Low-voltage distribution network topology identification method and system based on power characteristics

Abstract

The invention discloses a low-voltage power distribution network topology identification method and system based on power characteristics, and the method comprises the steps: carrying out the data collection of a branch box electric meter and a user electric meter of a to-be-identified transformer area in a low-voltage power distribution network at preset time intervals, and forming a voltage, current and power sequence, the user ammeter corresponds to a user node; judging whether an empty room user exists or not by using a current and power sequence, and if so, not participating in the topology identification process by the user; calculating a correlation coefficient by using the power sequence of each branch node, and judging a connection relationship of each branch node; and a 0-1 integer quadratic programming model is constructed by using the relationship between the branch section power and the user node power, the optimal solution of the 0-1 integer quadratic programming model corresponds to the user connected with the branch section, so that topology identification of the low-voltage distribution network is realized, and the branch section is a branch node downstream section.

Description

Technical Field

[0001] This invention belongs to the field of low-voltage distribution network topology identification, specifically relating to a method and system for low-voltage distribution network topology identification based on power characteristics. Background Technology

[0002] Low-voltage distribution networks are located at the end of the power grid, connecting transmission lines and electricity users, and bear the important responsibility of ensuring power quality and improving user experience. Due to a late start in development and construction, and a bias in development focus, the current storage of topology information for low-voltage distribution networks is incomplete. There are discrepancies between user information and the actual recorded topology. At the same time, there is a lack of effective means to quickly and accurately obtain the topology of low-voltage distribution networks, which affects the management level and power supply reliability of low-voltage distribution networks.

[0003] Currently, the main problems and difficulties in low-voltage distribution network topology identification include: First, low-voltage distribution networks are characterized by short lines, numerous branches, and complex structures, with flexible user-side access. Traditional methods relying on planning archives and manual inspections cannot guarantee the long-term accuracy of information and generally suffer from information lag and untimely updates. Second, existing GIS or management systems often lack unified standards and update mechanisms, making it difficult to share and integrate different data sources. Third, identification methods based on smart meter data are also limited by data quality and communication reliability; for example, packet loss, clock asynchrony, and similar load characteristics can affect identification accuracy. Furthermore, existing research is mostly conducted in idealized or small-scale scenarios, lacking systematic verification for large-scale, complex distribution areas, and the robustness and scalability of algorithms still need improvement. Finally, the fusion of multi-source heterogeneous data and privacy protection requirements also bring additional challenges to low-voltage distribution network topology identification. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for identifying the topology of a low-voltage distribution network based on power characteristics, so as to overcome the defects of the existing technology. This invention identifies the branch connection relationship and user connection relationship by combining the measurement data of smart meters with power characteristics, which can effectively improve the accuracy of the identification of the topology structure of the low-voltage distribution network. This has important practical significance for maintaining the management level and power supply reliability of the low-voltage distribution network.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: The low-voltage distribution network topology identification method based on power characteristics includes the following steps: Step 1: Collect data from the branch box meters and user meters of the distribution area to be identified in the low-voltage distribution network at preset time intervals to form voltage, current and power sequences. The branch box meters correspond to branch nodes and the user meters correspond to user nodes. Step 2: Use current and power sequences to determine if there are any vacant users. If so, the user will not participate in this topology identification process. Step 3: Calculate the correlation coefficient using the power sequences of each branch node to determine the connection relationship of each branch node; Step 4: Construct a 0-1 integer quadratic programming model using the relationship between the power of the branch section and the power of the user node. The optimal solution of the 0-1 integer quadratic programming model corresponds to the user connected by the branch section, thereby realizing the topology identification of the low-voltage distribution network. The branch section is the downstream section of the branch node.

[0006] Furthermore, in step 1, data is collected from the branch box meters and user meters of the distribution area to be identified in the low-voltage distribution network every 15 minutes, and 24 hours of data are collected to form a voltage, current and power sequence.

[0007] Furthermore, the formula for determining whether there are vacant users in step 2 using current and power sequences is as follows:

[0008] in, It is n A sequence of three-phase current RMS values ​​for each user I set It is the set current threshold for vacant room users. P n It is n The sequence of total active power RMS values ​​for each user P set It is the set active power threshold for vacant rooms. N This refers to the total number of users; When the above formula is satisfied, it indicates that the user is an empty room user, and the user will not participate in this topology identification process.

