Power system reactive power partitioning method and system based on short-circuit current support capability

Abstract

The present invention belongs to the technical field of dynamic reactive power compensation optimization, and specifically relates to a method and system for reactive power zoning of an electric power system based on short-circuit current supporting capability, comprising: obtaining quantized values of the short-circuit current supporting capability of reactive sources at different installation positions; calculating the short-circuit current promotion limit between nodes based on the obtained quantized values; calculating the node distance matrix based on the obtained short-circuit current promotion limit between nodes; and performing clustering and partitioning of the node distance matrix based on a density peak method to complete reactive power zoning of the electric power system.

Description

Technical Field

[0001] The present invention belongs to the technical field of dynamic reactive power compensation optimization, and in particular relates to a method and system for reactive power zoning of an electric power system based on short-circuit current support capability. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the rapid development of renewable energy generation, modern power systems are gradually evolving into new ones dominated by renewable energy. Conventional generators are being largely replaced by renewable energy, and the proportion of installed power capacity from traditional synchronous generators continues to decline. When renewable energy is integrated into the grid via power electronic devices, they are limited by the control methods and device overcurrent capabilities. In the event of a grid fault, the short-circuit current they can provide is far less than that of synchronous generators. This weakens the power system's transient reactive power support capability and gradually reduces voltage stability.

[0004] To improve the system's transient voltage stability and strengthen its reactive power support capabilities, optimizing the configuration of dynamic reactive power sources has become a critical issue for new power systems. Dynamic reactive power sources refer to dynamic reactive compensation components such as phase-shifting converters, SVGs, grid-connected energy storage, and new energy generators with active support capabilities. During faults, they typically exhibit voltage source characteristics and can output dynamic reactive power when the system voltage drops, providing the system with additional short-circuit current and enhancing its voltage stability. Power systems are large in scale and complex in structure. To rationally plan the configuration of dynamic reactive power sources, power grids generally use a voltage zoning method, dividing the power system into multiple sub-regions in the spatial dimension and decoupling and optimizing the dynamic reactive power sources within each region.

[0005] According to the inventors' understanding, power grids usually partition power systems based on electrical distance indicators, where electrical distance is the equivalent impedance between system nodes. In view of the problem of optimal configuration of dynamic reactive sources in new power systems, the main purpose of partitioning the power system is to ensure the short-circuit current support capability of dynamic reactive sources for nodes in the region. However, the relationship between the electrical distance indicator and the short-circuit current support of reactive sources is not clear. The clustering partitioning method based on electrical distance is difficult to ensure that reactive sources in the region fully exert their short-circuit current support capability. In addition, most partitioning methods are still based on traditional clustering algorithms such as the K-means algorithm for partitioning. However, the clustering accuracy for irregular shape clustering problems is poor, and multiple iterative operations are required, which is not feasible for power systems with large data sets and complex and changeable data point distribution. Summary of the Invention

[0006] To solve the above problems, the present invention proposes a method and system for reactive power zoning of an electric power system based on short-circuit current supporting capability. Aiming at the problem of optimal configuration of dynamic reactive power zoning in new electric power systems, the short-circuit current supporting capability of dynamic reactive sources at different installation locations is quantitatively calculated to obtain a distance index reflecting the short-circuit current supporting capability of different nodes. The density peak clustering algorithm (DPC) is used to complete the power system zoning, providing technical guidance for the optimal configuration of dynamic reactive sources in the power system.

[0007] According to some embodiments, a first solution of the present invention provides a method for reactive power partitioning of a power system based on short-circuit current support capability, which adopts the following technical solution:

[0008] A method for reactive power partitioning of a power system based on short-circuit current support capability, comprising:

[0009] Obtain the quantitative value of the short-circuit current supporting capacity of the reactive power source at different installation locations;

[0010] Calculate the short-circuit current improvement limit between nodes based on the obtained quantized value;

[0011] Calculate the node distance matrix based on the obtained short-circuit current improvement limit between nodes;

[0012] The node distance matrix is clustered and partitioned based on the density peak method to complete the reactive power partitioning of the power system.

