Wind field aggregation model construction method based on dominant mode characteristic clustering

Abstract

The invention discloses a wind field aggregation model construction method based on dominant mode characteristic clustering, and belongs to the technical field of wind power generation, and the method comprises the steps: S1, constructing a dynamic model of a wind power grid-connected system based on a state variable of the wind power grid-connected system; s2, acquiring dominant modal information of each fan in the wind power integration system under different working conditions and different control parameters based on wind power plant parameters and the dynamic model; s3, according to the dominant modal information, acquiring dominant data sets of each fan under different working conditions and different control parameters; and S4, grouping each fan based on the dominant data group and a clustering algorithm to obtain an optimal grouping scheme, performing equivalent processing on the fans in the same group in the optimal grouping scheme to obtain an equivalent processing result, and constructing a wind field aggregation model based on the equivalent processing result. The problem that when equivalent processing is conducted on the draught fan, the difference of the draught fan under different working conditions and different control parameters is not considered, and consequently the model precision is poor is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to a method for constructing a wind field aggregation model based on dominant modal characteristic grouping. Background Art

[0002] With the development of renewable energy generation, wind power generation provides a large amount of clean energy while also bringing many challenges to traditional power grids. Wind farms usually contain a large number of wind turbines, which leads to a large increase in the number of network nodes and complexity when performing detailed modeling of the entire wind farm. The existing technology mainly uses the single-machine equivalent method to perform equivalent processing on the generator set, thereby reducing the complexity of wind turbine modeling. For example, a method and system for establishing a spatiotemporal distribution model of power grid frequency response with patent number CN117955125A includes: establishing a single-machine equivalent SFR model containing a renewable energy site and shared energy storage; calculating the shortest electrical distance and the corresponding path between any two nodes in the regional power grid; calculating the inertia time constant on each line based on the shortest electrical distance and the corresponding path; normalizing the shortest electrical distance and the inertia time constant between the load disturbance node and each test node; calculating the spatiotemporal distribution factors of key parameters; introducing the spatiotemporal distribution factors into the single-machine equivalent SFR model to establish a spatiotemporal distribution model of regional power grid frequency response. The above scheme uses single-machine equivalence to equate multiple units to one unit based on considerations of new energy stations and shared energy storage, thereby achieving common access to new energy stations and shared energy storage. However, in the process of equating multiple units, the above scheme does not take into account the differences between wind turbines under different operating conditions and different control parameters, nor does it perform targeted grouping and equating of wind turbines under different operating conditions and different control parameters. Therefore, the model accuracy obtained by the above scheme is poor. Summary of the invention

[0003] In view of the problem that the prior art performs equivalent processing on wind turbines without considering the differences between wind turbines under different operating conditions and different control parameters, resulting in poor model accuracy, the present invention provides a method for constructing a wind farm aggregation model based on dominant modal characteristic clustering, which realizes the simulation of wind turbine behaviors under different operating conditions and control parameters by combining an accurate dynamic model constructed according to the state variables of the system with wind farm parameters, while further obtaining dominant modal information that has the greatest impact on the dynamic response and stability of the system under different operating conditions and different control parameters, and groups and performs equivalent processing on wind turbines through dominant data groups corresponding to dominant modal information under different operating conditions and control parameters, thereby achieving targeted processing of wind turbines under different operating conditions and different control parameters, and solving the problem that the differences between wind turbines under different operating conditions and different control parameters are not considered when performing equivalent processing on wind turbines, resulting in poor model accuracy.

[0004] In order to solve the above technical problems, the present invention provides a method for constructing a wind field aggregation model based on dominant modal characteristic grouping, comprising the following steps: S1: Construct a dynamic model of the wind power grid-connected system based on the state variables of the wind power grid-connected system; S2: obtaining dominant modal information of each wind turbine in the wind power grid-connected system under different operating conditions and different control parameters based on the wind farm parameters and the dynamic model; S3: acquiring a dominant data group of each wind turbine under different operating conditions and different control parameters according to the subsynchronous oscillation frequency in the state variable and the dominant modal information; S4: Grouping each wind turbine based on the dominant data group and the clustering algorithm to obtain an optimal grouping scheme, performing equivalent processing on the wind turbines in the same group in the optimal grouping scheme to obtain equivalent processing results, and constructing a wind farm aggregation model based on the equivalent processing results.

