Low-voltage power distribution network topology and parameter combined identification method and device

Abstract

The invention discloses a low-voltage power distribution network topology and parameter combined identification method and device, and belongs to the technical field of power systems, and the method comprises the steps: calculating a voltage correlation matrix based on preprocessed measurement data, recognizing feeder line groups through employing a spectral clustering algorithm, and reconstructing a topological structure of a power distribution network through combining with a maximum spanning tree algorithm; comparing and correcting the reconstructed topological structure of the power distribution network with an existing power distribution GIS system to obtain a corrected topological structure of the power distribution GIS system; based on the corrected topological structure of the power distribution GIS system, establishing an optimization model with the minimum upstream node voltage variance as a target function; solving the optimization model by adopting an optimization algorithm to obtain resistance and reactance parameters of the power distribution network line; according to the method, the voltage correlation between different nodes in the low-voltage distribution network is analyzed, and the spectral clustering algorithm and the maximum spanning tree algorithm are utilized, so that the identification of the topological structure of the low-voltage distribution network and the accurate estimation of the line parameters are realized.

Description

Technical Field

[0001] The present invention relates to a method and device for jointly identifying low-voltage distribution network topology and parameters, and belongs to the technical field of power systems. Background Art

[0002] In the power system, the distribution network is the last link connecting substations and end users. The accuracy of its topology and line parameters is crucial to the safe and stable operation of the power system. However, due to the need to frequently adjust the topology structure in the daily operation and maintenance of the distribution network to reduce network losses, balance loads and improve power supply reliability, the topological connection information in the power company's distribution network GIS system cannot be updated in real time, resulting in a large number of errors. In addition, traditional line parameter estimation methods rely on the measurement and operation of switch state quantities and cannot adapt to the changes in distributed power access and complex topology structures in modern distribution networks. With the advancement of the smart grid strategy, power system measurement technology has been continuously improved, providing new ideas and methods for the joint identification of distribution network topology and line parameters. Summary of the Invention

[0003] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method and device for jointly identifying the topology and parameters of a low-voltage distribution network. By analyzing the voltage correlation between different nodes in the low-voltage distribution network and utilizing a spectral clustering algorithm and a maximum spanning tree algorithm, the method can identify the topology of the low-voltage distribution network and accurately estimate the line parameters. The method combines voltage correlation analysis with a data-driven approach, can adapt to complex and changeable distribution network structures, and provide technical support for the construction and management of smart distribution networks.

[0004] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0005] In a first aspect, the present invention provides a method for jointly identifying low-voltage distribution network topology and parameters, comprising:

[0006] The voltage correlation matrix is ​​calculated based on the preprocessed measurement data, and the spectral clustering algorithm is used to identify feeder groups. The maximum spanning tree algorithm is then combined to reconstruct the topology of the distribution network.

[0007] Compare and revise the topology of the reconstructed distribution network with the existing distribution GIS system to obtain the topology of the revised distribution GIS system;

[0008] Based on the revised topology of the distribution GIS system, an optimization model with the minimum upstream node voltage variance as the objective function is established.

[0009] The optimization algorithm is used to solve the optimization model and obtain the resistance and reactance parameters of the distribution network lines.

[0010] Furthermore, the preprocessing of the measurement data includes:

[0011] The system collects measurement data from the low-voltage distribution network, normalizes the data, and time-aligns the data to eliminate measurement differences between devices. The measurement data includes the voltage, current, active power, and reactive power of each distribution transformer.

[0012] Furthermore, the voltage correlation matrix is ​​calculated based on the pre-processed measurement data, including:

[0013] The three-phase voltage is converted into equivalent voltage by the low-voltage outlet side voltage reduction method, and the Pearson correlation coefficient between each distribution transformer is calculated based on the equivalent voltage to form a voltage correlation matrix , as shown below: ;

[0014] in, is the voltage correlation matrix The elements in represent the distribution transformers and Voltage correlation between distribution transformers; and Respectively represent distribution transformers and A distribution transformer at a time voltage; and Respectively represent distribution transformers and The average voltage of each distribution transformer; Indicates the length of the time series dataset.