[0013] As a further technical limitation, the branch addition method is used to obtain the quantitative value of the reactive source short-circuit current supporting capacity under different installation positions, that is, the strength of the node reactive source's supporting capacity for the node short-circuit current.

[0014] As a further technical limitation, the inter-node short-circuit current increase limit is the upper limit of the reactive source of one node's ability to support the short-circuit current of another node, which is only related to the mutual impedance and self-impedance between the two nodes, indicating the influence of the node topological position on its short-circuit current supporting capacity.

[0015] As a further technical limitation, the size of the short-circuit current increase limit between nodes is positively correlated with the short-circuit current supporting capacity between the two nodes. The inverse of the short-circuit current increase limit between nodes is used to represent the node distance of the short-circuit current supporting capacity between nodes. The obtained node distance is normalized and represented in matrix form to obtain a node distance matrix.

[0016] As a further technical limitation, the higher the inter-node short-circuit current increase limit between two nodes, the stronger the short-circuit current support capability of the other node when the reactive source is installed at one of the nodes, and the stronger the connection between the two nodes; the inter-node short-circuit current increase limit is symmetrical.

[0017] As a further technical limitation, the specific process of clustering and partitioning the node distance matrix based on the density peak method is: calculating the local density and node relative distance of each data point, and excluding isolated nodes; finding the density peak point in the data set as the cluster center point; clustering according to the found cluster center point to obtain node cluster partitioning.

[0018] According to some embodiments, a second solution of the present invention provides a power system reactive power partitioning system based on short-circuit current support capability, which adopts the following technical solutions:

[0019] A reactive power partitioning system for a power system based on short-circuit current support capability, comprising:

[0020] an acquisition module configured to obtain a quantitative value of the short-circuit current supporting capability of the reactive power source at different installation positions;

[0021] A calculation module is configured to calculate the short-circuit current promotion limit between nodes based on the obtained quantized value; and calculate the node distance matrix based on the obtained short-circuit current promotion limit between nodes;

[0022] The partitioning module is configured to perform clustering partitioning of the node distance matrix based on the density peak method to complete the reactive power partitioning of the power system.

[0023] According to some embodiments, a third solution of the present invention provides a computer-readable storage medium, which adopts the following technical solution:

[0024] A computer-readable storage medium stores a program thereon, which, when executed by a processor, implements the steps of the method for reactive power partitioning of a power system based on short-circuit current supporting capability as described in the first solution of the present invention.

[0025] According to some embodiments, a fourth solution of the present invention provides an electronic device, which adopts the following technical solution:

[0026] An electronic device includes a memory, a processor, and a program stored in the memory and running on the processor. When the processor executes the program, it implements the steps of the power system reactive power partitioning method based on short-circuit current support capability as described in the first embodiment of the present invention.

[0027] According to some embodiments, a fifth solution of the present invention provides a computer program product, which adopts the following technical solution:

[0028] A computer program product includes software codes, wherein the program in the software codes executes the steps in the method for reactive power partitioning of a power system based on short-circuit current support capability as described in the first embodiment of the present invention.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] The present invention addresses the problem of optimal configuration of dynamic reactive power zoning in novel power systems. It quantitatively calculates the short-circuit current supporting capacity of dynamic reactive sources at different installation locations, obtains distance indicators reflecting the short-circuit current supporting capacity of different nodes, and uses a density peak clustering algorithm to complete the power system zoning, providing technical guidance for optimal configuration of dynamic reactive sources in power systems.

