A coherent equivalent method for grid-type converters

Abstract

A synchronization equivalence method applicable to a grid-type converter relates to the field of virtual synchronous machine algorithm control. A synchronization equivalence method applicable to a grid-type converter is used to cluster multiple inverters, comprising: constructing an inverter rotor motion equation; setting inverter reference parameters, substituting the inverter reference parameters into the general solution of the inverter rotor motion equation, and sequentially performing partial derivative and integral operations on the inverter reference parameters in the general solution of the inverter rotor motion equation to obtain inverter weight parameters; multiplying the inverter weight parameters with the parameters of each inverter to obtain weighted parameters of each inverter; and inputting the weighted parameters of all inverters into a K-means algorithm to obtain multiple inverter synchronization groups output by the K-means algorithm. The present invention introduces a partial derivative integral method to quantify the weights of parameters on the rotor angle trajectory, and combines this with a weighted K-means clustering strategy to solve the problem of inaccurate division of virtual synchronous machine synchronization groups.

Description

Technical Field

[0001] The present invention belongs to the field of grid-type converter control, and in particular relates to a coherent equivalence method applicable to grid-type converters. Background Art

[0002] With the expansion of the scale of new energy power systems, traditional coherence equivalence algorithms face significant deficiencies in dealing with multi-inverter parameter coupling problems. The currently commonly used K-means (K-means) clustering algorithm only relies on the Euclidean distance of parameters to divide the coherence groups, without considering the differentiated effects of different parameters (such as moment of inertia, damping coefficient, and line reactance) on the dynamic characteristics of the virtual rotor angle, resulting in vague physical meaning and low accuracy of the coherence group division. In addition, the existing methods do not effectively quantify the parameter weights and cannot dynamically adjust the clustering strategy during transient processes, resulting in large errors in the active and reactive power of the equivalent model, making it difficult to meet the requirements of high-precision reduced-order modeling. The existing model- or data-based coherence algorithms generally have problems such as poor adaptability to disturbance scenarios, low computational efficiency, and dependence on high-quality data, which limits their application in large-scale power systems. Summary of the Invention

[0003] The present invention aims to solve the problem of inaccurate inverter synchronization group division in the prior art due to the failure to consider the dynamic influence weights of parameters. A synchronization equivalence method suitable for grid-type converters is now provided. By introducing the partial derivative integral method to quantify the weights of inverter parameters on rotor angle trajectories, combined with the weighted K-means clustering strategy, the physical significance and accuracy of synchronization group division are significantly improved, providing an efficient and high-precision solution for power system reduced-order modeling.

[0004] In a first aspect, the present application provides a coherence equivalence method applicable to a grid-type converter, which is used to cluster multiple inverters, including:

[0005] Construct the inverter rotor motion equation;

[0006] Setting inverter reference parameters, substituting the inverter reference parameters into the general solution of the inverter rotor motion equation, and performing partial derivative and integral operations on the inverter reference parameters in the general solution of the inverter rotor motion equation in sequence to obtain inverter weight parameters;

[0007] Multiplying the inverter weight parameter by the parameter of each inverter to obtain a weighted parameter of each inverter;

[0008] The weighted parameters of all inverters are input into the K-means algorithm to obtain multiple inverter coherence groups output by the K-means algorithm.

[0009] In one possible design, the inverter rotor motion equations constructed above include:

[0010] The inverter rotor motion equation is obtained, and the inverter rotor motion equation is subjected to small signal linearization processing to obtain the small signal linearized inverter rotor motion equation.

[0011] In one possible implementation, the small-signal linearized inverter rotor motion equation is expressed as:

[0012]

[0013] Among them, v k is the output voltage of the kth inverter, E is the grid voltage, J k is the moment of inertia of the k-th inverter, X mk is the equivalent reactance between the kth inverter and the grid, D k is the damping coefficient of the kth inverter, and Δδ is the small signal disturbance change of the rotor angle.

