A low voltage substation topology modeling method and system based on hierarchical clustering

Abstract

The present invention discloses a method and system for modeling low-voltage substation topology based on hierarchical clustering. The method includes the following steps: establishing a sensitivity relationship between voltage and current, calculating the sensitivity coefficient of voltage to current, and calculating a sensitivity matrix based on the resistance and reactance values of each line segment in the substation topology; subtracting electrical quantities under continuous time sections, converting the sensitivity relationship, and obtaining an associated impedance matrix, including an associated resistance matrix and an associated reactance matrix; inputting the associated resistance matrix or the associated reactance matrix based on an improved hierarchical clustering algorithm to generate a dendrogram; identifying hidden nodes based on the dendrogram, determining the generation order of the hidden nodes and the subordinate connection relationship between the nodes, and constructing the substation topology. The implementation of the present invention can not only accurately calculate the associated impedance between terminal nodes, but also identify all hidden nodes in the network based on the associated impedance matrix and correctly deduce the network topology, showing high accuracy and robustness, and at low cost.

Description

Technical Field

[0001] The present invention relates to the technical field of low-voltage distribution network management, and in particular to a low-voltage substation topology modeling method and system based on hierarchical clustering. Background Art

[0002] As the terminal unit of the power system, low-voltage distribution networks have complex and variable structures, significant safety hazards, and a low level of information technology. They have long operated under an open-loop management model characterized by emergency response and regular inspections. These issues have become increasingly pressing with the aging of distribution networks and the integration of new energy sources, such as distributed photovoltaics. Accurate topology and line parameter information are the cornerstones of digital distribution networks, but current distribution networks have limited perception capabilities. In actual management, topology and line parameter information is mostly derived from planning documents, equipment records, and nameplate information. This data is often missing and not updated in a timely manner.

[0003] Although a large amount of technical research and engineering practice has been devoted to the topology and line parameter identification of low-voltage substations, many challenges still exist: smart meters are the main source of measurement data, and their data needs to complete topology and parameter identification without containing phase angle information; smart meters are usually only deployed at user nodes, making the number and location of branch nodes difficult to determine; the access of distributed photovoltaics makes the power flow direction in the distribution network changeable and the voltage fluctuations intensify, which poses a severe challenge to the accuracy of the identification algorithm. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a low-voltage substation topology modeling method and system based on hierarchical clustering, which can accurately identify the low-voltage substation topology and line parameters under conditions such as no phase angle measurement information, missing branch node information, and voltage fluctuations and reverse and variable currents caused by distributed photovoltaic access.

[0005] To solve the above technical problems, as one aspect of the present invention, a low voltage substation topology modeling method based on hierarchical clustering is provided, which comprises the following steps:

[0006] Step S10, establishing a sensitivity relationship between voltage and current, calculating the sensitivity coefficient of voltage to current, and calculating a sensitivity matrix based on the resistance and reactance values of each line segment in the substation topology;

[0007] Step S11, taking the difference of the electrical quantities under the continuous time sections, converting the sensitivity relationship, and obtaining the associated impedance matrix, including the associated resistance matrix and the associated reactance matrix;

[0008] Step S12, based on the improved hierarchical clustering algorithm, input the associated resistance matrix or the associated reactance matrix to generate a dendrogram;

[0009] Step S13: Identify hidden nodes according to the tree diagram, determine the order of generating hidden nodes and the subordinate connection relationship between nodes, and construct the substation topology.

[0010] Preferably, step S10 further includes:

[0011] The angle between voltage and current is calculated by the power factor, and the sensitivity coefficient of voltage to current is obtained by combining the difference in voltage and current amplitudes.

[0012] Preferably, in step S11, it further includes:

[0013] The associated resistance matrix and the associated reactance matrix are obtained by taking the inverse of the elements in the sensitivity matrix. The associated impedance matrix is formed by taking the elements in the associated resistance matrix as the real part and the elements at the corresponding positions in the associated reactance matrix as the imaginary part.