[0005] After adopting the above technical solution, the present invention has the following advantages: Considering that the behavior of wind turbines is affected by the mutual influence between the components in the wind power grid-connected system, the mutual influence between the internal components of the wind turbine and the external environment, and the state variables of power generation grid-connected basically cover the parameter changes and environmental changes caused by the mutual influence of the above components, the dynamic model constructed by the state variables of the power generation grid-connected system can simulate the behavior of wind turbines under different working conditions and control parameters. By simulating the behavior of wind turbines under different working conditions and control parameters, the quantification of wind turbine behavior is achieved, thereby improving the accuracy of the constructed wind farm aggregation model; By combining the precise dynamic model constructed according to the state variables of the system with the wind farm parameters, the dominant modal information with the greatest impact on the dynamic response and stability of the system under different operating conditions and different control parameters is obtained, and the wind turbines are grouped and equivalently processed through the dominant data groups corresponding to the dominant modal information under different operating conditions and control parameters, thereby achieving targeted processing of wind turbines under different operating conditions and different control parameters, and further significantly improving the accuracy of the constructed wind farm aggregation model; and solving the problem of poor model accuracy caused by not considering the differences of wind turbines under different operating conditions and different control parameters when performing equivalent processing on wind turbines.

[0006] Preferably, the S1 comprises: S11: constructing a sub-dynamic model of each component based on the state variables and the characteristics of each component in the wind power grid-connected system; S12: constructing the dynamic model based on the sub-dynamic model and the input variables of the wind power grid-connected system.

[0007] Taking into account that the wind power grid-connected system consists of multiple parts and the operating characteristics and laws of each component are different, a sub-dynamic model of each component is constructed through state variables, characteristics of each component and input variables. While fully considering the operating characteristics and laws of each component, it also fully considers the impact of changes in different components on the wind power grid-connected system, thereby improving the accuracy, reliability and adaptability of the constructed dynamic model.

[0008] Preferably, in S12, the expression of the dynamic model is X=AY+BU, wherein X represents the dynamic model, A represents the state matrix, Y represents the state variable, B represents the input matrix, and U represents the input variable.

[0009] Preferably, S2 includes: S21: inputting wind farm parameters corresponding to each wind turbine under different operating conditions and different control parameters into a dynamic model, thereby obtaining a state matrix of each wind turbine under different operating conditions and different control parameters; S22: obtaining modal information of each wind turbine under different operating conditions and different control parameters based on the eigenvalues ​​of the state matrix, and obtaining dominant modal information of each wind turbine under different operating conditions and different control parameters based on the modal information.

[0010] Preferably, in S22, obtaining the dominant modal information of each wind turbine under different operating conditions and different control parameters based on the modal information includes: The absolute values ​​of the real parts of the modal information of each fan under different operating conditions and different control parameters are compared, and the modal information with the smallest absolute value of the real part of the modal is taken as the dominant modal information of each fan under different operating conditions and different control parameters.

[0011] The absolute value of the real part of the mode reflects the decay rate of the mode. The slower the decay rate, the greater the impact on the dynamic response and stability of the system. By taking the mode with the slowest decay rate as the dominant mode, the fans under different operating conditions and different control parameters can be robustly grouped and treated as equal. Even under the most unstable conditions, the fans can be grouped and treated as equal, thereby improving the applicability and accuracy of the model.

[0012] Preferably, S3 includes: S31: constructing a dominant function according to the decay speed in the state variable and the subsynchronous oscillation frequency; S32: selecting a dominant data group of each wind turbine under different operating conditions and different control parameters from the dominant modal information using a dominant function and a time range preset based on the subsynchronous oscillation frequency.

[0013] Preferably, the S31 includes: The expression of the dominant function is y(t) = |e at cosβt|, where y(t) represents the dominant function value at time t, e represents the base of the natural logarithm, a is the decay rate, and β is the subsynchronous oscillation frequency.