[0014] In one possible design, the general solution to the inverter rotor motion equations for small-signal linearization is:

[0015]

[0016] Where C1 is a constant.

[0017] In a possible design, partial derivatives of the moment of inertia of the inverter reference machine, the damping coefficient of the inverter reference machine, and the line impedance of the inverter reference machine in the general solution of the small-signal linearized inverter rotor motion equation are respectively performed to obtain the calculation method of the partial derivative of the moment of inertia, the partial derivative of the damping coefficient, and the partial derivative of the line impedance:

[0018]

[0019] Where V is the output voltage of the inverter reference machine, J is the moment of inertia of the inverter reference machine, D is the damping coefficient of the inverter reference machine, X is the line impedance of the inverter reference machine, ω J is an intermediate variable;

[0020] The calculation method of the inverter moment of inertia weight, inverter damping coefficient weight and inverter line impedance weight is obtained by integrating the partial derivative of the moment of inertia, the partial derivative of the damping coefficient and the partial derivative of the line impedance respectively:

[0021]

[0022] Among them, K J is the moment of inertia weight, K D is the damping coefficient weight, K X is the line impedance weight.

[0023] A second aspect of the present application provides a synchronization equivalent method system applicable to a grid-type converter, comprising:

[0024] An equation building unit is used to: build an inverter rotor motion equation and send the inverter rotor motion equation to an inverter weight parameter calculation unit;

[0025] an inverter weight parameter calculation unit, configured to: set an inverter reference parameter, substitute the inverter reference parameter into a general solution of the inverter rotor motion equation, perform partial derivative and integral operations on the inverter reference parameter in the general solution of the inverter rotor motion equation in sequence to obtain an inverter weight parameter, and send the inverter weight parameter to a weighted parameter calculation unit;

[0026] A weighted parameter calculation unit, configured to: multiply the inverter weight parameter by the parameter of each inverter to obtain a weighted parameter of each inverter, and send the weighted parameter of each inverter to a clustering unit;

[0027] The clustering unit is used to input the weighted parameters of all inverters into the K-means algorithm to obtain multiple inverter homology groups output by the K-means algorithm.

[0028] A third aspect of the present application provides a computer storage medium, wherein the computer storage medium stores at least one instruction, wherein the at least one instruction is loaded and executed by a processor to implement a synchronization equivalence method applicable to a grid-type converter as described in the first aspect of the present application;

[0029] The fourth aspect of the present application provides a control device for a synchronization equivalent method suitable for a grid-type converter, comprising a storage device, a processor, and a computer program stored in the storage device and executable on the processor. The processor executes the computer program to implement the steps of a synchronization equivalent method suitable for a grid-type converter as described in the first aspect of the present application.

[0030] The fifth aspect of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of a synchronization equivalence method applicable to a grid-type converter as described in the first aspect of the present application.

[0031] Beneficial effects of the present invention: This application proposes a synchronization equivalence method applicable to grid-type converters, which solves the problem of inaccurate division of inverter synchronization groups; compared with the existing technology, it has significant effects:

[0032] (1) The partial derivative integration method is introduced to quantify the weight of the parameters on the rotor angular trajectory, which significantly improves the physical significance and accuracy of the homology group division.

[0033] (2) Combined with the weighted K-means clustering strategy, an efficient and high-precision solution is provided for power system reduced-order modeling. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A flow chart of a synchronization equivalent method applicable to a grid-type converter provided in an embodiment of the present application;

[0035] Figure 2 The topological structure of the inverter parallel model diagram provided in the embodiment of the present application using a synchronization equivalent method suitable for a grid-type converter is shown in FIG;

[0036] Figure 3 Schematic diagram of the inverter synchronization group provided in an embodiment of the present application;

[0037] Figure 4 A schematic diagram of the angle deviation of the three-phase short-circuit inverter synchronization group provided in an embodiment of the present application;

[0038] Figure 5 A schematic diagram of the angle deviation of the single-phase short-circuit inverter synchronization group provided in an embodiment of the present application;

[0039] Figure 6 A schematic diagram of a control device for a coherent equivalent method applicable to a grid-type converter provided in an embodiment of the present application;

[0040] Figure 7 A schematic diagram of a computer storage medium provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other in the absence of conflict.