[0014] Preferably, in step S12, based on an improved hierarchical clustering algorithm, the associated resistance matrix or the associated reactance matrix is input to generate a dendrogram, further comprising:

[0015] Input the associated resistance matrix or the associated reactance matrix as the initial input matrix;

[0016] Initialize the association resistance matrix and distance matrix, and treat each user node as an independent cluster;

[0017] The node cluster merging, association resistance matrix updating, and distance matrix updating operations are performed cyclically until all user nodes are clustered into one cluster and a dendrogram is output.

[0018] Preferably, in step S13, the hidden nodes are identified according to the tree diagram, and the generation order of the hidden nodes and the subordinate connection relationship between the nodes are determined to construct the station area topology, which further includes:

[0019] Initialize the node set;

[0020] Find the first node to be merged in the dendrogram, determine the order of generating hidden nodes and the subordinate connection relationship between nodes;

[0021] The hidden node generation and node set update operations are performed cyclically until the node set contains only one node, the connection between the distribution transformer outgoing line node and the distribution transformer node is established, the topology reconstruction is completed, and the topological structure diagram of the low-voltage distribution station area is drawn.

[0022] Accordingly, as another aspect of the present invention, a low voltage substation topology modeling system based on hierarchical clustering is provided, which includes:

[0023] A sensitivity relationship establishment module is used to establish the sensitivity relationship between voltage and current, calculate the sensitivity coefficient of voltage to current, and calculate the sensitivity matrix;

[0024] The module for obtaining the associated impedance matrix is used to make a difference in the electrical quantities under continuous time sections, transform the sensitivity relationship, and obtain the associated impedance matrix;

[0025] A hierarchical clustering processing module is used to input the correlation impedance matrix and generate a dendrogram based on an improved hierarchical clustering algorithm;

[0026] The topology modeling module is used to identify hidden nodes based on the tree diagram, determine the generation order of hidden nodes and the subordinate connection relationship between nodes, and build the substation area topology.

[0027] Preferably, in the sensitivity relationship establishment module, the angle between the voltage and the current is calculated by the power factor, and the sensitivity coefficient of the voltage to the current is calculated in combination with the difference in the voltage and current amplitudes.

[0028] Preferably, in the associated impedance matrix obtaining module, the associated resistance matrix and the associated reactance matrix are obtained by taking the inverse of the elements in the sensitivity matrix, and the elements in the associated resistance matrix are used as the real part and the elements at the corresponding positions in the associated reactance matrix are used as the imaginary part to form the associated impedance matrix.

[0029] Preferably, the hierarchical clustering processing module further comprises:

[0030] An input matrix obtaining unit is used to input a correlation resistance matrix or a correlation reactance matrix as an initial input matrix;

[0031] An initialization processing unit is used to initialize the association resistance matrix and the distance matrix, and regard each user node as an independent cluster;

[0032] The dendrogram obtaining unit is used to cyclically execute node cluster merging, association resistance matrix updating and distance matrix updating operations until all user nodes are clustered into one cluster and output a dendrogram.

[0033] Preferably, the topology modeling module further includes:

[0034] A node set initialization unit, used to initialize a node set;

[0035] The hidden node determination unit is used to find the first node to be merged in the dendrogram, determine the generation order of the hidden nodes and the subordinate connection relationship between the nodes;

[0036] The topology reconstruction unit is used to cyclically execute hidden node generation and node set update operations until the node set contains only one node, establish the connection between the distribution transformer outgoing line node and the distribution transformer node, complete the topology reconstruction, and draw the topology structure diagram of the low-voltage distribution station area.

[0037] The implementation of the embodiments of the present invention has the following beneficial effects:

[0038] This paper provides a method and system for modeling low-voltage substation topology based on hierarchical clustering. This method specifically addresses the complex characteristics of single-phase low-voltage distribution networks, such as the lack of phase angle measurement, missing branch node information, and the dynamic changes caused by distributed photovoltaic access, enabling topology and line parameter identification. This method not only accurately calculates the associated impedance between end nodes but also identifies all hidden nodes in the network based on the associated impedance matrix and correctly derives the network topology. This method demonstrates high accuracy and robustness, possessing significant engineering application value.