[0015] Furthermore, the spectral clustering algorithm is used to identify feeder groups and combined with the maximum spanning tree algorithm to reconstruct the topology of the distribution network, including:

[0016] Constructing the Laplacian matrix , as shown below:

[0017] ;

[0018] in, is a diagonal matrix, a diagonal matrix The diagonal elements of Indicates the The degree of a distribution transformer is as follows:

[0019] ;

[0020] in, Indicates the number of distribution transformers in the low-voltage distribution network;

[0021] Laplacian matrix Perform eigendecomposition and obtain eigenvalues and the corresponding eigenvector , before selecting smallest non-zero eigenvalue and the corresponding eigenvector , these eigenvectors are combined into a matrix ;

[0022] The matrix Each row of is considered as a node, and clustering algorithm is used to cluster these nodes. Each node will be assigned to a cluster, and each cluster corresponds to a feeder;

[0023] Based on the voltage correlation between distribution transformers Construct an undirected weighted graph and use the maximum spanning tree algorithm to generate a tree with the largest total weight from the undirected weighted graph;

[0024] According to the clustering results, the tree with the largest total weight is cut according to different feeders to obtain the topological structure on each feeder.

[0025] Furthermore, based on the revised topology of the distribution GIS system, an optimization model with the minimum upstream node voltage variance as the objective function is established, including:

[0026] Based on the revised topology of the distribution GIS system, the distribution transformer is selected as the downstream node, and the voltage of the upstream node is derived from the downstream node, as shown in the following formula:

[0027] ;

[0028] in, and Represents the voltage of the upstream and downstream nodes respectively; Z represents the impedance of the line, including resistance and reactance ; Indicates the current of the line;

[0029] Based on the voltage relationship between the upstream and downstream nodes, the voltage of the most upstream node of the circuit can be deduced as shown in the following formula:

[0030] ;

[0031] in, represents the voltage derivation function; Indicates the most upstream node; Indicates the most upstream node voltage; Indicates the most upstream node the resistance of the first downstream node; Indicates the most upstream node The resistance of the last downstream node; Indicates the most upstream node The reactance of the first downstream node; Indicates the most upstream node The reactance of the last downstream node;

[0032] Combined with the distribution transformer data at different times in the data set, different nodes in the distribution network are used as downstream nodes to deduce the upstream node The voltage matrix is ​​as follows:

[0033] ;

[0034] in, Indicates at time The first The most upstream node derived from the distribution transformer as the starting node Voltage value;

[0035] An optimization model with the minimum upstream node voltage variance as the objective function is established, as shown in the following formula:

[0036] .

[0037] Furthermore, a sequential quadratic programming algorithm is used to solve the problem, obtain the resistance and reactance parameters of each line in the low-voltage distribution network, and realize the joint identification of the topology structure and line parameters; wherein, the sequential quadratic programming algorithm includes Taylor expansion of the objective function to form a quadratic subproblem, using the quasi-Newton method to construct a second-order derivative matrix, and judging the validity of the optimal solution by satisfying the KKT condition.

[0038] In a second aspect, the present invention provides a low-voltage distribution network topology and parameter joint identification device, comprising:

[0039] The topology reconstruction module is used to calculate the voltage correlation matrix based on the preprocessed measurement data, identify feeder groups using the spectral clustering algorithm, and reconstruct the topology of the distribution network in combination with the maximum spanning tree algorithm;

[0040] The topology correction module is used to compare and correct the topology of the reconstructed distribution network with the existing distribution GIS system to obtain the topology of the corrected distribution GIS system;

[0041] The model building module is used to establish an optimization model with the minimum upstream node voltage variance as the objective function based on the revised topology of the distribution GIS system;

[0042] The result acquisition module is used to solve the optimization model using the optimization algorithm to obtain the resistance and reactance parameters of the distribution network line.

[0043] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of any of the aforementioned methods when executed by a processor.

[0044] In a fourth aspect, the present invention provides a computer device, comprising:

[0045] Memory, used to store computer programs / instructions;

[0046] A processor is configured to execute the computer program / instructions to implement the steps of any of the aforementioned methods.

[0047] In a fifth aspect, the present invention provides a computer program product, comprising a computer program / instruction, which implements the steps of any of the aforementioned methods when executed by a processor.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. The present invention provides a method for joint identification of distribution network topology and line parameters based on voltage correlation, which can simultaneously realize topology structure identification and line parameter estimation, thereby improving the efficiency and accuracy of distribution network operation and management;

[0050] 2. This invention addresses the complex topology and three-phase load imbalance issues of medium-voltage distribution networks and proposes an effective feature data extraction and reduction method, thereby improving the accuracy and reliability of topology identification.