[0042] The K-means clustering algorithm, currently used by mainstream methods, offers advantages in computational efficiency, but its static clustering mechanism, based solely on Euclidean distance, suffers from serious physical limitations. First, the algorithm fails to consider the differential impact of key parameters such as moment of inertia, damping coefficient, and line reactance on the dynamic characteristics of the virtual rotor angle, leading to the incorrect grouping of units with different electromechanical time constants. For example, units dominated by moment of inertia and units dominated by damping exhibit distinct dynamic response characteristics during power angle oscillation. Existing methods lack an effective identification mechanism for this, resulting in unclear physical meaning of the coherent grouping. Second, the parameter weighting lacks dynamic adaptability. When the system experiences transient disturbances, the weighting coefficients of the characteristic parameters cannot be adjusted in real time based on the fault type (e.g., symmetrical / asymmetrical short circuit), the disturbance location (near-end / remote fault), and the operating condition (high / low penetration scenarios). This results in errors of up to 15%-20% in active power and over 30% in reactive power in the equivalent model during transients, seriously impacting the accuracy of grid stability analysis.

[0043] Improved methods proposed by existing technologies still face significant bottlenecks: while physical model-based coherence criteria maintain theoretical rigor, they rely on precise differential equation solutions. When faced with hybrid systems containing multiple types of renewable energy, the model complexity grows exponentially, making it difficult to meet real-time simulation requirements. While data-driven clustering methods reduce model dependency, they are limited by PMU measurement noise, data synchronization accuracy, and insufficient feature extraction dimensions, resulting in a significant decrease in classification accuracy in weak power grids or harmonic pollution scenarios. Other studies have attempted to introduce fuzzy clustering or neural network algorithms, but these methods generally suffer from difficulties in parameter tuning, large training data requirements, and weak generalization capabilities, making them difficult to adapt to the dynamic characteristics of modern power systems with multiple time scales and operating modes.

[0044] Based on the above analysis, existing technologies still face the following challenges:

[0045] (1) The K-means clustering algorithm does not consider the differential influence weights of key parameters such as moment of inertia, damping coefficient, and line reactance on the dynamic characteristics of the virtual rotor angle, which reduces the accuracy of the coherent group classification;

[0046] (2) The coherence criterion based on the physical model relies on the accurate solution of differential equations. When faced with a hybrid system containing multiple types of new energy, the model complexity increases exponentially, making it difficult to meet the real-time simulation requirements.

[0047] In view of this, the embodiment of the present application provides a synchronization equivalent method applicable to the grid-type converter in order to solve the above problems. Figures 1 to 5 , the solution of the embodiment of this application is described in detail.

[0048] refer to Figure 1, an embodiment of the present application provides a coherent equivalence method applicable to a grid-type converter, for clustering a plurality of inverters, including: constructing an inverter rotor motion equation;

[0049] The inverter reference parameters are set, the inverter reference parameters are substituted into the general solution of the inverter rotor motion equation, and the inverter reference parameters in the general solution of the inverter rotor motion equation are sequentially subjected to partial derivative and integral operations to obtain inverter weight parameters; the inverter weight parameters are multiplied by the parameters of each inverter respectively to obtain weighted parameters of each inverter; the weighted parameters of all inverters are input into the K-means algorithm to obtain multiple inverter coherence groups output by the K-means algorithm.

[0050] In one embodiment, constructing the inverter rotor motion equation may include: obtaining the inverter rotor motion equation, and performing small-signal linearization processing on the inverter rotor motion equation to obtain a small-signal linearized inverter rotor motion equation.