[0009] Furthermore, the step of calculating the correlation coefficient using the power sequences of each branch node to determine the connection relationship of each branch node specifically includes: Calculate the mean of the power sequence of all branch nodes, sort them in descending order, and determine the branch node with the largest mean as the root node, as shown in the following formula:

[0010] in, E ( P j ) is the first j The power sequence mean of each branch node L It is the power sequence length. P j,k It is j The branch node at the _th ... kPower values ​​at each sampling point m It is the number of branch nodes. It is the result of sorting the power sequence mean of all branch nodes in descending order. Specifically, s 1 corresponds to the mean of the power sequence of the largest branch node. s 2 corresponds to the power sequence mean of the branch node ranked second in power. s m The mean of the power sequence of the branch node with the corresponding power ranking m, s The branch node corresponding to 1 is determined as the root node. s 2. The corresponding branch node is determined. s 1's child node; The power sequence of the upper-level branch node is updated by subtracting the power sequences of all lower-level nodes from the power sequence of the upper-level branch node. This process is repeated sequentially. s i The corresponding branch node and s 1 to s i-1 The correlation coefficient of the updated power between the corresponding branch nodes r i,1 , r i,2 , … , r i,i-1 , i =3,4,…, m Select the highest correlation coefficient r i,s ,but s i The corresponding branch node is s s The child nodes of the corresponding branch node are defined by the following formula:

[0011] in, It is after the update s j The corresponding branch node is at the 1st k Power values ​​at each sampling point P a,k It is all with s j The sum of the power of the corresponding branch nodes that have connection relationships. r i,j yes s i The corresponding branch node and s j The correlation coefficient of the updated power between the corresponding branch nodes.

[0012] Furthermore, the power calculation process for the branch section is as follows: The power of a branch segment is calculated using the established branch node connection relationship. For a branch segment with branch nodes at both ends, its power is equal to the power of the first branch node minus the power of all the last branch nodes. For a branch segment at the end, its power is equal to the power of the last branch node.

[0013] Furthermore, the construction of a 0-1 integer quadratic programming model using the relationship between branch segment power and user node power specifically includes: By utilizing power conservation and introducing 0-1 variables to represent the connection relationship between user nodes and branch segments, the formula is as follows:

[0014] in, P M,k It is a section M Upper k Power values ​​at each sampling point P n,k User n The k Power values ​​at each sampling point x n It is a 0-1 variable, if the user n Connected to the section M ,but x n =1, otherwise x n =0, β It is a section M Power loss and meter measurement error N Indicates the total number of segments; A 0-1 integer quadratic programming model is constructed using the power conservation principle, as shown in the following formula:

[0015] in, f It is the objective function. β It is the error vector. P M It is the active power vector of segment M. X It is a vector of 0-1 variables. P load It is the active power matrix of all users. P n It is n Active power vector of each user.

[0016] Furthermore, the optimal solution of the 0-1 integer quadratic programming model corresponds to the users connected in each segment, thereby realizing the topology identification of the low-voltage distribution network, specifically as follows: By trying different X Find the value of . β The smallest group X The value is retrieved to determine the connection location of the user node.

[0017] A low-voltage distribution network topology identification system based on power characteristics includes: Data acquisition module: Collects data from branch box meters and user meters of the distribution area to be identified in the low-voltage distribution network at preset time intervals, and forms voltage, current and power sequences. The branch box meters correspond to branch nodes and the user meters correspond to user nodes. Determination module: Uses current and power sequences to determine whether there are vacant users. If so, the user will not participate in this topology identification process. Calculation module: Calculates correlation coefficients using the power sequences of each branch node to determine the connection relationship between each branch node; Identification module: Constructs a 0-1 integer quadratic programming model using the relationship between the power of the branch section and the power of the user node. The optimal solution of the 0-1 integer quadratic programming model corresponds to the user connected by the branch section, thereby realizing the topology identification of the low-voltage distribution network. The branch section is the downstream section of the branch node.