[0051] 3. The present invention adopts a sequential quadratic programming algorithm to solve the line parameter estimation problem, which can effectively handle nonlinear optimization problems and obtain accurate line resistance and reactance values, providing an important basis for accurate modeling and optimized operation of the distribution network;

[0052] 4. The method of the present invention does not rely on the measurement and operation of switch state quantities, is applicable to the access of distributed power sources and changes in complex topology structures in modern distribution networks, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flow chart of a method for jointly identifying low-voltage distribution network topology and parameters provided by an embodiment of the present invention;

[0054] Figure 2 is a low-voltage distribution network topology diagram provided by an embodiment of the present invention;

[0055] Figure 3 This is a flow chart of a sequential quadratic programming algorithm provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0057] Example 1: This example introduces a method for jointly identifying low-voltage distribution network topology and parameters, including:

[0058] The voltage correlation matrix is ​​calculated based on the preprocessed measurement data, and the spectral clustering algorithm is used to identify feeder groups. The maximum spanning tree algorithm is then combined to reconstruct the topology of the distribution network.

[0059] Compare and revise the topology of the reconstructed distribution network with the existing distribution GIS system to obtain the topology of the revised distribution GIS system;

[0060] Based on the revised topology of the distribution GIS system, an optimization model with the minimum upstream node voltage variance as the objective function is established.

[0061] The optimization algorithm is used to solve the optimization model and obtain the resistance and reactance parameters of the distribution network lines.

[0062] The application process of the low-voltage distribution network topology and parameter joint identification method provided in this embodiment specifically involves the following steps:

[0063] Step 1: Collect measurement data such as voltage, current, active power, and reactive power from each distribution transformer in the distribution network, and perform normalization and time alignment to eliminate differences between measurement data from different devices;

[0064] The step 1 includes the following steps:

[0065] Collect measurement data such as voltage, current, active power, and reactive power from each distribution transformer in the distribution network. Data collection equipment includes smart meters, distribution terminals (DTUs), feeder terminals (FTUs), and transformer terminals (TTUs). The sampling interval is 15 minutes and the sampling time is 24 hours. This ensures that the data can reflect the operating status of the distribution network under different load conditions.

[0066] The collected measurement data is normalized, and the voltage, current and other data of each distribution transformer are linearly processed to between 0 and 1 to eliminate the differences between the measurement data of different devices. The node voltage data is normalized as follows:

[0067] (1);

[0068] At the same time, interpolation methods are used to fill in missing data, and abnormal data are eliminated or corrected through statistical analysis methods to ensure data consistency;

[0069] Step 2: The three-phase voltage is converted to the equivalent voltage under equilibrium state by using the low-voltage outlet side voltage reduction method, and the Pearson correlation coefficient or mutual information value is used as the similarity metric to obtain the voltage correlation matrix;

[0070] The second step includes the following steps:

[0071] Extract the voltage data of each distribution transformer. To address the problem of asymmetric voltage at the distribution transformer outlet caused by unbalanced three-phase load, extract the characteristic data of each distribution transformer by calculating the voltage at the low-voltage outlet side.

[0072] The voltage correlation between distribution transformers is calculated, and the Pearson correlation coefficient is used as the similarity measure to obtain the voltage correlation matrix. The specific formula is as follows:

[0073] (2);

[0074] in, represents the Pearson correlation coefficient of distribution transformers i and j, U i (t) and U j (t) represents the voltage value of distribution transformer i and j at time t, and are the average values ​​of their voltages, respectively, and T is the length of the data set;

[0075] Step three: Using spectral clustering analysis and the maximum spanning tree algorithm, the topology of the distribution network is identified based on the voltage correlation matrix. The identified topology is compared and verified with the existing distribution network GIS system to correct the erroneous topology information in the GIS system. Step three includes:

[0076] Based on the voltage correlation matrix, spectral clustering analysis is used to identify the line-to-line relationship between distribution transformers and feeders. Transformers with high voltage correlation are divided into the same group, and each group corresponds to a feeder.

[0077] Using the Pearson correlation coefficient as the similarity measure between different nodes and combining it with the maximum spanning tree algorithm, the topology of the distribution network is reconstructed. The specific steps are as follows:

[0078] Calculate the Pearson correlation coefficient between each node as the edge weight;

[0079] Initialize the topological tree, which contains all nodes but no edges. Sort the edges by weight from small to large, and select the largest edge to add to the topological tree. If no loop is formed after adding, keep the edge; otherwise, discard it.