[0014] Preferably, S4 includes: S41: randomly selecting a number of clustering dominant data groups from the dominant data groups; S42: Mark the dominant data groups other than the cluster dominant data group in the dominant data group as process dominant data groups, calculate the Euclidean distance between the process dominant data group and the cluster dominant data group, allocate the process dominant data group based on the Euclidean distance to obtain an allocation result, update the cluster dominant data group based on the allocation result and obtain the update number, determine whether the update number is greater than the preset number, if so, execute S43, if less than or equal to, execute S42; S43: Obtain an optimal grouping solution based on the updated cluster-dominant data group.

[0015] By dividing the dominant data groups through multiple iterations, the allocation accuracy of the dominant data groups corresponding to each fan under different operating conditions and different control parameters is improved, thereby improving the allocation accuracy of each fan under different operating conditions and different control parameters.

[0016] Preferably, the S43 includes: S431: obtaining a grouping scheme based on the updated cluster-dominant data group, and obtaining a silhouette coefficient of each data point in the same group based on an average distance between data points in the same group in the grouping scheme; S432: Obtain an optimal grouping solution based on the silhouette coefficient.

[0017] The contour coefficient reflects the fitness of each data point in the group. By obtaining the contour coefficient, the incompatible data points in the group can be reallocated, that is, the wind turbines can be regrouped, which further improves the allocation accuracy of the wind turbines, thereby ensuring the accuracy of the equivalent results and further improving the accuracy and reliability of the constructed wind farm aggregation model.

[0018] Preferably, in S431, the expression for obtaining the silhouette coefficient of each data point in the same group based on the average distance between data points in the same group in the grouping scheme is: Wherein, s(i) represents the silhouette coefficient of the ith data point in the same group, a(i) represents the average distance between the ith data point in the same group and all other data points in the same group except the ith data point, and b(i) represents the average distance between the ith data point in the same group and all data points in the adjacent groups obtained from the grouping scheme based on the Euclidean distance.

[0019] Beneficial effects of this program: Considering that the wind power grid-connected system is composed of multiple parts, and the operating characteristics and laws of each component are different, the sub-dynamic model of each component is constructed through the state variables, the characteristics of each component and the input variables. While fully considering the operating characteristics and laws of each component, the impact of the changes of different components on the wind power grid-connected system is also fully considered, which improves the accuracy, reliability and adaptability of the constructed dynamic model; The dynamic model constructed by the state variables of the power generation grid-connected system can simulate the behavior of wind turbines under different operating conditions and control parameters. By simulating the behavior of wind turbines under different operating conditions and control parameters, the behavior of wind turbines can be quantified, thereby improving the accuracy of the constructed wind farm aggregation model. By combining the precise dynamic model constructed according to the state variables of the system with the wind farm parameters, the dominant modal information with the greatest impact on the dynamic response and stability of the system under different operating conditions and different control parameters is obtained, and the wind turbines are grouped and equivalently processed through the dominant data groups corresponding to the dominant modal information under different operating conditions and control parameters, thereby achieving targeted processing of wind turbines under different operating conditions and different control parameters, and further significantly improving the accuracy of the constructed wind farm aggregation model; and solving the problem of poor model accuracy caused by not considering the differences of wind turbines under different operating conditions and different control parameters when performing equivalent processing on wind turbines. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Other features, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the drawings to represent the same parts.