[0051] In one embodiment, setting the inverter reference parameters may include: selecting an inverter reference machine, and using the moment of inertia of the inverter reference machine, the damping coefficient of the inverter reference machine, and the line impedance of the inverter reference machine as the inverter reference parameters;

[0052] Substituting the inverter reference parameters into the general solution of the inverter rotor motion equation, performing partial derivative and integral operations on the inverter reference parameters in the general solution of the inverter rotor motion equation in sequence to obtain the inverter weight parameters, which may include:

[0053] Substitute the moment of inertia of the inverter reference machine, the damping coefficient of the inverter reference machine, and the line impedance of the inverter reference machine into the general solution of the inverter rotor motion equation of small signal linearization;

[0054] Partial derivatives of the moment of inertia of the inverter reference machine, the damping coefficient of the inverter reference machine, and the line impedance of the inverter reference machine in the general solution of the small-signal linearized inverter rotor motion equation are respectively performed to obtain the partial derivative of the moment of inertia, the partial derivative of the damping coefficient, and the partial derivative of the line impedance;

[0055] The partial derivative of the moment of inertia, the partial derivative of the damping coefficient and the partial derivative of the line impedance are integrated respectively to obtain the inverter moment of inertia weight, the inverter damping coefficient weight and the inverter line impedance weight.

[0056] In one embodiment, the small-signal linearized inverter rotor motion equation is expressed as:

[0057]

[0058] Among them, v kis the output voltage of the kth inverter, E is the grid voltage, J k is the moment of inertia of the k-th inverter, X k is the line impedance of the kth inverter, D k is the damping coefficient of the kth inverter, and Δδ is the small signal disturbance change of the rotor angle.

[0059] In one possible design, the setting of the inverter reference parameters includes: selecting an inverter reference machine, and using the moment of inertia of the inverter reference machine, the damping coefficient of the inverter reference machine, and the line impedance of the inverter reference machine as the inverter reference parameters;

[0060] Substitute the inverter reference parameters into the general solution of the inverter rotor motion equation, and perform partial derivative and integral operations on the inverter reference parameters in the general solution of the inverter rotor motion equation to obtain the inverter weight parameters, including:

[0061] Substitute the moment of inertia of the inverter reference machine, the damping coefficient of the inverter reference machine, and the line impedance of the inverter reference machine into the general solution of the inverter rotor motion equation of small signal linearization;

[0062] Partial derivatives of the moment of inertia of the inverter reference machine, the damping coefficient of the inverter reference machine, and the line impedance of the inverter reference machine in the general solution of the small-signal linearized inverter rotor motion equation are respectively performed to obtain the partial derivative of the moment of inertia, the partial derivative of the damping coefficient, and the partial derivative of the line impedance;

[0063] The partial derivative of the moment of inertia, the partial derivative of the damping coefficient and the partial derivative of the line impedance are integrated respectively to obtain the inverter moment of inertia weight, the inverter damping coefficient weight and the inverter line impedance weight.

[0064] In one example, an inverter whose rotation inertia of the reference inverter, the damping coefficient of the reference inverter, and the line impedance of the reference inverter are closest to an average value is selected as the reference inverter.

[0065] In one embodiment, the general solution of the inverter rotor motion equation for small signal linearization is:

[0066]

[0067] Where C1 is a constant.

[0068] The moment of inertia of the inverter reference machine, the damping coefficient of the inverter reference machine, and the line impedance of the inverter reference machine in the general solution of the small-signal linearized inverter rotor motion equation are respectively subjected to partial derivative operations. The calculation method of the partial derivative of the moment of inertia, the partial derivative of the damping coefficient, and the partial derivative of the line impedance can be obtained as follows:

[0069]

[0070] Where V is the output voltage of the inverter reference machine, J is the moment of inertia of the inverter reference machine, D is the damping coefficient of the inverter reference machine, X is the line impedance of the inverter reference machine, ω J is an intermediate variable;

[0071] The inverter moment of inertia weight, inverter damping coefficient weight and inverter line impedance weight are obtained by integrating the partial derivative of the moment of inertia, the partial derivative of the damping coefficient and the partial derivative of the line impedance respectively. The calculation method can be:

[0072]

[0073] Among them, K J is the moment of inertia weight, K D is the damping coefficient weight, K X is the line impedance weight.