[0080] Repeat the above steps until the topology tree contains all nodes and the topology structure of the distribution network is obtained;

[0081] Compare and verify the identified topology with the existing distribution network GIS system and correct the erroneous topology information in the GIS system;

[0082] Step 4: Based on the identified topology, and in accordance with the two scenarios of upstream nodes being unmeasurable and measurable, an optimization model for minimizing the voltage variance of upstream nodes is established. The sequential quadratic programming (SQP) algorithm is used to solve the nonlinear optimization problem and obtain the reactance and resistance between each node in the power grid network. Step 4 includes:

[0083] Based on the identified topology, a suitable mathematical model of the medium-voltage distribution network is selected, line losses are taken into account, and the voltage matrix of the upstream nodes is derived using the measurement data of each distribution transformer.

[0084] Based on the two scenarios of upstream nodes being unmeasurable and measurable, an optimization model for minimizing the upstream node voltage variance is established. The specific objective function is:

[0085] (3);

[0086] Among them, U At represents the voltage value of the upstream node A derived from the distribution transformer i as the starting node at the t-th moment, and T is the length of the data set;

[0087] The sequential quadratic programming (SQP) algorithm is used to solve the nonlinear optimization problem. The specific steps are as follows:

[0088] First, the objective function is Taylor expanded at the iteration point and converted into a quadratic programming subproblem;

[0089] Secondly, the quasi-Newton method is used to calculate the second-order derivative matrix of the objective function and solve the quadratic programming subproblem to obtain the optimal solution;

[0090] Finally, the effectiveness of the optimal solution is judged according to the KT condition, and the resistance and reactance values ​​of the power line are obtained, thereby realizing the joint identification of the topology and line parameters of the entire network.

[0091] The following describes the contents involved in the above embodiment in conjunction with a preferred embodiment.

[0092] like Figure 1-Figure 3 As shown, a method for jointly identifying low-voltage distribution network topology and line parameters includes the following steps:

[0093] Step 1: Collect measurement data such as voltage, current, active power, and reactive power from each distribution transformer in the distribution network. Data collection equipment includes smart meters, distribution terminals (DTUs), feeder terminals (FTUs), and transformer terminals (TTUs). The sampling interval is 15 minutes and the sampling time is 24 hours.

[0094] Organize the voltage and power data of the distribution transformer into a time series dataset:

[0095] ; (4);

[0096] ; (5);

[0097] in, They represent the time series data set of distribution transformer voltage and power respectively; u aαt and p aαt They represent the voltage and power of the αth distribution transformer at time t, respectively; T represents the length of the time series data set;

[0098] Step 2: Calculate the voltage correlation between the distribution transformers and use the Pearson correlation coefficient as the similarity measure to obtain the voltage correlation matrix R. The specific formula is as follows:

[0099] ; (6);

[0100] Among them, R ij is an element in the voltage correlation matrix R, which represents the voltage correlation between the i-th distribution transformer and the j-th distribution transformer; U i (t) and U j (t) represents the voltage of the i-th distribution transformer and the j-th distribution transformer at time t, respectively; and They represent the mean voltages of the i-th distribution transformer and the j-th distribution transformer respectively; T represents the length of the time series data set;

[0101] Step 3: Use the voltage correlation matrix R as the similarity matrix. Each element in the matrix represents the similarity between distribution transformer i and distribution transformer j. Construct a Laplace matrix:

[0102] (7);

[0103] Where D is a diagonal matrix, and the diagonal elements D ii is the degree of node i, that is:

[0104] (8);

[0105] Perform eigendecomposition on the Laplace matrix L and solve its eigenvalues ​​λ1,λ2,…,λ n and the corresponding eigenvectors v1,v2,…,v n , select the eigenvectors v1,v2,…,v corresponding to the first k smallest non-zero eigenvalues k , and these eigenvectors are combined into a matrix V∈R n×k ;

[0106] Treat each row of the matrix V as a point and cluster these points using traditional clustering algorithms (such as K-Means). Each node will be assigned to a cluster, and each cluster corresponds to a feeder.

[0107] The voltage correlation R of the distribution transformer ij As the weight of the edge, the larger the weight, the closer the electrical distance between the two nodes. An undirected weighted graph is constructed, and a tree with the largest total weight is generated from the graph using the maximum spanning tree algorithm.