[0031] The present invention deduces the short-circuit current supporting capacity limit of reactive sources when installed at different nodes, uses it as a distance indicator for clustering and partitioning of the power system, and fully considers the influence of the reactive source installation position on its short-circuit current supporting capacity. When optimizing the configuration of dynamic reactive sources, the short-circuit current supporting capacity can be fully utilized, while the optimization configuration range is narrowed. The density peak clustering algorithm is used for clustering and partitioning, which does not require multiple iterative calculations, has simple calculation steps, high clustering accuracy, and good adaptability to various power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings constituting a part of the specification of this embodiment are used to provide a further understanding of this embodiment. The schematic embodiments and descriptions of this embodiment are used to explain this embodiment and do not constitute an improper limitation on this embodiment.

[0033] Figure 1 This is a flow chart of a method for reactive power partitioning of a power system based on short-circuit current support capability in the first embodiment of the present invention;

[0034] Figure 2 Schematic diagram of an equivalent model for dynamic reactive power source access in a power system according to the first embodiment of the present invention;

[0035] Figure 3 Schematic diagram of the SCIL indicator and its accuracy verification in Example 1 of the present invention;

[0036] Figure 4 This is a schematic diagram of the IEEE39 node partitioning result in Example 1 of the present invention. DETAILED DESCRIPTION

[0037] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0038] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present application belongs.

[0039] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0040] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0041] Example 1

[0042] Embodiment 1 of the present invention introduces a method for reactive power partitioning of a power system based on short-circuit current support capability.

[0043] This embodiment aims at the problem of optimizing the configuration of dynamic reactive power source partitions in a new power system, and proposes a reactive power partitioning method for a power system based on short-circuit current support capability. Figure 1 As shown in the figure, the short-circuit current supporting capacity of the dynamic reactive source at different installation positions is first quantitatively calculated, and based on this, the distance index reflecting the short-circuit current supporting capacity of different nodes is obtained. Finally, the power system partition is completed according to the density peak clustering algorithm, providing guidance for the optimal configuration of the dynamic reactive source.

[0044] (1) Quantification method of reactive power source short-circuit current support capacity at different installation locations

[0045] This embodiment uses the branch addition method to quantify the short-circuit current support capacity of reactive power sources at different installation locations. In the per-unit system, the short-circuit current of a node n in the power system is expressed as the inverse of the self-impedance in the impedance matrix of the node, that is,

[0046]

[0047] Among them, I fm,n , Z nn are the short-circuit current and self-impedance of node n, respectively. When a dynamic reactive source is connected to another node i in the system, the reactive source can be equivalent to a series connection of a voltage source and an impedance, that is, a grounding chain branch is added to node i, and its equivalent impedance is Z eq , the internal potential amplitude is E, and the phase angle is δ, as Figure 2 As shown. Assume that before the reactive power source is installed, the system impedance matrix is Z 0 ,Right now

[0048] U=Z 0 I 0 (2)

[0049] Among them, U, I 0 are the node voltage and injected current of each node before the reactive source is installed. The access impedance of node i is Z eq After the grounding branch is connected, the injection current at this point changes. Let the change be I g ,but:

[0050]

[0051] Among them, I 1 is the current injected into each node after the reactive source is installed. Substituting into formula (2), we have:

[0052] U=Z 0 I 0 =Z 0 I 1 -Z 0 A m I g (4)

[0053] The voltage equation of node i can be obtained:

[0054] U i =Z eq I g =A m T U (5)

[0055] Combining formula (2), formula (3), formula (4) and formula (5), the change of the node impedance matrix after the reactive source is connected is:

[0056]

[0057] Among them, Z 1 This is the system node impedance matrix after the reactive source is installed. Expanding the denominator in formula (6), we get:

[0058]

[0059] Where ΔZ is the change in each element of the system impedance matrix before and after the reactive source is installed. Formula (7) shows that when a node in the system is connected to a dynamic reactive source, the self-impedance of all nodes will change, that is:

[0060]

[0061] Where ΔZ nnThis is the change in the self-impedance of any point n in the system before and after the reactive source is installed. Furthermore, to simplify the expression, the superscript "0" is ignored here. According to formula (1), the node self-impedance and its short-circuit current have an inverse relationship. Therefore, the change rate of the short-circuit current of each node in the system after the reactive source is installed can be derived from formula (8), that is:

[0062]

[0063] Formula (9) is an analytical expression of the effect of a reactive source installed at node i on the short-circuit current of any node n in the system. Its magnitude represents the change in the short-circuit current of another node n in the system when a reactive source is installed at node i, reflecting the strength of the reactive source at node i in supporting the short-circuit current of node n.