[0074] In one embodiment, multiplying the inverter weight parameter by the parameter of each inverter to obtain the weighted parameter of each inverter may include:

[0075] The parameters of the inverter include moment of inertia, damping coefficient and line impedance; the moment of inertia, damping coefficient and line impedance of each inverter are multiplied by the inverter moment of inertia weight, inverter damping coefficient weight and inverter line impedance weight respectively to obtain the weighted equivalent inertia, weighted equivalent damping and weighted equivalent reactance corresponding to each inverter;

[0076] The weighted equivalent inertia, weighted equivalent damping and weighted equivalent reactance constitute the weighted parameters.

[0077] In one embodiment, each inverter synchronization group includes a plurality of inverters, and each inverter belongs to a corresponding inverter synchronization group.

[0078] To further introduce the solution of the embodiment of this application, Figure 2 A parallel inverter model diagram using a synchronization equivalent method suitable for grid-type converters is provided;

[0079] When performing coherent analysis on power systems, it is usually assumed that the coherent clustering is independent of the disturbance. This allows the small signal model of the system to be used to linearize the nonlinear quantities in the case of small disturbances. That is, the clustering results under small disturbances are also applicable to large disturbances, except that the threshold selection is different.

[0080] In a strong power grid environment, the voltage amplitude and phase at the point of common coupling (PCC) can be regarded as being clamped by an infinite power source. The inverter parallel model topology used in this application is as follows: Figure 2As shown, it includes n inverters connected in parallel to the grid, wherein the resistance between inverter k and the grid is Rg2-k, the reactance between inverter k and the grid is Lg2-k, the line resistance is Rg1, the line inductance is Lg1, the load resistance is Rload, and the impedance of the grid is XgT;

[0081] The output power of the kth inverter is shown in formula (1):

[0082]

[0083] Where n is the number of inverters, P ek is the output power of the kth inverter, v k is the output voltage of the kth inverter, G mk is the conductance directly connected to the node of the kth inverter, E is the grid voltage, δ mk is the phase difference between the voltage of the kth inverter and the grid voltage, B mk is the susceptance directly connected to the node of the kth inverter;

[0084] Assuming that the line is inductive and ignoring the line resistance, equation (1) can be simplified to equation (2):

[0085]

[0086] It can be seen that the output active power of the inverter is not coupled with other inverters and is only related to its own parameters and line parameters; the rotor angle of the inverter is controlled by the rotor motion equation of the active link, which is shown in formula (3):

[0087]

[0088] Among them, P mk is the reference power of the kth inverter, D k is the damping coefficient of the kth inverter, ω k is the rated angular velocity of the kth inverter, J k is the moment of inertia of the kth inverter, ω grid is the grid angular velocity;

[0089] The small-signal linearized inverter rotor motion equation is obtained by performing small-signal linearization on Equation (3), namely, Equation (4):

[0090]

[0091] Among them, X k is the line impedance of the k-th inverter, Δδ is the small signal disturbance change of the rotor angle;

[0092] Observing formula (4), it is found that this formula is a second-order homogeneous differential equation with constant coefficients. The general solution of the inverter rotor motion equation for small signal linearization is:

[0093]

[0094] Where C1 is a constant, v k E is a fixed value;

[0095] Therefore, the rotor motion trend of each inverter is only related to the moment of inertia, damping coefficient and the line impedance directly connected to the inverter; by analyzing the distribution of the moment of inertia, damping coefficient and line impedance, the clustering results of the inverters can be roughly predicted.