[0039] In this embodiment of the present invention, the solution is achieved using only the limited scalar data provided by smart meters, without the need for additional phase angle measurement information. This method not only rapidly calculates the associated impedance between end nodes, but also effectively identifies hidden nodes in the network and derives the topology, with rapid computation speed and accurate and reliable identification results.

[0040] Furthermore, this method has low requirements for the density and distribution of measurement devices. Only basic sensing equipment needs to be deployed on the transformer and user side to complete the identification task, significantly reducing investment costs. Furthermore, the identification accuracy of this method is minimally affected by different measurement error distributions, demonstrating good adaptability and stability, providing strong support for intelligent management and operation and maintenance of distribution networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, without inventive work, other drawings derived from these drawings still fall within the scope of the present invention.

[0042] Figure 1 A schematic diagram of the main process of an embodiment of a low-voltage substation topology modeling method based on hierarchical clustering provided by the present invention;

[0043] Figure 2 for Figure 1 A more detailed flowchart of step S12 and step S13;

[0044] Figure 3 Schematic diagram of the topology of a single-phase low-voltage distribution network in one embodiment of the present invention;

[0045] Figure 4 A dendrogram generated by an improved hierarchical clustering algorithm in one embodiment of the present invention;

[0046] Figure 5 The low voltage area topology modeling result in one embodiment of the present invention;

[0047] Figure 6 A schematic structural diagram of an embodiment of a low-voltage substation topology modeling system based on hierarchical clustering provided by the present invention;

[0048] Figure 7 for Figure 6 Schematic diagram of the structure of the mid-level clustering processing module;

[0049] Figure 8 for Figure 6 Schematic diagram of the structure of the topology modeling module. DETAILED DESCRIPTION

[0050] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention will be described in further detail below with reference to the accompanying drawings.

[0051] like Figure 1 FIG. 1 shows a schematic diagram of the main process of an embodiment of a low-voltage station area topology modeling method based on hierarchical clustering provided by the present invention; and FIG. Figure 2 As shown, in this embodiment, the method includes at least the following steps:

[0052] Step S10, establishing a sensitivity relationship between voltage and current, calculating the sensitivity coefficient of voltage to current, and calculating a sensitivity matrix based on the resistance and reactance values of each line segment in the substation topology;

[0053] In step S10 , the angle between the voltage and the current is calculated by the power factor, and the difference between the voltage and current amplitudes is combined to obtain a sensitivity coefficient of the voltage to the current.

[0054] Specifically, in a single-phase low-voltage distribution network, the sensitivity coefficient of voltage to current is defined as:

[0055]

[0056] Where: and The operators are used to obtain the real and imaginary parts of the phasor respectively; and The sizes are The voltage and current amplitude matrix, where is the number of loads on the phase under study, is the number of available samples; the matrix The angle between the load voltage and current is stored and can be calculated based on the power factor; and are the differences between the voltage amplitude and current phasor at different (usually continuous) time sections; and is the sensitivity matrix.

[0057] set up Representation node 、 The set of branches that are common to the complete path formed by each of them and the root node, then the matrix and The elements in can be calculated as follows:

[0058]

[0059] Where: Matrix and The elements in are the resistance and reactance values of each section of the line in the substation topology; and is the node label.

[0060] Step S11, taking the difference of the electrical quantities under the continuous time sections, converting the sensitivity relationship, and obtaining the associated impedance matrix, including the associated resistance matrix and the associated reactance matrix;

[0061] In step S11, the associated resistance matrix and the associated reactance matrix are obtained by taking the inverse of the elements in the sensitivity matrix, and the associated impedance matrix is formed by taking the elements in the associated resistance matrix as the real part and the elements at the corresponding positions in the associated reactance matrix as the imaginary part.

[0062] Specifically, by making a difference in the electrical quantities under continuous time sections, equation (1) can be transformed into:

[0063]

[0064] Where: 、 、 express The column vector of measurement values sampled at each moment; 、 、 express The column vector of the measurement values sampled at the previous moment.