[0064] (2) Calculation method of node distance index based on short-circuit current support capacity

[0065] The basis for partitioning the power system is to obtain the distance matrix between data points. Therefore, this section uses the quantitative formula for the short-circuit current support capacity between nodes obtained in the previous section, that is, formula (9), to obtain the node distance matrix that can reflect the short-circuit current support capacity between nodes.

[0066] From formula (9), we can know that the supporting capacity of a node's reactive power source to another node in the system is proportional to the capacity of the reactive power source itself (i.e., Z eq ) and the topological positions of the two nodes in the power system (i.e., Z ii , Z nn and Z in ) are related. When the topological positions of the two nodes are determined, the short-circuit current support rate increases with the increase of reactive source capacity (the decrease of equivalent impedance). When the reactive source capacity approaches infinity, that is, the equivalent impedance is zero, its short-circuit current improvement rate for the other node will reach the theoretical maximum value, which is defined as the short-circuit current improvement limit (SCIL) between the two nodes, that is,

[0067]

[0068] The SCIL index represents the upper limit of the short-circuit current support capability of a reactive source at one node for another node. It is only related to the mutual impedance and self-impedance between the two nodes, reflecting the impact of the node topological location on its short-circuit current support capability. Obviously, the higher the SCIL between the two nodes, the stronger the short-circuit current support capability of the reactive source when installed at one node for the other node, and the stronger the connection between the two nodes. In addition, the SCIL index is symmetrical, that is, SCIL ij =SCIL ji .

[0069] The size of the SCIL index is positively correlated with the short-circuit current support capability between two nodes, so the inverse of the SCIL index can be expressed as the "distance" of the short-circuit current support capability between nodes, that is,

[0070]

[0071] Among them, d ij ' is the distance between nodes i and j. In addition, in order to more intuitively reflect the distance between two nodes, the distance is normalized and written in matrix form, that is:

[0072]

[0073]

[0074] Among them, d ij is the normalized expression of the distance between nodes i and j, and W is the node distance matrix. Formula (10), Formula (11), Formula (12), and Formula (13) are the distance matrix calculation methods based on the short-circuit current support capacity between nodes.

[0075] (3) Node clustering partitioning method based on density peak algorithm

[0076] After obtaining the node distance matrix reflecting the short-circuit current supporting capacity between nodes, this embodiment uses the Density Peak Clustering (DPC) algorithm to partition the system nodes. It can automatically discover the density peak points in the data based on the distance matrix and cluster the data according to these peak points. The principle is simple and effective, and the physical meaning is clear.

[0077] The specific steps of the node clustering partitioning method based on the density peak algorithm are as follows:

[0078] 1) Calculate the local density ρ of each data point i .

[0079] In the DPC algorithm, the local density ρ i Refers to the number of data points within a certain radius around a data point, which can be used to describe the density near the point, that is,

[0080] ρ i =∑K(d(i,j)) (14)

[0081] Among them, K(d(i,j)) is a kernel function used to measure the influence of the distance d(i,j) between data points i and j. Kernel functions include Gaussian kernel function and truncated kernel function. The formula for calculating local density using Gaussian kernel function is:

[0082]

[0083] The formula for calculating the local density using the truncated kernel function is:

[0084]

[0085] Among them, d c The neighborhood cutoff distance between defined data points represents the distance between two points. If the distance between two nodes exceeds the cutoff distance, the nodes are far apart. The choice of the cutoff distance depends on the specific circumstances of the dataset. In this example, since the node distances are normalized, a cutoff distance of 0.9 is selected.

