[0139] The equivalent line impedance is obtained by weighting and aggregating the per-unit values ​​of each line impedance according to the dynamic weighting coefficient.

[0140] Construct an equivalent line impedance model based on the equivalent line impedance.

[0141] This embodiment explicitly obtains the per-unit value of the line impedance rather than the actual impedance value (such as the ohm value). Its core purpose is to eliminate the differences in impedance magnitude between lines with different capacities (WTG) and different lengths, and to achieve unified quantification and comparison.

[0142] Per-unit values ​​are the ratio of actual values ​​to a reference value, and can convert impedance parameters with different physical dimensions into dimensionless relative values. In wind farms, the line length and conductor cross-section of different WTGs to the common node may vary, and using per-unit values ​​can uniformly measure their actual impact on the power grid.

[0143] Wind farm collection systems typically employ a topology where multiple turbines are connected in parallel to a common node, and then connected to the power grid via a main line. The line impedance of each WTG is the segmented impedance from the turbine's output to the common node. Accurate quantification of the local losses and voltage drops in the transmission of power from each WTG to the common node is crucial.

[0144] The equivalent line impedance is obtained by weighting and aggregating the per-unit impedance values ​​of each line using dynamic weighting coefficients, specifically according to the formula. get;

[0145] In the formula, Indicates the equivalent line impedance. Let be the per-unit value of the line impedance connecting the k-th wind turbine to the power grid.

[0146] The equivalent line impedance model is constructed by replacing the set of line impedances from all WTGs to common nodes in the wind farm with an equivalent impedance. The equivalent line impedance model can accurately reproduce the overall voltage drop and loss characteristics of the lines inside the wind farm, and can also meet the simplified calculation needs of large-scale wind farm and grid interaction analysis.

[0147] According to the above embodiments, in a further specific embodiment, the equivalent wind turbine model, the equivalent turbine model, and the equivalent line impedance model are coupled in multiple domains to form a wind farm aggregation model, including:

[0148] Establish the mechanical torque transmission link between the equivalent turbine model and the equivalent wind turbine model;

[0149] Establish the electrical connection relationship between the equivalent wind turbine model and the equivalent line impedance model;

[0150] Based on the multi-domain coupling logic of the overall dynamic mathematical model of the wind farm, the equivalent wind turbine model, the equivalent turbine model, and the equivalent line impedance model are integrated to form a wind farm aggregate model.

[0151] This specific embodiment refines the multi-domain coupling process of the wind farm aggregation model through a three-step process of mechanical domain coupling, electrical domain coupling, and multi-domain logical integration.

[0152] The purpose of establishing a mechanical torque transmission link between the equivalent turbine model and the equivalent wind turbine model is to accurately transfer the mechanical energy converted from wind energy to the generator. The mechanical torque output by the equivalent turbine model (calculated from parameters such as equivalent wind speed and equivalent power coefficient, reflecting the overall mechanical power captured by the wind farm) needs to be used as the input mechanical torque of the equivalent wind turbine model and converted into electrical energy through the electromagnetic induction of the generator.

[0153] The purpose of establishing the electrical connection between the equivalent wind turbine model and the equivalent line impedance model is to transmit the electrical energy output by the generator to the power grid through the line impedance, simulating the losses and voltage drops during the energy transmission process. The terminal voltage and current output by the equivalent wind turbine model need to be connected to the equivalent line impedance model. By simulating the impact of the collector line on the electrical energy through impedance, the actual output of the wind farm to the power grid is ultimately reflected in the form of common node voltage and current. The parameters of the equivalent line impedance model (equivalent resistance, reactance) directly affect the output characteristics of the generator.

[0154] Based on the multi-domain coupling logic of the overall dynamic mathematical model of the wind farm, the integration of three models into a wind farm aggregate model uses unified physical laws to constrain the interaction of each model, ensuring that local coupling conforms to global characteristics. The overall dynamic mathematical model of the wind farm already includes cross-domain correlation laws (such as torque balance, power conservation, and control response) of the aerodynamic, mechanical, electromagnetic, and control domains. Based on this, the three models are integrated, forming a closed loop within the framework of the global mathematical model. Real-time interaction of state variables (such as speed, current, and voltage) is achieved through global coupling logic, ensuring that the aggregate model can reflect both local details (such as generator transient response) and global characteristics (such as the power output curve of the entire wind farm).