[0065] Sensitivity matrix and Taking the opposite of all elements in , we can get the associated resistance matrix and the associated reactance matrix ,by The elements in are real parts, with The elements at the corresponding positions in the matrix are imaginary parts, which can form the associated impedance matrix , where the elements That is the node 、 The associated impedance between nodes is equal to 、 The sum of the impedances of the common parts of the complete paths formed by each node and the root node.

[0066] Step S12, based on the improved hierarchical clustering algorithm, input the associated resistance matrix or the associated reactance matrix to generate a dendrogram;

[0067] In step S12, it further includes:

[0068] Input the associated resistance matrix or the associated reactance matrix as the initial input matrix;

[0069] Initialize the association resistance matrix and distance matrix, and treat each user node as an independent cluster;

[0070] The node cluster merging, association resistance matrix updating, and distance matrix updating operations are performed cyclically until all user nodes are clustered into one cluster and a dendrogram is output.

[0071] Specifically, in practical examples, the following content needs to be included:

[0072] a) Input matrix construction: For The single-phase low-voltage distribution network of each user, its associated resistance matrix and the associated reactance matrix They are:

[0073]

[0074] Where: 、 Node 、 The associated resistance and associated reactance between them.

[0075] Theoretically, both the associated resistance matrix and the associated reactance matrix can be used as inputs for subsequent steps. For ease of description, only the associated resistance is taken as an example. The associated reactance can be calculated synchronously using the same method.

[0076] b) Initialize the associated resistance matrix: execute Get a symmetric matrix , That is the initial correlation resistance matrix :

[0077]

[0078] Where: For nodes 、 The initial value of the associated resistance between .

[0079] c) Distance matrix initialization: define nodes 、 The initial resistance distance between , fill the initial distance matrix with the initial resistance distance :

[0080]

[0081] d) Node cluster initialization: treat each user node as an independent cluster and obtain the initial node cluster set ,in Represented by the node Individually formed clusters.

[0082] e) Node cluster merging: Let The node cluster set before the second cycle is , are consecutive integers starting from 1. After the cycle starts, Find the two closest clusters and , merge them into a new cluster , so the current node cluster set is from Became .

[0083] f) Update of the associated resistance matrix: Let The correlation resistance matrix before the second cycle is: , No. After the cycle starts, Delete the cluster and Related rows and columns, and add a new row and column to store the new cluster and The correlation resistance between all clusters in is as follows: , ,in 、 Respectively represent Cluster after secondary cycle and The associated resistance between clusters The associated resistance between itself and itself, according to this principle, the current associated resistance matrix can be changed from becomes .

[0084] g), distance matrix update: let The distance matrix before the second cycle is , No. After the cycle starts, Delete the cluster and Related rows and columns, and add a new row and column to store the new cluster and The resistance distance between all clusters in is as follows: ,in Indicates the Cluster after secondary cycle and The resistance distance between them, according to this principle, the current distance matrix can be changed from becomes .

[0085] h) Loop through the "Merge" and "Update" operations in steps e) to g). When all user nodes are clustered into one cluster, stop the loop and output the dendrogram.

[0086] Step S13: Identify hidden nodes according to the tree diagram, determine the order of generating hidden nodes and the subordinate connection relationship between nodes, and construct the substation topology.

[0087] In step S13, it further includes:

[0088] Initialize the node set;

[0089] Find the first node to be merged in the dendrogram, determine the order of generating hidden nodes and the subordinate connection relationship between nodes;

[0090] The hidden node generation and node set update operations are performed cyclically until the node set contains only one node, the connection between the distribution transformer outgoing line node and the distribution transformer node is established, the topology reconstruction is completed, and the topological structure diagram of the low-voltage distribution station area is drawn.

[0091] Specifically, in the improved hierarchical clustering algorithm, one cycle includes one cluster merge. Each merge is accompanied by the generation of hidden nodes or the inclusion of existing nodes into generated hidden nodes. After the dendrogram is generated through steps a) to h) above, the following steps are performed to mine the information contained in the "merge" to determine the generation order of hidden nodes and the subordinate connection relationship between nodes:

[0092] i) Node set initialization: Set the initial node set ,in Indicates the User nodes.