[0086] It is important to note that the choice of Gaussian kernel function and truncated kernel function should be determined by the number of nodes in the dataset. For larger datasets, the truncated kernel method has better clustering results, while for smaller datasets, the Gaussian kernel method has better clustering results.

[0087] For power systems with more nodes, the truncated kernel is used to calculate the local density, and for power systems with fewer nodes, the Gaussian kernel is used to calculate the local density.

[0088] 2) Calculate the relative distance δ between nodes.

[0089] Relative distance refers to the minimum distance between a data point and a point with a greater density (ρ) than it. Before calculating the relative distance of data points, it is necessary to sort the local density of each data point and find the data point with the highest density and the other data points.

[0090] For the data point with the highest density, the relative distance is defined as:

[0091]

[0092] For the remaining data points, the relative distance is defined as

[0093]

[0094] 3) Eliminate isolated nodes.

[0095] An isolated node is a node whose distance to any other node is greater than the cutoff distance. Such a node is far away from all other nodes and theoretically should not belong to any region. Therefore, when clustering, such nodes should be excluded first to avoid affecting the partitioning results. According to the definition, a node that meets the following formula is an isolated node:

[0096]

[0097] 4) Find cluster centroids.

[0098] After calculating the local density and relative distance of each data point, DPC will search for the density peak points in the data set and define these data points as the cluster centers in the data set. For the data point with the highest density, since there is no point with a higher density than it, this point must be the cluster center. For other data points, it is clear that the cluster center must meet the following conditions: the local density ρ is high, that is, the distance between the data point and other points in the area is small; the relative distance δ is large, that is, the distance between the data point and other points outside the area is large. Generally, the decision value γ is used to find this type of density peak. The definition of γ is as follows:

[0099] γ i =ρ i ×δ i (20)

[0100] If we want to divide the power system into m areas, then the first m-1 nodes with the largest γ, excluding the point with the highest density, are the target nodes. These nodes and the point with the highest density together constitute the cluster center of the system.

[0101] 5) Perform clustering.

[0102] After finding all cluster centers, the cluster centers are used as the initial clustered nodes. DPC assigns the remaining data points to the cluster with a higher density and the closest distance to the node according to their relative distance.

[0103] Assume that i is an unclustered node and j is a clustered node. When i and j meet the following conditions, i will be assigned to the cluster where j is located:

[0104] ①The local density at point j is greater than the local density at point i, that is, ρ i >ρ j ;

[0105] ② Among all nodes whose local density is greater than the density of point j, the distance between point i and point j is the smallest.

[0106] System nodes form multiple tree structures originating from density peaks, each representing a region, completing the zoning process. A reactive power source installed at a node has strong short-circuit current support capabilities for nodes within that region, but weak support capabilities for nodes outside that region. When insufficient short-circuit current levels at certain nodes in the system require dynamic reactive power optimization, local optimization can be performed only within the region where the relevant node resides, based on the zoning results.

[0107] Case Analysis

[0108] This embodiment adopts the IEEE 39-node system, based on Figure 1The partitioning process shown is to partition the 29 reactive source candidate nodes in the system except the generator nodes. The specific steps are as follows:

[0109] Calculate the SCIL index between system nodes, such as Figure 3 The figure shows the calculated SCIL for nodes 1-10 relative to node 15. To verify the validity of the SCIL, it is compared with the actual change in short-circuit current at nodes 1-10 after a 300 Mvar reactive power source was installed at node 15. This comparison demonstrates that the SCIL between two nodes effectively reflects the impact of the reactive power source installed at one node on the short-circuit current at the other node.

[0110] Then, the node distance matrix is obtained according to the SCIL index, and the system is partitioned based on the density peak clustering algorithm mentioned above. It is planned to divide the system into 4 areas. After partition calculation, the final result is as follows Figure 4 And the partition results shown in Table 1.