[0155] In the above embodiments, a wind farm model aggregation method for doubly-fed induction generator (DFIG) wind turbines has been described in detail. This application also provides embodiments corresponding to a wind farm model aggregation device for DFIG wind turbines. It should be noted that this application describes the embodiments of the device from two perspectives: one based on functional modules and the other based on hardware.

[0156] From the perspective of functional modules Figure 3 A structural diagram of a wind farm model aggregation device for doubly-fed wind turbines provided in this application embodiment is shown below. Figure 3 As shown, a wind farm model aggregation device for doubly-fed induction generator (DFIG) wind turbines includes:

[0157] The mathematical model construction module 21 is used to obtain a dynamic mathematical model of a single wind turbine and a wind farm based on the structure of the wind farm containing a doubly fed induction motor and a constant speed induction motor, and the dynamic characteristics of the wind farm and the wind turbine.

[0158] The weighting conversion module 22 is used to quantify the contribution of each wind turbine to the grid injection current into a dynamic weighting coefficient based on the dynamic mathematical model, combined with wind speed difference data and parameter difference data of each wind turbine; wherein, the dynamic weighting coefficient is dynamically updated according to the operating conditions.

[0159] The first aggregation module 23 is used to perform weighted aggregation of the electrical parameters, control parameters and mechanical parameters of each wind turbine according to the dynamic weight coefficient, and to construct an equivalent wind turbine model.

[0160] The second aggregation module 24 is used to construct an equivalent turbine model based on wind speed distribution characteristic data, wind energy conversion principle and dynamic weighting coefficients.

[0161] The third aggregation module 25 is used to construct an equivalent line impedance model based on line impedance distribution difference data and power transmission mechanism, combined with dynamic weighting coefficients.

[0162] The coupling module 26 is used to couple the equivalent wind turbine model, the equivalent turbine model, and the equivalent line impedance model into a wind farm aggregate model through multi-domain coupling.

[0163] Since the embodiments of the apparatus and the embodiments of the method correspond to each other, please refer to the description of the embodiments of the method for the embodiments of the apparatus, which will not be repeated here.

[0164] Figure 4 A structural diagram of another wind farm model aggregation device for doubly-fed wind turbines provided in this application embodiment is shown below. Figure 4 As shown, the wind farm model aggregation device for doubly fed wind turbines includes: a memory 30 for storing computer programs;

[0165] The processor 31 is used to execute a computer program to implement the steps of the method for obtaining user operation habit information as described in the above embodiment (wind farm model aggregation method for doubly fed wind turbines).

[0166] The wind farm model aggregation device for doubly fed wind turbines provided in this embodiment may include, but is not limited to, mobile terminals, personal computers, workstations, etc.

[0167] The processor 31 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 31 may be implemented using at least one of the following hardware forms: Digital Signal Processor (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 31 may also include a main processor and a coprocessor. The main processor, also known as the Central Processing Unit (CPU), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 31 may integrate a Graphics Processing Unit (GPU), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor 31 may also include an Artificial Intelligence (AI) processor, which handles computational operations related to machine learning.

[0168] The memory 30 may include one or more computer-readable storage media, which may be non-transitory. The memory 30 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In this embodiment, the memory 30 is used to store at least the following computer program 301, which, after being loaded and executed by the processor 31, is capable of implementing the relevant steps of the wind farm model aggregation method for doubly-fed induction generator (DFIG) wind turbines disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 30 may also include an operating system 302 and data 303, and the storage method may be temporary or permanent storage. The operating system 302 may include Windows, Unix, Linux, etc. The data 303 may include, but is not limited to, the data involved in implementing the wind farm model aggregation method for DFIG wind turbines.

[0169] In some embodiments, the wind farm model aggregation device for doubly fed wind turbines may further include a display screen 32, an input / output interface 33, a communication interface 34, a power supply 35, and a communication bus 36.

[0170] Those skilled in the art will understand that Figure 4 The structure shown does not constitute a limitation on the wind farm model aggregation device for doubly fed wind turbines and may include more or fewer components than shown.