[0093] j) Hidden node generation: Let the generation of The node set before the hidden node is , For consecutive integers starting from 1, find The first two nodes to merge are at height Merge above to produce a cluster , then locked In range, in clusters The nodes participating in these mergers are considered to be all subordinate to the hidden nodes. , and consider that the dendrogram The position is the condensation point corresponding to the highest of these mergers. It is a freely settable height difference threshold. Experiments show that it is easier to get ideal results when the value is between 0.004 and 0.005.

[0094] k), node set update: Delete the hidden nodes Those nodes and add hidden nodes , so the current node set is from Became .

[0095] l) Loop through steps i) to k) when The loop stops when only one node is included. This node is the distribution transformer outgoing line node.

[0096] m) Establish the connection between the distribution transformer outgoing line node and the distribution transformer node, and the distribution transformer outgoing line node is directly subordinate to the distribution transformer node.

[0097] n) After obtaining the generation order of hidden nodes and the subordinate connection relationship between nodes, the topology reconstruction can be performed from bottom to top to draw the topological structure diagram of the low-voltage distribution station area.

[0098] The more detailed flowchart of the above steps S12 and S13 can be found in Figure 2 shown.

[0099] The following combination Figures 3 to 5 A specific example is used to illustrate the specific application of the method provided by the present invention.

[0100] A real low-voltage distribution network in a city (such as Nanjing) is selected as a test sample to verify this method. The single-phase topology diagram of the network is shown in the figure below: Figure 3 As shown in the figure, the sample data is collected at an interval of 15 minutes and the time span is 10 days. Based on the ledger records and relevant literature, the true value of the line impedance is calculated, and the true value of the associated impedance matrix is further obtained.

[0101] First, the collected current, power, and voltage data sets are used as input to obtain the associated resistance and associated reactance between each end-user node. Then, based on the associated impedance matrix, an improved hierarchical clustering algorithm is used to generate the following Figure 4Finally, all the hidden nodes in the network are identified by combining the tree diagram and the station area topology is constructed from the bottom up. The generation order of the hidden nodes and the subordinate connection relationship between the nodes are recorded in Table 1. According to Table 1, the following can be drawn: Figure 5 The topology diagram of the substation is shown.

[0102] Table 1. The order of generating hidden nodes and the subordinate connection relationship between nodes in the error-free scenario

[0103]

[0104] To further explore the impact of different measurement error distributions on the proposed model, Gaussian distribution errors of 0.2% and 0.5% were added to the current, power, and voltage data sets, respectively, to form measurement sets, as shown in Table 2. These sets were then input into the proposed model to obtain the associated impedance matrix and deduce the substation topology.

[0105] Table 2 Test scenario settings

[0106]

[0107] The Robinson-Foulds (RF) distance is used to measure the distance between the topology identification result and the true topology. An RF distance of 0 indicates that the identification result is completely consistent with the true situation. The average impedance error is used to evaluate the accuracy of the associated impedance calculation. The average impedance error is divided into two parts: the average resistance error and the average reactance error. The average resistance error is equal to the mean absolute error (MAE) between the calculated associated resistance matrix and its true value, and the average reactance error is equal to the mean absolute error (MAE) between the calculated associated reactance matrix and its true value. Table 3 shows the accuracy of the associated impedance and topology identification under different test scenarios.

[0108] Table 3 Comparison of correlation impedance and topology identification accuracy under different test scenarios

[0109]

[0110] like Figure 6 FIG. 1 shows a schematic diagram of a structure of an embodiment of a low-voltage station area topology modeling system based on hierarchical clustering provided by the present invention; and FIG. Figures 7 and 8 As shown, in this embodiment, the low-voltage substation topology modeling system 1 based on hierarchical clustering includes at least:

[0111] A sensitivity relationship establishing module 10 is used to establish a sensitivity relationship between voltage and current, calculate the sensitivity coefficient of voltage to current, and calculate a sensitivity matrix;

[0112] The associated impedance matrix obtaining module 11 is used to make a difference in the electrical quantities under continuous time sections, convert the sensitivity relationship, and obtain the associated impedance matrix;

[0113] A hierarchical clustering processing module 12 is configured to input the associated impedance matrix and generate a dendrogram based on an improved hierarchical clustering algorithm;

[0114] The topology modeling module 13 is used to identify hidden nodes according to the tree diagram, determine the generation order of hidden nodes and the subordinate connection relationship between nodes, and build the substation area topology.