[0111] Table 1 IEEE 39-node partitioning results

[0112]

[0113] The power system has completed the partitioning based on the short-circuit current supporting capacity between nodes. The short-circuit current supporting capacity of the reactive source between nodes in the same area is strong, while the short-circuit current supporting capacity between nodes in different areas is weak. When the short-circuit current level of some nodes in the system is insufficient and dynamic reactive source reactive power optimization configuration is required, local optimization can be performed only in the area where the relevant nodes are located based on the partitioning results.

[0114] This embodiment derives the short-circuit current support capacity limits of reactive power sources installed at different nodes and uses them as distance indicators for clustering and partitioning power systems. This approach has clear physical significance and fully considers the impact of reactive power source installation location on its short-circuit current support capacity. This allows for optimal configuration of dynamic reactive power sources, fully utilizing their short-circuit current support capacity while narrowing the optimization range. Furthermore, the DPC algorithm for clustering and partitioning eliminates the need for multiple iterative calculations, simplifies the calculation steps, and achieves high clustering accuracy, making it highly adaptable to various power systems.

[0115] Example 2

[0116] The second embodiment of the present invention introduces a reactive power partitioning system for a power system based on short-circuit current support capability.

[0117] A reactive power partitioning system for a power system based on short-circuit current support capability, comprising:

[0118] an acquisition module configured to obtain a quantitative value of the short-circuit current supporting capability of the reactive power source at different installation positions;

[0119] A calculation module is configured to calculate the short-circuit current promotion limit between nodes based on the obtained quantized value; and calculate the node distance matrix based on the obtained short-circuit current promotion limit between nodes;

[0120] The partitioning module is configured to perform clustering partitioning of the node distance matrix based on the density peak method to complete the reactive power partitioning of the power system.

[0121] The detailed steps are the same as those of the power system reactive power partitioning method based on short-circuit current support capability provided in Example 1, and will not be repeated here.

[0122] Example 3

[0123] A third embodiment of the present invention provides a computer-readable storage medium.

[0124] A computer-readable storage medium stores a program thereon, which, when executed by a processor, implements the steps of the method for reactive power partitioning of a power system based on short-circuit current supporting capability as described in the first embodiment of the present invention.

[0125] The detailed steps are the same as those of the power system reactive power partitioning method based on short-circuit current support capability provided in Example 1, and will not be repeated here.

[0126] Example 4

[0127] A fourth embodiment of the present invention provides an electronic device.

[0128] An electronic device includes a memory, a processor, and a program stored in the memory and running on the processor. When the processor executes the program, it implements the steps of the power system reactive power partitioning method based on short-circuit current support capability as described in Example 1 of the present invention.

[0129] The detailed steps are the same as those of the power system reactive power partitioning method based on short-circuit current support capability provided in Example 1, and will not be repeated here.

[0130] Example 5

[0131] A fifth embodiment of the present invention provides a computer program product.

[0132] A computer program product includes software code, wherein the program in the software code executes the steps of the method for reactive power partitioning of a power system based on short-circuit current supporting capability as described in the first embodiment of the present invention.

[0133] The detailed steps are the same as those of the power system reactive power partitioning method based on short-circuit current support capability provided in Example 1, and will not be repeated here.

[0134] The above description is merely a preferred embodiment of this embodiment and is not intended to limit this embodiment. Those skilled in the art will readily appreciate that this embodiment may be modified and varied in various ways. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this embodiment shall be within the scope of protection of this embodiment.