[0021] Figure 1 It is a flow chart of the method for constructing a wind field aggregation model based on dominant modal characteristic grouping of the present invention; Figure 2 It is a structural diagram of a permanent magnet direct-drive wind turbine wind power grid-connected system in a wind field aggregation model construction method based on dominant modal characteristic clustering of the present invention; Figure 3 A model diagram of the shaft system in the wind field aggregation model construction method based on dominant modal characteristic grouping of the present invention; Figure 4 It is an equivalent circuit of the dq coordinate system of the synchronous generator in the wind farm aggregation model construction method based on the dominant modal characteristic grouping of the present invention; Figure 5 It is a model diagram of the machine-side converter in the wind farm aggregation model construction method based on dominant modal characteristic grouping of the present invention; Figure 6This is a model diagram of the grid-side converter in the wind farm aggregation model construction method based on dominant modal characteristic grouping of the present invention. DETAILED DESCRIPTION

[0022] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0023] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the drawings; the process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0024] Embodiment 1: like Figure 1 As shown in FIG. 1 , the method for constructing a wind field aggregation model based on dominant modal characteristic clustering includes the following steps: S1: Construct a dynamic model of the wind power grid-connected system based on the state variables of the wind power grid-connected system.

[0025] The S1 includes: S11: constructing a sub-dynamic model of each component based on the state variables and the characteristics of each component in the wind power grid-connected system; S12: constructing the dynamic model based on the sub-dynamic model and the input variables of the wind power grid-connected system.

[0026] In S12, the expression of the dynamic model is X=AY+BU, where X represents the dynamic model, A represents the state matrix, Y represents the state variable, B represents the input matrix, and U represents the input variable.

[0027] In this embodiment, the sub-dynamic model includes a shaft system model, a synchronous generator model, a machine-side converter (MSC) model, a grid-side converter (GSC) model, a phase-locked loop model, a filter inductor model, a collector line model and a grid-connected line model. The structure diagram of the wind power grid-connected system, i.e., the permanent magnet direct-drive wind turbine wind power grid-connected system, is as shown in FIG. Figure 2 As shown, the state variables of the wind power grid-connected system are shown in Table 1: Table 1 State variables of permanent magnet direct-drive wind turbine wind power grid-connected system and the system to which it belongs like Figure 3 As shown in the figure, the shaft system of the wind turbine connects the wind wheel and the generator rotor, and the wind energy captured by the wind wheel acts on the rotor of the generator. The dynamic equation of the shaft system model is: Where ω s is the fan speed, i.e. the state variable of the shaft system dynamic model, J is the total moment of inertia of the fan, F is the rotational viscosity coefficient, T m is the mechanical torque of the wind turbine, Te is the electromagnetic torque of the wind turbine, ρ is the air density, R is the blade radius of the wind turbine, C p is the wind energy utilization coefficient, v is the wind speed in the area. p is the number of permanent magnet motor poles, ψ is the magnetic flux, i qs is the q-axis component of the stator current, and the state equation is linearized to obtain:

[0028] When modeling the synchronous generator, the following optimizations are performed: the internal magnetic field is assumed to be sinusoidally distributed; hysteresis loss and eddy current loss due to cogging are ignored; the terminal voltage is assumed to be three-phase symmetrical; a hidden-pole motor is used, and the dq axis inductance is the same. The equivalent circuit of the synchronous generator in the dq coordinate system is as follows: Figure 4 As shown, the state equation of the synchronous generator is: The hidden pole motor is used, and the dq axis inductance is the same, both L s , where i ds 、i qs is the state variable of the synchronous generator model, which represents the dq axis component of the stator current, L s , R s are the stator inductance and resistance respectively, u ds 、u qs It represents the dq-axis component of the stator voltage. The state equation is linearized to obtain:

[0029] The q-axis of the machine-side converter controls the output power of the fan by controlling the fan speed. The d-axis control current is 0 for two purposes. The first is to enable the q-axis current to independently control the speed, and the second is to minimize the loss of the fan system. Figure 5 As shown, the state equation of the machine-side converter is: k p1 , k i1 are the proportional and integral parameters of the inner loop control of the d-axis current, k p2 , k i2 are the proportional and integral parameters of the q-axis voltage outer loop control, k p3 , k i3are the proportional and integral parameters of the inner loop control of the q-axis current respectively. The state equation is linearized to obtain:

[0030] like Figure 6 As shown in the figure, the grid-side converter adopts grid-oriented vector control as a whole. When the phase-locked loop inputs the voltage vector phase of the grid into the control system for coordinate transformation, the d-axis of the dq coordinate system is synchronized with the grid vector phase. In this way, the dq axes can control the active and reactive power output by the inverter respectively. The state equation of the grid-side converter model is: Linearizing the state equation yields:

[0031] The state equation of the DC side model uses the power balance equation as the principle, that is, the capacitor current multiplied by the capacitor voltage is equal to the input power minus the output power. The dynamic process of the machine-side converter and the grid-side converter is linked together. The state equation of the DC side model is: Where udt and uqt are the dq-axis components of the grid-side converter output voltage, respectively. dg 、i qg are the dq axis currents at the outlet of the grid-side flowmeter respectively; C is the DC side capacitance, u DC is the state variable of the DC side dynamic model, and the equation is linearized to obtain:

[0032] The phase-locked loop is responsible for extracting the electrical angle of the grid voltage. The dynamic equation of the phase-locked loop model is as follows: In the grid-side converter model of direct-drive wind turbines, the node voltage u g Set the dq rotation coordinate system as the reference, u g The q-axis component of is the input of the PLL. pp , k ii are the proportional integral coefficients in the PLL controller of the direct-drive wind turbine. g is the grid angular velocity. a 、x b is the intermediate variable, i.e. the state variable of the phase-locked loop. After considering the change of the coordinate axis, it is linearized to obtain:

[0033] The dynamic equation of the filter inductor is: Linearization gives:

[0034] The collector circuit adopts RLC circuit, and its dynamic equation is: In the formula, u dg 、u qg、i d1 、i q1 is the state variable of the dynamic model of the collector line. dt 、u qt and i dg 、i qg It is divided into dq axis voltage and current at the grid-side converter outlet, u dg 、u qg are the dq axis components of the voltage at the outlet of the wind turbine, i d1 、i q1 are the dq axis current of the wind turbine collector line, e d 、e q are the dq axis components of the voltage at the wind farm busbar respectively. k1 is the transformer ratio at the wind turbine outlet, and the linearization result is:

[0035] The state equation of the grid-connected line is also expressed by RLC circuit, and its dynamic equation is: Where, k2 is the transformation ratio of the grid-connected transformer, u sd 、u sq is the dq axis component of the grid voltage, i dss 、i qss is the dq axis current of the power grid. The state equation is linearized to:

[0036] In the modeling of the above-mentioned components, the operating characteristics and laws of each component are fully considered, and the impact of changes in different components on the wind power grid-connected system is also fully considered, thereby improving the accuracy, reliability and adaptability of the constructed dynamic model. The dynamic model constructed through the state variables of the power generation grid-connected system can simulate the behavior of wind turbines under different operating conditions and control parameters. By simulating the behavior of wind turbines under different operating conditions and control parameters, the quantification of wind turbine behavior is achieved, thereby improving the accuracy of the constructed wind farm aggregation model.

[0037] S2: Based on the wind farm parameters and the dynamic model, the dominant modal information of each wind turbine in the wind power grid-connected system under different operating conditions and different control parameters is obtained.

[0038] The S2 includes: S21: inputting wind farm parameters corresponding to each wind turbine under different operating conditions and different control parameters into a dynamic model, thereby obtaining a state matrix of each wind turbine under different operating conditions and different control parameters; S22: obtaining modal information of each wind turbine under different operating conditions and different control parameters based on the eigenvalues ​​of the state matrix, and obtaining dominant modal information of each wind turbine under different operating conditions and different control parameters based on the modal information.

[0039] In S22, the obtaining of the dominant modal information of each wind turbine under different operating conditions and different control parameters based on the modal information includes: The absolute values ​​of the real parts of the modal information of each fan under different operating conditions and different control parameters are compared, and the modal information with the smallest absolute value of the real part of the modal is taken as the dominant modal information of each fan under different operating conditions and different control parameters.