[0018] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the low-voltage distribution network topology identification method based on power characteristics.

[0019] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the low-voltage distribution network topology identification method based on power characteristics.

[0020] Compared with the prior art, the present invention has the following beneficial technical effects: This invention collects measurement data from branch box meters and user meters in a low-voltage distribution network to obtain branch point and user meter information. The method reflects user electricity consumption through current and power sequences, enabling rapid and accurate identification of vacant users. By calculating the correlation coefficient of power sequences at each branch node, the method accurately determines the connection relationship of each branch node. Furthermore, by constructing a 0-1 integer quadratic programming model based on the relationship between branch section power and user power, the method reliably identifies the connection relationship between users and sections. This invention requires no additional measuring equipment, only analyzing electrical quantity data measured by smart meters, making it economical. It also involves less computation, is easy to understand, and has strong real-time performance, showing promising engineering application prospects. Attached Figure Description

[0021] The accompanying drawings are provided to further understand the invention and constitute a part of this invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0022] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a flowchart for identifying vacant room users; Figure 3 This is a flowchart for identifying branch connection relationships; Figure 4 This is a flowchart for identifying user connection relationships; Figure 5 This is a schematic diagram of a low-voltage distribution network topology. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] Example 1 With the increasing intelligence level of low-voltage distribution networks, various intelligent monitoring terminals can acquire power consumption information from different distribution areas for analysis, providing a data foundation for low-voltage distribution network topology identification. Furthermore, the topology of low-voltage distribution networks has become indispensable basic information for power operations such as load forecasting and user management. Therefore, this invention utilizes measurement data from smart meters and combines it with power characteristics to identify branch connection relationships and user connection relationships, aiming to improve the accuracy of low-voltage distribution network topology identification and provide crucial data support for applications such as low-voltage distribution network fault location, line loss management, and electricity theft detection.

[0026] Specifically, the present invention provides a low-voltage distribution network topology identification method based on power characteristics, comprising the following steps: Step 1: Collect data from the branch box meters and user meters of the distribution area to be identified in the low-voltage distribution network at preset time intervals to form voltage, current and power sequences. The branch box meters correspond to branch nodes and the user meters correspond to user nodes. Step 2: Use current and power sequences to determine if there are any vacant users. If so, the user will not participate in this topology identification process. Step 3: Calculate the correlation coefficient using the power sequences of each branch node to determine the connection relationship of each branch node; Step 4: Construct a 0-1 integer quadratic programming model using the relationship between the power of the branch section and the power of the user node. The optimal solution of the 0-1 integer quadratic programming model corresponds to the user connected by the branch section, thereby realizing the topology identification of the low-voltage distribution network. The branch section is the downstream section of the branch node.

[0027] Example 2 like Figure 1 As shown, this invention is a low-voltage distribution network topology identification method based on power characteristics, specifically including the following steps: I. Data Acquisition: Data is collected from the branch box meters and user meters of the low-voltage distribution network to be identified every 15 minutes. Data for 24 hours is collected to form voltage, current and power sequences.

[0028] II. Identification of vacant room users, based on, for example... Figure 2 The flowchart for identifying vacant rooms shown below uses the following formula to determine the presence of vacant rooms based on current and power sequences:

[0029] in, It is n A sequence of three-phase current RMS values ​​for each user I set It is the set current threshold for vacant room users. P n It isn The sequence of total active power RMS values ​​for each user P set It is the set active power threshold for vacant rooms. N This is the total number of users.

[0030] When there is an empty room, that user will not participate in the topology identification process.

[0031] III. Identification of branch connection relationships, based on, for example... Figure 3 The flowchart shown illustrates the branch connection identification process. It calculates the correlation coefficient using the power sequences of each branch node to determine the connection relationship between them. Specifically, it first calculates the mean of the power sequences of all branch nodes and sorts them from largest to smallest. The branch node with the largest mean is determined as the root node. Then, it updates the power sequence of the upper-level branch node by subtracting the power sequences of all lower-level nodes from the power sequence of the upper-level branch node. The correlation coefficient between the updated power sequences of the branch nodes is calculated sequentially. Finally, it determines that the two nodes with the largest correlation coefficient are connected.