[0115] More specifically, in the sensitivity relationship establishing module 10 , the angle between the voltage and the current is calculated by the power factor, and the sensitivity coefficient of the voltage to the current is calculated in combination with the difference in the voltage and current amplitudes.

[0116] In the associated impedance matrix obtaining module 11, the associated resistance matrix and the associated reactance matrix are obtained by taking the inverse of the elements in the sensitivity matrix, and the associated impedance matrix is formed by taking the elements in the associated resistance matrix as the real part and the elements at the corresponding positions in the associated reactance matrix as the imaginary part.

[0117] like Figure 7 As shown, in a specific example, the hierarchical clustering processing module further includes:

[0118] An input matrix obtaining unit 120 is configured to input a correlation resistance matrix or a correlation reactance matrix as an initial input matrix;

[0119] An initialization processing unit 121 is used to initialize the association resistance matrix and the distance matrix, and regard each user node as an independent cluster;

[0120] The dendrogram obtaining unit 122 is configured to cyclically execute node cluster merging, association resistance matrix updating, and distance matrix updating operations until all user nodes are clustered into one cluster, and output a dendrogram.

[0121] like Figure 8 As shown, in a specific example, the topology modeling module 13 further includes:

[0122] A node set initialization unit 130 is used to initialize a node set;

[0123] The hidden node determination unit 131 is used to find the first node to be merged in the dendrogram, determine the order of generating hidden nodes and the subordinate connection relationship between nodes;

[0124] The topology reconstruction unit 132 is used to cyclically execute hidden node generation and node set update operations until the node set contains only one node, establish the connection between the distribution transformer outgoing line node and the distribution transformer node, complete the topology reconstruction, and draw the topology structure diagram of the low-voltage distribution station area.

[0125] For more details, please refer to and combine the above Figures 1 to 5 The description is not repeated here.

[0126] The implementation of the embodiments of the present invention has the following beneficial effects:

[0127] This paper provides a method and system for modeling low-voltage substation topology based on hierarchical clustering. This method specifically addresses the complex characteristics of single-phase low-voltage distribution networks, such as the lack of phase angle measurement, missing branch node information, and the dynamic changes caused by distributed photovoltaic access, enabling topology and line parameter identification. This method not only accurately calculates the associated impedance between end nodes but also identifies all hidden nodes in the network based on the associated impedance matrix and correctly derives the network topology. This method demonstrates high accuracy and robustness, possessing significant engineering application value.

[0128] In this embodiment of the present invention, the solution is achieved using only the limited scalar data provided by smart meters, without the need for additional phase angle measurement information. This method not only rapidly calculates the associated impedance between end nodes, but also effectively identifies hidden nodes in the network and derives the topology, with rapid computation speed and accurate and reliable identification results.

[0129] Furthermore, this method has low requirements for the density and distribution of measurement devices. Only basic sensing equipment needs to be deployed on the transformer and user side to complete the identification task, significantly reducing investment costs. Furthermore, the identification accuracy of this method is minimally affected by different measurement error distributions, demonstrating good adaptability and stability, providing strong support for intelligent management and operation and maintenance of distribution networks.

[0130] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, units, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0131] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A unit of functionality specified in a box or multiple boxes.