[0040] In this embodiment, the wind farm parameters include rotor parameters, generator parameters, wind farm environment parameters, etc., the state matrix is ​​a 22*22-order matrix, and the input matrix is ​​a 22*6-order matrix. The precise dynamic model constructed according to the state variables of the system is combined with the wind farm parameters to obtain the dominant modal information that has the greatest impact on the dynamic response and stability of the system under different operating conditions and different control parameters. This facilitates the subsequent grouping and equivalent processing of wind turbines according to the dominant data groups corresponding to the dominant modal information under different operating conditions and control parameters, thereby achieving targeted processing of wind turbines under different operating conditions and different control parameters, and further significantly improving the accuracy of the constructed wind farm aggregation model.

[0041] S3: Obtaining a dominant data group of each wind turbine under different operating conditions and different control parameters according to the subsynchronous oscillation frequency in the state variable and the dominant modal information.

[0042] The S3 includes: S31: constructing a dominant function according to the decay speed in the state variable and the subsynchronous oscillation frequency; S32: selecting a dominant data group of each wind turbine under different operating conditions and different control parameters from the dominant modal information using a dominant function and a time range preset based on the subsynchronous oscillation frequency.

[0043] The S31 includes: The expression of the dominant function is y(t) = |e at cosβt|, where y(t) represents the dominant function value at time t, e represents the base of the natural logarithm, a is the decay rate, and β is the subsynchronous oscillation frequency.

[0044] In this embodiment, the sub-synchronous oscillation frequency range is 10-50Hz, and the preset time range is 0-0.3. A certain number of points are selected at equal intervals within the preset time range as the dominant data group of each wind turbine under different operating conditions and different control parameters. By obtaining the dominant data group corresponding to the dominant modal information under different operating conditions and control parameters, it is convenient to group and perform equivalent processing of the wind turbines according to the dominant data groups corresponding to the wind turbines, thereby achieving targeted processing of wind turbines under different operating conditions and different control parameters, and further significantly improving the accuracy of the constructed wind field aggregation model.

[0045] S4: Grouping each wind turbine based on the dominant data group and the clustering algorithm to obtain an optimal grouping scheme, performing equivalent processing on the wind turbines in the same group in the optimal grouping scheme to obtain equivalent processing results, and constructing a wind farm aggregation model based on the equivalent processing results.

[0046] The S4 includes: S41: randomly selecting a number of clustering dominant data groups from the dominant data groups; S42: Mark the dominant data groups other than the cluster dominant data group in the dominant data group as process dominant data groups, calculate the Euclidean distance between the process dominant data group and the cluster dominant data group, allocate the process dominant data group based on the Euclidean distance to obtain an allocation result, update the cluster dominant data group based on the allocation result and obtain the update number, determine whether the update number is greater than the preset number, if so, execute S43, if less than or equal to, execute S42; S43: Obtain an optimal grouping solution based on the updated cluster-dominant data group.

[0047] The S43 includes: S431: obtaining a grouping scheme based on the updated cluster-dominant data group, and obtaining a silhouette coefficient of each data point in the same group based on an average distance between data points in the same group in the grouping scheme; S432: Obtain an optimal grouping solution based on the silhouette coefficient.

[0048] In S431, the expression for obtaining the silhouette coefficient of each data point in the same group based on the average distance between data points in the same group in the grouping scheme is: Wherein, s(i) represents the silhouette coefficient of the ith data point in the same group, a(i) represents the average distance between the ith data point in the same group and all other data points in the same group except the ith data point, and b(i) represents the average distance between the ith data point in the same group and all data points in the adjacent groups obtained from the grouping scheme based on the Euclidean distance.

[0049] In this embodiment, before determining whether the number of updates is greater than a preset number of times, it also includes calculating whether the change of all cluster-dominant data groups is less than a preset threshold. If it is less, it is determined whether the number of updates is greater than the preset number. If it is greater than or equal to, continue to execute S41. By setting the preset threshold, it can be ensured that only when the change of the cluster-dominant data group is small enough, the clustering is considered to have stabilized, thereby avoiding premature termination of iterations due to minor fluctuations, affecting the accuracy of clustering, and thereby improving the accuracy of clustering.