[0096] When calculating the weight, the inverter whose moment of inertia, damping coefficient and line impedance are closest to the average value among the n inverters is set as the inverter reference machine, and the moment of inertia of the inverter reference machine, the damping coefficient of the inverter reference machine and the line impedance of the inverter reference machine are used as the inverter reference parameters. Partial derivative operations are performed on the moment of inertia of the inverter reference machine, the damping coefficient of the inverter reference machine and the line impedance of the inverter reference machine respectively to obtain their influence on the rotor motion trend at each moment. By integrating these partial derivatives, the total influence of each parameter on the rotor motion during the disturbance time can be determined;

[0097] The partial derivative expression of the inverter reference parameter is formula (6):

[0098]

[0099] Where, V is the output voltage of the inverter reference machine, J is the moment of inertia of the inverter reference machine, D is the damping coefficient of the inverter reference machine, X is the line impedance of the inverter reference machine, ω J is an intermediate variable.

[0100] After integrating formula (6), the inverter weight parameter expression is obtained as formula (7):

[0101]

[0102] Among them, K J is the moment of inertia weight, K D is the damping coefficient weight, K X is the line impedance weight;

[0103] The moment of inertia, damping coefficient and line impedance of each inverter are multiplied by the inverter moment of inertia weight, inverter damping coefficient weight and inverter line impedance weight respectively to obtain the weighted equivalent inertia, weighted equivalent damping and weighted equivalent reactance corresponding to each inverter. The calculation formula of weighted equivalent inertia, weighted equivalent damping and weighted equivalent reactance is formula (8):

[0104]

[0105] Among them, K J_k is the weighted equivalent inertia of the k-th inverter, K D_k is the weighted equivalent damping of the k-th inverter, K X_k is the weighted equivalent reactance of the k-th inverter, J k is the moment of inertia of the kth inverter, D k is the damping coefficient of the kth inverter, X k is the line impedance of the k-th inverter.

[0106] Define the required number of cluster groups and use the K-means algorithm to cluster these arrays to obtain the corresponding cluster groups.

[0107] Furthermore, in order to verify the correctness and effectiveness of the method of the present invention, a parallel system with ten inverters was established on the PLECS (piecewise linear electrical circuit) simulation platform. These inverters were all controlled by the virtual synchronous machine algorithm and were connected in parallel and operated together on the same AC bus. The system and inverter parameters are shown in Tables 1 and 2.

[0108] In a simulated small disturbance scenario, the system behavior was simulated by applying the same three-connected resistor grounding. The first machine was selected as the reference machine, and the weights of various parameters were calculated based on the reference machine. These weights reflect the impact of each parameter on the virtual rotor angle in the disturbance scenario and are recorded in Table 3.

[0109] Table 1 System parameters

[0110] System parameters Numerical Line resistance Rg1-k (k∈[1,2,3···,10]) 0.1Ω Line inductance Lg1-k (k∈[1,2,3···,10]) 2e-3H Load resistance Rload 60Ω Inverter output side resistance Rg2 0.1Ω Inverter output side inductor Lg2 1e-3H Grid voltage E 311V (volts)

[0111] After weighting the parameters, the K-means algorithm can be used to classify them. By representing the weighted data points in a three-dimensional coordinate system, a weighted discrimination homology operation can be performed. After weighted discrimination homology, the homologous inverters are represented with the same color to highlight their correlation. The results are as follows: Figure 3 shown.

[0112] According to the observation results, Figure 3 In the figure, we can see that inverters 1, 4, 6, and 9 form synchronization group 11, inverters 2, 7, and 8 form synchronization group 2, and inverters 3, 5, and 10 form synchronization group 3. To verify the correctness of the synchronization judgment method, after steady state (4 seconds), single-phase short circuit and three-phase short circuit faults were applied and the rotor angular motion curve was observed. Figure 4 Schematic diagram of the inverter synchronization group angle deviation with a three-phase short circuit applied. Figure 5Figure 3 is a schematic diagram of the inverter synchronization group angle deviation with a single-phase short circuit applied; such observations help verify the accuracy and effectiveness of the synchronization judgment method.