[0132] The above disclosure is only a preferred embodiment of the present invention and certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A low voltage station area topology modeling method based on hierarchical clustering, characterized in that: The following steps are involved: Step S10, establishing a sensitivity relationship between voltage and current, calculating the sensitivity coefficient of voltage to current, and calculating a sensitivity matrix based on the resistance and reactance values of each line segment in the substation topology; Step S11, taking the difference of the electrical quantities under the continuous time sections, converting the sensitivity relationship, and obtaining the associated impedance matrix, including the associated resistance matrix and the associated reactance matrix; Step S12, based on the improved hierarchical clustering algorithm, input the associated resistance matrix or the associated reactance matrix to generate a dendrogram; Step S13, identifying hidden nodes according to the tree diagram, determining the order of generating hidden nodes and the subordinate connection relationship between nodes, and constructing the station area topology; Wherein, in step S12, based on the improved hierarchical clustering algorithm, the associated resistance matrix or the associated reactance matrix is input to generate a dendrogram, further comprising: Input the associated resistance matrix or the associated reactance matrix as the initial input matrix; Initialize the association resistance matrix and distance matrix, and treat each user node as an independent cluster; The node cluster merging, association resistance matrix updating, and distance matrix updating operations are performed cyclically until all user nodes are clustered into one cluster and a dendrogram is output.

2. The method according to claim 1, characterized in that Step S10 further includes: The angle between voltage and current is calculated by the power factor, and the sensitivity coefficient of voltage to current is obtained by combining the difference in voltage and current amplitudes.

3. The method according to claim 2, characterized in that In step S11, it further includes: The associated resistance matrix and the associated reactance matrix are obtained by taking the inverse of the elements in the sensitivity matrix. The associated impedance matrix is formed by taking the elements in the associated resistance matrix as the real part and the elements at the corresponding positions in the associated reactance matrix as the imaginary part.

4. The method according to claim 3, characterized in that In step S13, the hidden nodes are identified according to the tree diagram, and the generation order of the hidden nodes and the subordinate connection relationship between the nodes are determined to construct the station area topology, which further includes: Initialize the node set; Find the first node to be merged in the dendrogram, determine the order of generating hidden nodes and the subordinate connection relationship between nodes; The hidden node generation and node set update operations are performed cyclically until the node set contains only one node, the connection between the distribution transformer outgoing line node and the distribution transformer node is established, the topology reconstruction is completed, and the topological structure diagram of the low-voltage distribution station area is drawn.

5. A low voltage substation topology modeling system based on hierarchical clustering, characterized in that: include: A sensitivity relationship establishment module is used to establish the sensitivity relationship between voltage and current, calculate the sensitivity coefficient of voltage to current, and calculate the sensitivity matrix; The module for obtaining the associated impedance matrix is used to make a difference in the electrical quantities under continuous time sections, transform the sensitivity relationship, and obtain the associated impedance matrix; A hierarchical clustering processing module is used to input the correlation impedance matrix and generate a dendrogram based on an improved hierarchical clustering algorithm; The topology modeling module is used to identify hidden nodes based on the tree diagram, determine the generation order of hidden nodes and the subordinate connection relationship between nodes, and build the substation topology; Wherein, the hierarchical clustering processing module further includes: An input matrix obtaining unit is used to input a correlation resistance matrix or a correlation reactance matrix as an initial input matrix; An initialization processing unit is used to initialize the association resistance matrix and the distance matrix, and regard each user node as an independent cluster; The dendrogram obtaining unit is used to cyclically execute node cluster merging, association resistance matrix updating and distance matrix updating operations until all user nodes are clustered into one cluster and output a dendrogram.

6. The system according to claim 5, characterized in that In the sensitivity relationship establishment module, the angle between voltage and current is calculated by the power factor, and the sensitivity coefficient of voltage to current is calculated by combining the difference in voltage and current amplitudes.

7. The system according to claim 6, characterized in that In the associated impedance matrix calculation module, the associated resistance matrix and the associated reactance matrix are obtained by taking the inverse of the elements in the sensitivity matrix. The associated impedance matrix is formed by taking the elements in the associated resistance matrix as the real part and the elements at the corresponding positions in the associated reactance matrix as the imaginary part.

8. The system according to claim 7, characterized in that The topology modeling module further includes: A node set initialization unit, used to initialize a node set; The hidden node determination unit is used to find the first node to be merged in the dendrogram, determine the generation order of the hidden nodes and the subordinate connection relationship between the nodes; The topology reconstruction unit is used to cyclically execute hidden node generation and node set update operations until the node set contains only one node, establish the connection between the distribution transformer outgoing line node and the distribution transformer node, complete the topology reconstruction, and draw the topology structure diagram of the low-voltage distribution station area.

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