[0050] The optimal grouping scheme based on the contour coefficient is obtained as follows: if the contour coefficient is close to 1, it means that the data point is in a good position in the current cluster and the clustering effect is good. If the contour coefficient is close to 0, it means that the data point may be incorrectly assigned to the cluster. When all the data points of the group are close to 1, it means that the grouping is correct. If there are data points close to 0 in the group, the data points close to 0 are reclassified until the data point is close to 0, which improves the accuracy of the grouping, thereby allowing each wind turbine to be accurately divided, thereby improving the accuracy of the equivalent results. The wind turbines in the same group in the optimal grouping scheme are subjected to equivalent processing, and the equivalent processing results are obtained as follows: The equivalent principle is to ensure that the total output power of the inverter before and after the equivalent is equal, and the dynamic process is close. In order to ensure that the total output power steady-state quantity of the aggregated single inverter model is equal to that of the actual multi-inverter parallel system, the equivalent circuit parameters of the wind farm aggregation model should be the parallel connection of each inverter element in the actual system: Where, L eq , C eq They are equivalent filter inductance, equivalent resistance and equivalent DC side capacitance respectively.

[0051] The calculation of equivalent control parameters is the key to ensure the accuracy of the dynamic characteristics of the wind farm aggregation model. The dynamic process of the DC side voltage of the inverter is determined by the input and output power. Therefore, the power balance equation of the grid-connected inverter is used to obtain the equivalent parameters of the voltage control loop. The power balance equation of the grid-connected inverter is: Where P in is the power input to the inverter on the DC side. Since the time scale of the current control loop response is faster than that of the voltage control loop, the dynamics of the current loop are ignored when aggregating the voltage control loop parameters. It is assumed that the actual current value can quickly track the current command. The power balance equation can be rewritten as:

[0052] The power balance equation of each inverter and the equivalent model satisfies the following formula. Through the power balance equation of the inverter and the constraint that the grid connection point voltage of the equivalent model and the detailed model is equal, the calculation formula of the proportional parameter and integral parameter of the equivalent voltage loop can be obtained as follows:

[0053] The current loop is a linear control link, so the frequency domain least squares method can be used to perform least squares fitting on the aggregate transfer function to obtain equivalent current loop control parameters. The transfer function of each inverter is multiplied by the capacity weighting coefficient, and then 10 points are taken from x = 1...10 to perform least squares fitting to obtain the control parameters of the wind farm aggregation model. The current loop transfer function of each wind turbine is: In the formula, is the transfer function of the current loop of the jth wind turbine inverter, c j is the capacity of the jth fan.

[0054] The equivalent phase-locked loop proportional coefficient and integral coefficient are taken as the weighted average of the PLL capacity in each inverter:

[0055] By performing equivalent treatment on multiple wind turbines, there is no need to perform separate modeling and simulation on each wind turbine when estimating and predicting the power generation capacity of a wind farm, thereby reducing the amount of calculation and time. Through equivalent treatment, the comprehensive characteristics of multiple wind turbines can be equivalent to the characteristics of one wind turbine, so that when estimating the power generation capacity of a wind farm, the overall power generation performance of the wind farm can be more accurately reflected, which helps to improve the prediction accuracy of the wind farm's power generation capacity and provide more reliable data support for the planning and construction of wind farms.

[0056] The specific implementation described above is a preferred implementation of the method for constructing a wind field aggregation model based on dominant modal characteristic clustering of the present invention, and is not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to this specific implementation. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A method for constructing a wind field aggregation model based on dominant modal characteristics clustering, characterized in that: The following steps are involved: S1: Construct a dynamic model of the wind power grid-connected system based on the state variables of the wind power grid-connected system; S2: obtaining dominant modal information of each wind turbine in the wind power grid-connected system under different operating conditions and different control parameters based on the wind farm parameters and the dynamic model; S3: acquiring a dominant data group of each wind turbine under different operating conditions and different control parameters according to the subsynchronous oscillation frequency in the state variable and the dominant modal information; S4: Grouping each wind turbine based on the dominant data group and the clustering algorithm to obtain an optimal grouping scheme, performing equivalent processing on the wind turbines in the same group in the optimal grouping scheme to obtain equivalent processing results, and constructing a wind farm aggregation model based on the equivalent processing results.