[0171] The wind farm model aggregation device for doubly-fed induction generator (DFIG) wind turbines provided in this application includes a memory and a processor. When the processor executes the program stored in the memory, it can implement the following method: wind farm model aggregation method for DFIG wind turbines.

[0172] Finally, this application also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps described in the above embodiment of the wind farm model aggregation method for doubly-fed induction generator (DFIG) wind turbines.

[0173] It is understood that if the methods in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and executes all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0174] The computer-readable storage medium provided in this embodiment stores a computer program. When the processor executes the program, it can implement the following method: a wind farm model aggregation method for doubly-fed induction generator (DFIG) wind turbines.

[0175] The foregoing has provided a detailed description of the wind farm model aggregation method, apparatus, and medium for doubly-fed induction generator (DFIG) wind turbines. The various embodiments in the specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

[0176] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for wind farm model aggregation for doubly-fed induction generator (DFIG) wind turbines, characterized in that, include: Based on the structure of a wind farm containing doubly fed induction motors and constant-speed induction motors, and the dynamic characteristics of the wind farm and wind turbine generators, a dynamic mathematical model of a single wind turbine generator and a wind farm is obtained. Based on the dynamic mathematical model, combined with wind speed difference data and parameter difference data of each wind turbine, the contribution of each wind turbine to the grid injection current is quantified into a dynamic weighting coefficient; wherein, the dynamic weighting coefficient is dynamically updated according to the operating conditions. Based on the dynamic weighting coefficients, the electrical, control, and mechanical parameters of each wind turbine are weighted and aggregated to construct an equivalent wind turbine model; Based on wind speed distribution characteristics data, an equivalent turbine model is constructed according to the wind energy conversion principle and the aforementioned dynamic weighting coefficients. Based on the line impedance distribution difference data and the power transmission mechanism, an equivalent line impedance model is constructed by combining the dynamic weighting coefficients. The equivalent wind turbine model, the equivalent turbine model, and the equivalent line impedance model are coupled in multiple domains to form a wind farm aggregation model.

2. The wind farm model aggregation method for doubly-fed induction generator (DFIG) wind turbines according to claim 1, characterized in that, Based on the wind farm structure containing doubly-fed induction motors and constant-speed induction motors, and the dynamic characteristics of the wind farm and wind turbines, dynamic mathematical models of a single wind turbine and the wind farm are obtained, including: Dynamic mathematical models of a single wind turbine generator are established in the aerodynamic, mechanical, electromagnetic, and control domains. The aerodynamic dynamic mathematical model is used to describe the conversion relationship between wind energy and mechanical power, the mechanical dynamic mathematical model is used to describe the speed and torque transmission characteristics, the electromagnetic dynamic mathematical model is used to describe the dynamic correlation of current, voltage, and magnetic flux, and the control dynamic mathematical model is used to characterize the power closed-loop control logic. Based on the parallel topology of multiple wind turbines in a wind farm, the dynamic mathematical models of individual wind turbines are extended into a dynamic mathematical model of the entire wind farm.

3. The wind farm model aggregation method for doubly-fed induction generator (DFIG) wind turbines according to claim 1, characterized in that, Based on the aforementioned dynamic mathematical model, and combining wind speed difference data and parameter difference data of each wind turbine, the contribution of each wind turbine to the grid injection current is quantified into a dynamic weighting coefficient, including: Based on the electromagnetic domain dynamic mathematical model in the dynamic mathematical model, the three-phase current is converted to the synchronous rotating dq coordinate system to obtain the active and reactive components. Rotate the dq coordinate system to the d'-q' coordinate system so that the q'-axis current component of the total injected current is 0, while retaining the d'-axis current component; The proportion of the d'-axis current component of each wind turbine to the total d'-axis current component of the wind farm is used as the dynamic weighting coefficient. When the wind speed change exceeds the preset threshold or the control method changes, the dynamic weighting coefficient is recalculated and updated.