[0108] According to the results of spectral clustering, the maximum spanning tree is cut according to different feeders to obtain the topological structure of each feeder;

[0109] Compare with the existing power distribution GIS system topology diagram, verify and correct the existing power distribution GIS system topology diagram;

[0110] Step 4: Based on the revised existing distribution GIS system topology, select the distribution transformer as the downstream node and derive the voltage relationship between the downstream node and the upstream node according to Ohm's law:

[0111] (9) ;

[0112] Among them, U 上游 and U 下游 are the voltages at the upstream and downstream nodes, respectively, Z is the impedance of the line (resistance R and reactance X), and I is the line current;

[0113] Line loss will cause voltage drop, so the influence of line loss needs to be considered in the derivation process. The line loss power can be calculated by the law of conservation of power:

[0114] (10);

[0115] (11);

[0116] Among them, P loss and Q loss are the losses of active power and reactive power respectively;

[0117] Similarly, the upstream node UA It can be expressed as the equation of resistance and reactance of each line in the distribution network:

[0118] (12);

[0119] Combining the distribution transformer data at different times in the dataset, and taking different nodes in the distribution network as downstream nodes for deduction, the voltage matrix of the upstream node can be obtained:

[0120] (13);

[0121] Among them, U Ati (t=1,2,3,…,T; i=1,2,3,…,n) represents the voltage value at point A derived from distribution transformer i as the starting node at time t, and T represents the length of the data set;

[0122] The objective function is to minimize the voltage variance of the upstream node. By minimizing the voltage variance, the resistance and reactance values ​​of each line in the distribution network can be obtained. The specific objective function is as follows:

[0123] (14);

[0124] in, , ;

[0125] The optimization model is solved iteratively by the sequential quadratic programming (SQP) algorithm, such as Figure 3 The specific steps are as follows:

[0126] First, the objective function is Taylor expanded at the iteration point and converted into a quadratic programming subproblem;

[0127] Secondly, the quasi-Newton method is used to calculate the second-order derivative matrix of the objective function and solve the quadratic programming subproblem to obtain the optimal solution;

[0128] Finally, the effectiveness of the optimal solution is judged according to the KT condition, and the optimal solution, namely the resistance and reactance values ​​of the power line, is obtained.

[0129] Embodiment 2: This embodiment provides a low-voltage distribution network topology and parameter joint identification device, including:

[0130] The topology reconstruction module is used to calculate the voltage correlation matrix based on the preprocessed measurement data, identify feeder groups using the spectral clustering algorithm, and reconstruct the topology of the distribution network in combination with the maximum spanning tree algorithm;

[0131] The topology correction module is used to compare and correct the topology of the reconstructed distribution network with the existing distribution GIS system to obtain the topology of the corrected distribution GIS system;

[0132] The model building module is used to establish an optimization model with the minimum upstream node voltage variance as the objective function based on the revised topology of the distribution GIS system;

[0133] The result acquisition module is used to solve the optimization model using the optimization algorithm to obtain the resistance and reactance parameters of the distribution network line.

[0134] The specific functional implementation of each of the above modules can be found in the relevant content of the method in Example 1 and will not be elaborated on here.

[0135] Example 3: This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of any one of the methods described in Example 1 are implemented.

[0136] Embodiment 4: This embodiment provides a computer device, including:

[0137] Memory, used to store computer programs / instructions;

[0138] A processor, configured to execute the computer program / instructions to implement the steps of any one of the methods described in Example 1.

[0139] Example 5: This embodiment provides a computer program product, including a computer program / instruction, which implements the steps of any method described in Example 1 when executed by a processor.

[0140] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

[0141] Those skilled in the art will appreciate that embodiments of the present disclosure may be provided as methods, systems, or computer program products. Thus, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure 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-ROMs, optical storage, etc.) containing computer-usable program code.

[0142] The present disclosure is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present disclosure. 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 device that provides the functions specified in a block or multiple blocks.

[0143] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0144] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0145] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present disclosure and are not intended to limit its scope of protection. Although the present disclosure has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that after reading the present disclosure, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the invention, but these changes, modifications or equivalent substitutions are all within the scope of protection of the disclosed claims to be approved.

Claims

1. A method for joint identification of low-voltage distribution network topology and parameters, characterized in that: include: The voltage correlation matrix is ​​calculated based on the preprocessed measurement data, and the spectral clustering algorithm is used to identify feeder groups. The maximum spanning tree algorithm is then combined to reconstruct the topology of the distribution network. Compare and revise the topology of the reconstructed distribution network with the existing distribution GIS system to obtain the topology of the revised distribution GIS system; Based on the revised topology of the distribution GIS system, an optimization model with the minimum upstream node voltage variance as the objective function is established. The optimization algorithm is used to solve the optimization model and obtain the resistance and reactance parameters of the distribution network lines.