[0113] Table 2 Inverter parameters

[0114]

[0115]

[0116] Table 3 The weight of each parameter affecting the virtual rotor angle

[0117] Virtual moment of inertia weight Virtual damping coefficient weight Virtual line impedance weight -0.97 0.01 -0.26

[0118] according to Figure 4 and Figure 5 The observation results show that the inverters in the same synchronization group show similar virtual rotor motion curves during the fault period. It can be found that the virtual rotor motion curves of inverters 1, 4, 6, 9, inverters 2, 7, 8, and inverters 3, 5, and 10 have the same change rules, which further verifies the accuracy of the synchronization judgment method.

[0119] The present application also provides a method system for coherence equivalence of a grid-type converter, referring to Figure 6 As shown, the system includes:

[0120] An equation building unit, configured to: build a small-signal linearized inverter rotor motion equation, and send the small-signal linearized inverter rotor motion equation to an inverter weight parameter calculation unit;

[0121] The inverter weight parameter calculation unit is used to set the inverter reference parameters, substitute the inverter reference parameters into the general solution of the small-signal linearized inverter rotor motion equation, perform partial reciprocal and integral operations to obtain the inverter weight parameters, and send the inverter weight parameters to the weight parameter calculation unit;

[0122] A weighted parameter calculation unit, configured to: multiply the inverter weight parameter by the parameter of each inverter to obtain a weighted parameter of each inverter, and send the weighted parameter of each inverter to a clustering unit;

[0123] The clustering unit is used to input the weighted parameters of all inverters into the K-means algorithm to obtain multiple inverter homology groups output by the K-means algorithm.

[0124] The present application also provides a computer storage medium, referring to Figure 7As shown, the computer storage medium stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement a synchronization equivalence method applicable to a grid-type converter as provided in the embodiment of the present application above.

[0125] An embodiment of the present application also provides a control device for a synchronization equivalent method suitable for a grid-type converter, comprising a storage device, a processor, and a computer program stored in the storage device and executable on the processor. The processor executes the computer program to implement a synchronization equivalent method suitable for a grid-type converter as provided in the embodiment of the present application described above.

[0126] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of a synchronization equivalence method applicable to a grid-type converter as provided in the embodiment of the present application above.

[0127] Although the present application is described herein with reference to specific embodiments, it should be understood that these embodiments are merely illustrative of the principles and applications of the present application. It should be understood that many modifications may be made to the illustrative embodiments, and that other arrangements may be devised, without departing from the spirit and scope of the present application as defined by the appended claims. It should be understood that the various dependent claims and features described herein may be combined in ways other than those described in the original claims. It should also be understood that features described in conjunction with individual embodiments may be used in conjunction with other described embodiments.

Claims

1. A coherent equivalent method applicable to a grid-type converter, characterized by: Used to cluster multiple inverters, including: Construct the inverter rotor motion equation; Setting inverter reference parameters, substituting the inverter reference parameters into the general solution of the inverter rotor motion equation, and performing partial derivative and integral operations on the inverter reference parameters in the general solution of the inverter rotor motion equation in sequence to obtain inverter weight parameters; Multiplying the inverter weight parameter by the parameter of each inverter to obtain a weighted parameter of each inverter; The weighted parameters of all inverters are input into the K-means algorithm to obtain multiple inverter coherence groups output by the K-means algorithm; Construct the inverter rotor motion equations, including: Constructing the inverter rotor motion equation, and performing small signal linearization processing on the inverter rotor motion equation to obtain the small signal linearized inverter rotor motion equation; The small signal linearized inverter rotor motion equation is expressed as: Among them, v k is the output voltage of the kth inverter, E is the grid voltage, J k is the moment of inertia of the k-th inverter, X k is the line impedance of the kth inverter, D k is the damping coefficient of the kth inverter, Δδ is the small signal disturbance change of the rotor angle; The moment of inertia of the inverter reference machine, the damping coefficient of the inverter reference machine, and the line impedance of the inverter reference machine in the general solution of the small-signal linearized inverter rotor motion equation are respectively subjected to partial derivative operations. The calculation methods of the partial derivatives of the moment of inertia, the partial derivatives of the damping coefficient, and the partial derivatives of the line impedance are obtained as follows: Where V is the output voltage of the inverter reference machine, J is the moment of inertia of the inverter reference machine, D is the damping coefficient of the inverter reference machine, X is the line impedance of the inverter reference machine, ω J is an intermediate variable; The calculation method of the inverter moment of inertia weight, inverter damping coefficient weight and inverter line impedance weight is obtained by integrating the partial derivative of the moment of inertia, the partial derivative of the damping coefficient and the partial derivative of the line impedance respectively: Among them, K J is the moment of inertia weight, K D is the damping coefficient weight, K X is the line impedance weight.