4. The wind farm model aggregation method for doubly-fed induction generator (DFIG) wind turbines according to claim 3, characterized in that, Based on the aforementioned dynamic weighting coefficients, the electrical, control, and mechanical parameters of each wind turbine are weighted and aggregated to construct an equivalent wind turbine model, including: Extract the electrical parameters of each wind turbine, including magnetizing inductance and stator resistance; Extract the control parameters of each wind turbine, including the proportional and integral coefficients of the PI controller; Extract the mechanical parameters of each wind turbine, including the damping coefficient; The electrical parameters, control parameters, and mechanical parameters are weighted and summed according to the dynamic weighting coefficients to obtain the equivalent electrical parameters, equivalent control parameters, and equivalent mechanical parameters. An equivalent wind turbine model is constructed based on the equivalent electrical parameters, the equivalent control parameters, and the equivalent mechanical parameters.

5. The wind farm model aggregation method for doubly-fed induction generator (DFIG) wind turbines according to claim 4, characterized in that, Based on wind speed distribution data, an equivalent turbine model is constructed according to the wind energy conversion principle and the aforementioned dynamic weighting coefficients, including: Collect real-time wind speed data and corresponding swept area parameters of wind turbines in different areas of the wind farm; Based on the principle of wind energy conversion, the equivalent wind speed is obtained by combining the dynamic weighting coefficient. The optimal power coefficient and optimal tip speed ratio of each wind turbine are weighted and aggregated according to the dynamic weighting coefficient to obtain the equivalent optimal power coefficient and equivalent optimal tip speed ratio. An equivalent turbine model is constructed by integrating the equivalent wind speed, the equivalent optimal power coefficient, and the equivalent optimal tip speed ratio.

6. The wind farm model aggregation method for doubly-fed induction generator (DFIG) wind turbines according to claim 5, characterized in that, Based on the line impedance distribution difference data and the power transmission mechanism, an equivalent line impedance model is constructed using the aforementioned dynamic weighting coefficients, including: Obtain the per-unit value of the line impedance from each wind turbine to the common node of the power grid; The equivalent line impedance is obtained by weighting and aggregating the per-unit values ​​of each line impedance according to the dynamic weighting coefficient. Construct an equivalent line impedance model based on the equivalent line impedance.

7. The wind farm model aggregation method for doubly-fed induction generator (DFIG) wind turbines according to claim 2, characterized in that, The equivalent wind turbine model, the equivalent turbine model, and the equivalent line impedance model are coupled across multiple domains to form a wind farm aggregation model, including: Establish the mechanical torque transmission link between the equivalent turbine model and the equivalent wind turbine model; Establish the electrical connection relationship between the equivalent wind turbine model and the equivalent line impedance model; Based on the multi-domain coupling logic of the overall dynamic mathematical model of the wind farm, the equivalent wind turbine model, the equivalent turbine model, and the equivalent line impedance model are integrated to form a wind farm aggregate model.

8. A wind farm model aggregation device for doubly-fed induction generator (DFIG) wind turbines, characterized in that, include: The mathematical model building module is used to obtain dynamic mathematical models of a single wind turbine and a wind farm based on the structure of a wind farm containing doubly fed induction motors and constant speed induction motors, and the dynamic characteristics of the wind farm and wind turbine. The weighting conversion module is used to quantify the contribution of each wind turbine to the grid injection current into a dynamic weighting coefficient based on the dynamic mathematical model, combined with wind speed difference data and parameter difference data of each wind turbine; wherein the dynamic weighting coefficient is dynamically updated according to the operating conditions. The first aggregation module is used to perform weighted aggregation of the electrical parameters, control parameters and mechanical parameters of each wind turbine according to the dynamic weighting coefficient, and to construct an equivalent wind turbine model. The second aggregation module is used to construct an equivalent turbine model based on wind speed distribution characteristic data, wind energy conversion principle and the dynamic weighting coefficients. The third aggregation module is used to construct an equivalent line impedance model based on the line impedance distribution difference data and the power transmission mechanism, combined with the dynamic weighting coefficients. The coupling module is used to couple the equivalent wind turbine model, the equivalent turbine model, and the equivalent line impedance model into a wind farm aggregate model through multi-domain coupling.

9. A wind farm model aggregation device for doubly-fed induction generator (DFIG) wind turbines, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the wind farm model aggregation method for doubly fed wind turbines as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the wind farm model aggregation method for doubly-fed wind turbines as described in any one of claims 1 to 7.

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