[0032] Calculate the mean of the power sequence of all branch nodes, sort them in descending order, and determine the branch node with the largest mean as the root node, as shown in the following formula:

[0033] in, E ( P j ) is the first j The power sequence mean of each branch node L It is the power sequence length. P j,k It is j The branch node at the _th ... k Power values ​​at each sampling point m It is the number of branch nodes. It is the result of sorting the power sequence mean of all branch nodes in descending order. Specifically, s 1 corresponds to the mean of the power sequence of the largest branch node. s 2 corresponds to the power sequence mean of the branch node ranked second in power. s m The mean of the power sequence of the branch node with the corresponding power ranking m, s The branch node corresponding to 1 is determined as the root node. s 2. The corresponding branch node is determined. s The child node of 1.

[0034] The power sequence of the upper-level branch node is updated by subtracting the power sequences of all lower-level nodes from the power sequence of the upper-level branch node. This process is repeated sequentially. si ( i =3,4,…, m The corresponding branch node and s 1 to s i-1 The correlation coefficient of the updated power between the corresponding branch nodes r i,1 , r i,2 , … , r i,i-1 Select the highest correlation coefficient r i,s ,but s i The corresponding branch node is s s The child nodes of the corresponding branch node are defined by the following formula:

[0035] in, It is after the update s j The corresponding branch node is at the 1st k Power values ​​at each sampling point P a,k It is all with s j The sum of the power of the corresponding branch nodes that have connection relationships. r i,j yes s i The corresponding branch node and s j The correlation coefficient of the updated power between the corresponding branch nodes.

[0036] IV. User connection relationship identification, based on, for example... Figure 4 The flowchart shown illustrates the user connection identification process. A 0-1 integer quadratic programming model is constructed using the relationship between branch segment power and user node power. The optimal solution of the model corresponds to the user connected to the segment.

[0037] The power of a branch segment is calculated using the established branch node connection relationship. For a branch segment with branch nodes at both ends, its segment power is equal to the power of the first branch node minus the power of all the last branch nodes. For a branch segment at the end, its segment power is equal to the power of the last branch node.

[0038] By utilizing power conservation and introducing 0-1 variables to represent the connection relationship between user nodes and branch segments, the formula is as follows:

[0039] in, PM,k It is a section M Upper k Power values ​​at each sampling point P n,k User n The k Power values ​​at each sampling point x n It is a 0-1 variable, if the user n Connected to the section M ,but x n =1, otherwise x n =0, β It is a section M Power loss and meter measurement error.

[0040] A 0-1 integer quadratic programming model is constructed using the power conservation principle, as shown in the following formula:

[0041] in, f It is the objective function. β It is the error vector. P M It is the active power vector of segment M. X It is a vector of 0-1 variables. P load It is the active power matrix of all users. P n It is n The active power vector of each user, and the parameters in each vector correspond to the parameters of each node (error, active power).

[0042] By trying different X Find the value of . β The smallest group X The user node connection location can be determined by retrieving the value.

[0043] Example 3 A schematic diagram of the low-voltage distribution network topology is shown below. Figure 5 As shown, data is collected from the branch box meters and user meters of the low-voltage distribution network to be identified every 15 minutes, and 24 hours of data are collected to form a voltage, current, and power sequence.

[0044] Determining the presence of vacant units using current and power sequences, including the determination of... L 19 and L 20 Users whose rooms are vacant will not participate in this topology identification process.

[0045] The correlation coefficient is calculated using the power sequences of each branch node to determine the connection relationship between them. The mean power sequence of all branch nodes is calculated, and the mean power result is [5.4974, 4.1060, 0.6168, 1.8645, 0.4585]. The branch node sequences are then sorted in descending order, and the reordered sequence is […]. C 1, C 2, C 4, C 3, C 5).

[0046] The power correlation coefficient is used to identify the connection relationship of the rearranged branch nodes. The power correlation coefficient between each branch node is shown in Table 1. According to the results of this table, the connection relationship between each branch node can be obtained as follows: Node 2 is connected to Node 1, and Nodes 3, 4, and 5 are all connected to Node 2.