2. The method for constructing a wind field aggregation model based on dominant modal characteristic clustering according to claim 1 is characterized in that: The S1 includes: S11: constructing a sub-dynamic model of each component based on the state variables and the characteristics of each component in the wind power grid-connected system; S12: Constructing the dynamic model based on the sub-dynamic model and input variables of the wind power grid-connected system.

3. The method for constructing a wind field aggregation model based on dominant modal characteristic clustering according to claim 2 is characterized in that: In S12, the expression of the dynamic model is X=AY+BU, where X represents the dynamic model, A represents the state matrix, Y represents the state variable, B represents the input matrix, and U represents the input variable.

4. The method for constructing a wind field aggregation model based on dominant modal characteristic clustering according to claim 1 is characterized in that: The S2 includes: S21: inputting wind farm parameters corresponding to each wind turbine under different operating conditions and different control parameters into a dynamic model, thereby obtaining a state matrix of each wind turbine under different operating conditions and different control parameters; S22: obtaining modal information of each wind turbine under different operating conditions and different control parameters based on the eigenvalues ​​of the state matrix, and obtaining dominant modal information of each wind turbine under different operating conditions and different control parameters based on the modal information.

5. The method for constructing a wind field aggregation model based on dominant modal characteristic clustering according to claim 4 is characterized in that: In S22, the obtaining of the dominant modal information of each wind turbine under different operating conditions and different control parameters based on the modal information includes: The absolute values ​​of the real parts of the modal information of each fan under different operating conditions and different control parameters are compared, and the modal information with the smallest absolute value of the real part of the modal is taken as the dominant modal information of each fan under different operating conditions and different control parameters.

6. The method for constructing a wind field aggregation model based on dominant modal characteristic clustering according to claim 1 is characterized in that: The S3 includes: S31: constructing a dominant function according to the decay speed in the state variable and the subsynchronous oscillation frequency; S32: selecting a dominant data group of each wind turbine under different operating conditions and different control parameters from the dominant modal information using a dominant function and a time range preset based on the subsynchronous oscillation frequency.

7. The method for constructing a wind field aggregation model based on dominant modal characteristic clustering according to claim 6 is characterized in that: The S31 includes: The expression of the dominant function is y(t) = |e at cosβt|, where y(t) represents the dominant function value at time t, e represents the base of the natural logarithm, a is the decay rate, and β is the subsynchronous oscillation frequency.

8. The method for constructing a wind field aggregation model based on dominant modal characteristic clustering according to claim 1 is characterized in that: The S4 includes: S41: randomly selecting a number of clustering dominant data groups from the dominant data groups; S42: Mark the dominant data groups other than the cluster dominant data group in the dominant data group as process dominant data groups, calculate the Euclidean distance between the process dominant data group and the cluster dominant data group, allocate the process dominant data group based on the Euclidean distance to obtain an allocation result, update the cluster dominant data group based on the allocation result and obtain the update number, determine whether the update number is greater than the preset number, if so, execute S43, if less than or equal to, execute S42; S43: Obtain an optimal grouping solution based on the updated cluster-dominant data group.

9. The method for constructing a wind field aggregation model based on dominant modal characteristic clustering according to claim 8, characterized in that: The S43 includes: S431: obtaining a grouping scheme based on the updated cluster-dominant data group, and obtaining a silhouette coefficient of each data point in the same group based on an average distance between data points in the same group in the grouping scheme; S432: Obtain an optimal grouping solution based on the silhouette coefficient.

10. The method for constructing a wind field aggregation model based on dominant modal characteristic clustering according to claim 9, characterized in that: In S431, the expression for obtaining the silhouette coefficient of each data point in the same group based on the average distance between data points in the same group in the grouping scheme is: Wherein, s(i) represents the silhouette coefficient of the ith data point in the same group, a(i) represents the average distance between the ith data point in the same group and all other data points in the same group except the ith data point, and b(i) represents the average distance between the ith data point in the same group and all data points in the adjacent groups obtained from the grouping scheme based on the Euclidean distance.

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