2. A coherent equivalent method applicable to a grid-type converter according to claim 1, characterized in that: The general solution of the inverter rotor motion equation for small signal linearization is: Where C1 is a constant.

3. A coherent equivalent method system applicable to a grid-type converter, characterized by: include: An equation building unit is used to: build an inverter rotor motion equation and send the inverter rotor motion equation to an inverter weight parameter calculation unit; an inverter weight parameter calculation unit, configured to: set an inverter reference parameter, substitute the inverter reference parameter into a general solution of the inverter rotor motion equation, perform partial derivative and integral operations on the inverter reference parameter in the general solution of the inverter rotor motion equation in sequence to obtain an inverter weight parameter, and send the inverter weight parameter to a weighted parameter calculation unit; A weighted parameter calculation unit, configured to: multiply the inverter weight parameter by the parameter of each inverter to obtain a weighted parameter of each inverter, and send the weighted parameter of each inverter to a clustering unit; The clustering unit is used to input the weighted parameters of all inverters into the K-means algorithm to obtain multiple inverter homology groups output by the K-means algorithm; Construct the inverter rotor motion equations, including: Constructing the inverter rotor motion equation, and performing small signal linearization processing on the inverter rotor motion equation to obtain the small signal linearized inverter rotor motion equation; The small signal linearized inverter rotor motion equation is expressed as: Among them, v k is the output voltage of the kth inverter, E is the grid voltage, J k is the moment of inertia of the k-th inverter, X k is the line impedance of the kth inverter, D k is the damping coefficient of the kth inverter, Δδ is the small signal disturbance change of the rotor angle; The moment of inertia of the inverter reference machine, the damping coefficient of the inverter reference machine, and the line impedance of the inverter reference machine in the general solution of the small-signal linearized inverter rotor motion equation are respectively subjected to partial derivative operations. The calculation methods of the partial derivatives of the moment of inertia, the partial derivatives of the damping coefficient, and the partial derivatives of the line impedance are obtained as follows: Where V is the output voltage of the inverter reference machine, J is the moment of inertia of the inverter reference machine, D is the damping coefficient of the inverter reference machine, X is the line impedance of the inverter reference machine, ω J is an intermediate variable; The calculation method of the inverter moment of inertia weight, inverter damping coefficient weight and inverter line impedance weight is obtained by integrating the partial derivative of the moment of inertia, the partial derivative of the damping coefficient and the partial derivative of the line impedance respectively: Among them, K J is the moment of inertia weight, K D is the damping coefficient weight, K X is the line impedance weight.

4. A computer storage medium, characterized in that: The computer storage medium stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the synchronization equivalence method applicable to a grid-type converter as claimed in any one of claims 1 to 2.

5. A control device for a coherent equivalent method for a grid-type converter, comprising a storage device, a processor, and a computer program stored in the storage device and executable on the processor, characterized in that: The processor executes the computer program to implement the steps of a synchronization equivalence method applicable to a grid-type converter as claimed in any one of claims 1 to 2.

6. A computer program product comprising a computer program, characterized in that: When the computer program is executed by a processor, the steps of a synchronization equivalence method applicable to a grid-type converter as claimed in any one of claims 1 to 2 are implemented.

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