[0047] Table 1 Power correlation coefficients between branch nodes

[0048] By calculating the branch section power using the established branch node connection relationship, the low-voltage distribution network can be divided into 5 sections, where nodes 1-2 are section 1, nodes 2-3, 4, and 5 are section 2, nodes 3 onwards are section 3, nodes 4 onwards are section 4, and nodes 5 onwards are section 5.

[0049] A 0-1 integer quadratic programming model was constructed and solved using the power conservation relationship. The results are shown in Table 2. According to the results in the table, segment 1 contains users 1, 2, 3, 4, 5, and 18; segment 2 contains users 6, 7, 8, and 9; segment 3 contains users 12 and 13; segment 4 contains users 14, 15, 16, and 17; and segment 5 contains users 10 and 11. This enables the identification of the connection relationship between users and segments within this region.

[0050] Analysis of the solution results shows that this invention can accurately identify vacant users in low-voltage distribution networks, effectively identify the connection relationships between branch nodes in the network, and reliably identify the connection relationships between users and sections. This invention can effectively improve the accuracy of low-voltage distribution network topology identification, and has significant practical implications for maintaining the management level and power supply reliability of low-voltage distribution networks.

[0051] Table 2. Results of User-Segment Connection Relationship Identification

[0052] Example 4 A low-voltage distribution network topology identification system based on power characteristics includes: Data acquisition module: Collects data from branch box meters and user meters of the distribution area to be identified in the low-voltage distribution network at preset time intervals, and forms voltage, current and power sequences. The branch box meters correspond to branch nodes and the user meters correspond to user nodes. Determination module: Uses current and power sequences to determine whether there are vacant users. If so, the user will not participate in this topology identification process. Calculation module: Calculates correlation coefficients using the power sequences of each branch node to determine the connection relationship between each branch node; Identification module: Constructs a 0-1 integer quadratic programming model using the relationship between the power of the branch section and the power of the user node. The optimal solution of the 0-1 integer quadratic programming model corresponds to the user connected by the branch section, thereby realizing the topology identification of the low-voltage distribution network. The branch section is the downstream section of the branch node.

[0053] Example 5 A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the low-voltage distribution network topology identification method based on power characteristics.

[0054] Example 6 A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the low-voltage distribution network topology identification method based on power characteristics.

[0055] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present invention, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the scope of protection of the pending claims of the invention.

Claims

1. A method for identifying the topology of a low-voltage distribution network based on power characteristics, characterized in that, The steps include: Step 1: Collect data from the branch box meters and user meters of the distribution area to be identified in the low-voltage distribution network at preset time intervals to form voltage, current and power sequences. The branch box meters correspond to branch nodes and the user meters correspond to user nodes. Step 2: Use current and power sequences to determine if there are any vacant users. If so, the user will not participate in this topology identification process. Step 3: Calculate the correlation coefficient using the power sequences of each branch node to determine the connection relationship of each branch node; Step 4: Construct a 0-1 integer quadratic programming model using the relationship between the power of the branch section and the power of the user node. The optimal solution of the 0-1 integer quadratic programming model corresponds to the user connected by the branch section, thereby realizing the topology identification of the low-voltage distribution network. The branch section is the downstream section of the branch node.

2. The low-voltage distribution network topology identification method based on power characteristics according to claim 1, characterized in that, In step 1, data is collected every 15 minutes from the branch box meters and user meters of the distribution area to be identified in the low-voltage distribution network. Data for 24 hours is collected to form a voltage, current and power sequence.

3. The low-voltage distribution network topology identification method based on power characteristics according to claim 1, characterized in that, The formula for determining whether there are vacant units using current and power sequences in step 2 is as follows: in, It is n A sequence of three-phase current RMS values ​​for each user I set It is the set current threshold for vacant room users. P n It is n The sequence of total active power RMS values ​​for each user P set It is the set active power threshold for vacant rooms. N This refers to the total number of users; When the above formula is satisfied, it indicates that the user is an empty room user, and the user will not participate in this topology identification process.