Claims

1. A method for reactive power partitioning of a power system based on short-circuit current support capability, characterized in that: include: The quantitative values of the short-circuit current supporting capacity of the reactive power source at different installation positions are obtained as follows: ; in, Representative Node i Node after installing reactive power source n Changes in short-circuit current, Representative Day point i Node before installing reactive power source n The short-circuit current, Z nn Representative Node n The self-impedance, Δ Z nn Represents the nodes before and after the reactive source is installed n The change of self-impedance, Representative Node i With node n The mutual impedance in the node impedance matrix, Represents the support capability of a reactive power source at a certain node in the system to another node and the capacity of the reactive power source itself. Representative Node i Self-impedance in the node impedance matrix; According to the obtained quantized value, the short-circuit current increase limit between nodes is calculated, which is: ; Wherein, SCIL is the short-circuit current increase limit between the nodes; Calculate the node distance matrix based on the obtained short-circuit current improvement limit between nodes; the node distance is the reciprocal of SCIL; The node distance matrix is clustered and partitioned based on the density peak method to complete the reactive power partitioning of the power system.

2. A method for reactive power partitioning of a power system based on short-circuit current support capability as claimed in claim 1, characterized in that: The branch addition method is used to obtain the quantitative value of the reactive source short-circuit current supporting capacity under different installation positions, that is, the strength of the node reactive source's supporting capacity for the node short-circuit current.

3. A method for reactive power partitioning of a power system based on short-circuit current support capability as claimed in claim 1, characterized in that: The inter-node short-circuit current increase limit is the upper limit of the reactive power source of one node's ability to support the short-circuit current of another node, which is only related to the mutual impedance and self-impedance between the two nodes, and represents the impact of the node topological position on its short-circuit current support capacity.

4. A method for reactive power partitioning of a power system based on short-circuit current support capability as claimed in claim 1, characterized in that: The size of the short-circuit current lifting limit between nodes is positively correlated with the short-circuit current supporting capacity between the two nodes. The inverse of the short-circuit current lifting limit between nodes is used to represent the node distance of the short-circuit current supporting capacity between nodes. The obtained node distance is normalized and represented in matrix form to obtain a node distance matrix.

5. A method for reactive power partitioning of a power system based on short-circuit current support capability as claimed in claim 1, characterized in that: The higher the inter-node short-circuit current increase limit between two nodes, the stronger the short-circuit current support capability of the other node when the reactive source is installed at one node, and the stronger the connection between the two nodes; the inter-node short-circuit current increase limit is symmetrical.

6. A method for reactive power partitioning of a power system based on short-circuit current support capability as claimed in claim 1, characterized in that: The specific process of clustering and partitioning the node distance matrix based on the density peak method is as follows: calculate the local density and node relative distance of each data point and exclude isolated nodes; find the density peak point in the data set as the cluster center point; cluster according to the cluster center point found to obtain node cluster partitioning.

7. A reactive power partitioning system for a power system based on short-circuit current support capability, characterized in that: include: An acquisition module configured to acquire the short-circuit current supporting capability of the reactive source at different installation locations Quantized values, specifically: ； in, Representative Node i Node after installing reactive power source n Changes in short-circuit current, Representative Day point i Node before installing reactive power source n The short-circuit current, Z nn Representative Node n The self-impedance, Δ Z nn Represents the nodes before and after the reactive source is installed n The change of self-impedance, Representative Node i With node n The mutual impedance in the node impedance matrix, Represents the support capability of a reactive power source at a certain node in the system to another node and the capacity of the reactive power source itself. Representative Node i Self-impedance in the node impedance matrix; The calculation module is configured to calculate the short-circuit current improvement threshold between nodes according to the obtained quantized value. Specifically: ； Wherein, SCIL is the short-circuit current increase limit between the nodes; Calculate the node distance matrix based on the obtained short-circuit current improvement limit between nodes; the node distance is the reciprocal of SCIL; The partitioning module is configured to perform clustering partitioning of the node distance matrix based on the density peak method to complete the reactive power partitioning of the power system.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method for reactive power partitioning of a power system based on short-circuit current support capability as described in any one of claims 1 to 6 are implemented.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the steps of the power system reactive power partitioning method based on short-circuit current support capability as described in any one of claims 1 to 6 are implemented.

10. A computer program product comprising software code, characterized in that The program in the software code executes the steps of the method for reactive power partitioning of a power system based on short-circuit current supporting capability as described in any one of claims 1 to 6.

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