4. The low-voltage distribution network topology identification method based on power characteristics according to claim 1, characterized in that, The step of calculating the correlation coefficient using the power sequences of each branch node to determine the connection relationship of each branch node specifically includes: Calculate the mean of the power sequence of all branch nodes, sort them in descending order, and determine the branch node with the largest mean as the root node, as shown in the following formula: in, E ( P j ) is the first j The power sequence mean of each branch node L It is the power sequence length. P j,k It is j The branch node at the _th ... k Power values ​​at each sampling point m It is the number of branch nodes. It is the result of sorting the power sequence mean of all branch nodes in descending order. Specifically, σ 1 corresponds to the mean of the power sequence of the largest branch node. σ 2 corresponds to the power sequence mean of the branch node ranked second in power. σ m The mean of the power sequence of the branch node with the corresponding power ranking m, σ The branch node corresponding to 1 is determined as the root node. σ 2. The corresponding branch node is determined. σ 1's child node; The power sequence of the upper-level branch node is updated by subtracting the power sequences of all lower-level nodes from the power sequence of the upper-level branch node. This process is repeated sequentially. σ i The corresponding branch node and σ 1 to σ i-1 The correlation coefficient of the updated power between the corresponding branch nodes ρ i,1 , ρ i,2 , … , ρ i,i-1 , i =3,4,…, m Select the highest correlation coefficient ρ i,s ,but σ i The corresponding branch node is σ s The child nodes of the corresponding branch node are defined by the following formula: in, It is after the update σ j The corresponding branch node is at the 1st k Power values ​​at each sampling point P a,k It is all with σ j The sum of the power of the corresponding branch nodes that have connection relationships. ρ i,j yes σ i The corresponding branch node and σ j The correlation coefficient of the updated power between the corresponding branch nodes.

5. The low-voltage distribution network topology identification method based on power characteristics according to claim 4, characterized in that, The power calculation process for the branch section is as follows: The power of a branch segment is calculated using the established branch node connection relationship. For a branch segment with branch nodes at both ends, its power is equal to the power of the first branch node minus the power of all the last branch nodes. For a branch segment at the end, its power is equal to the power of the last branch node.

6. The low-voltage distribution network topology identification method based on power characteristics according to claim 5, characterized in that, The construction of a 0-1 integer quadratic programming model using the relationship between branch segment power and user node power specifically includes: By utilizing power conservation and introducing 0-1 variables to represent the connection relationship between user nodes and branch segments, the formula is as follows: in, P M,k It is a section M Upper k Power values ​​at each sampling point P n,k User n The k Power values ​​at each sampling point x n It is a 0-1 variable, if the user n Connected to the section M ,but x n =1, otherwise x n =0, β It is a section M Power loss and meter measurement error N Indicates the total number of segments; A 0-1 integer quadratic programming model is constructed using the power conservation principle, as shown in the following formula: in, f It is the objective function. β It is the error vector. P M It is the active power vector of segment M. X It is a vector of 0-1 variables. P load It is the active power matrix of all users. P n It is n Active power vector of each user.

7. The low-voltage distribution network topology identification method based on power characteristics according to claim 6, characterized in that, The optimal solution of the 0-1 integer quadratic programming model corresponds to the users connected in each segment, thereby realizing the topology identification of the low-voltage distribution network, specifically: By trying different X Find the value of . β The smallest group X The value is retrieved to determine the connection location of the user node.

8. A low-voltage distribution network topology identification system based on power characteristics, characterized in that, include: Data acquisition module: Collects data from branch box meters and user meters of the distribution area to be identified in the low-voltage distribution network at preset time intervals, and forms voltage, current and power sequences. The branch box meters correspond to branch nodes and the user meters correspond to user nodes. Determination module: Uses current and power sequences to determine whether there are vacant users. If so, the user will not participate in this topology identification process. Calculation module: Calculates correlation coefficients using the power sequences of each branch node to determine the connection relationship between each branch node; Identification module: Constructs a 0-1 integer quadratic programming model using the relationship between the power of the branch section and the power of the user node. The optimal solution of the 0-1 integer quadratic programming model corresponds to the user connected by the branch section, thereby realizing the topology identification of the low-voltage distribution network. The branch section is the downstream section of the branch node.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the low-voltage distribution network topology identification method based on power characteristics as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the low-voltage distribution network topology identification method based on power characteristics as described in any one of claims 1 to 7.

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