2. The low-voltage distribution network topology and parameter joint identification method according to claim 1 is characterized in that: The preprocessing of measurement data includes: The system collects measurement data from the low-voltage distribution network, normalizes the data, and time-aligns the data to eliminate measurement differences between devices. The measurement data includes the voltage, current, active power, and reactive power of each distribution transformer.

3. The low-voltage distribution network topology and parameter joint identification method according to claim 1 is characterized in that: Calculate the voltage correlation matrix based on the preprocessed measurement data, including: The three-phase voltage is converted into equivalent voltage by the low-voltage outlet side voltage reduction method, and the Pearson correlation coefficient between each distribution transformer is calculated based on the equivalent voltage to form a voltage correlation matrix , as shown below: ； in, is the voltage correlation matrix The elements in represent the distribution transformers and Voltage correlation between distribution transformers; and Respectively represent distribution transformers and A distribution transformer at a time voltage; and Respectively represent distribution transformers and The average voltage of each distribution transformer; Indicates the length of the time series dataset.

4. The low-voltage distribution network topology and parameter joint identification method according to claim 3 is characterized in that: The spectral clustering algorithm is used to identify feeder groups and the maximum spanning tree algorithm is used to reconstruct the topology of the distribution network, including: Constructing the Laplacian matrix , as shown below: ； in, is a diagonal matrix, a diagonal matrix The diagonal elements of Indicates the The degree of a distribution transformer is as follows: ； in, Indicates the number of distribution transformers in the low-voltage distribution network; Laplacian matrix Perform eigendecomposition and obtain eigenvalues and the corresponding eigenvector , before selecting smallest non-zero eigenvalue and the corresponding eigenvector , these eigenvectors are combined into a matrix ; The matrix Each row of is considered as a node, and clustering algorithm is used to cluster these nodes. Each node will be assigned to a cluster, and each cluster corresponds to a feeder; Based on the voltage correlation between distribution transformers Construct an undirected weighted graph and use the maximum spanning tree algorithm to generate a tree with the largest total weight from the undirected weighted graph; According to the clustering results, the tree with the largest total weight is cut according to different feeders to obtain the topological structure on each feeder.

5. The method for joint identification of low-voltage distribution network topology and parameters according to claim 4, characterized in that: Based on the revised topology of the distribution GIS system, an optimization model with the minimum upstream node voltage variance as the objective function is established, including: Based on the revised topology of the distribution GIS system, the distribution transformer is selected as the downstream node, and the voltage of the upstream node is derived from the downstream node, as shown in the following formula: ； in, and Represents the voltage of the upstream and downstream nodes respectively; Z represents the impedance of the line, including resistance and reactance ; Indicates the current of the line; Based on the voltage relationship between the upstream and downstream nodes, the voltage of the most upstream node of the circuit can be deduced as shown in the following formula: ； in, represents the voltage derivation function; Indicates the most upstream node; Indicates the most upstream node voltage; Indicates the most upstream node the resistance of the first downstream node; Indicates the most upstream node The resistance of the last downstream node; Indicates the most upstream node The reactance of the first downstream node; Indicates the most upstream node The reactance of the last downstream node; Combined with the distribution transformer data at different times in the data set, different nodes in the distribution network are used as downstream nodes to deduce the upstream node The voltage matrix is ​​as follows: ； in, Indicates at time The first The most upstream node derived from the distribution transformer as the starting node Voltage value; An optimization model with the minimum upstream node voltage variance as the objective function is established, as shown in the following formula: 。 6. The method for joint identification of low-voltage distribution network topology and parameters according to claim 1, characterized in that: A sequential quadratic programming algorithm is used to solve the problem, obtain the resistance and reactance parameters of each line in the low-voltage distribution network, and realize the joint identification of the topology structure and line parameters. The sequential quadratic programming algorithm includes Taylor expansion of the objective function to form quadratic subproblems, constructing the second-order derivative matrix using the quasi-Newton method, and judging the validity of the optimal solution by satisfying the KKT condition.

7. A low-voltage distribution network topology and parameter joint identification device, characterized in that: include: The topology reconstruction module is used to calculate the voltage correlation matrix based on the preprocessed measurement data, identify feeder groups using the spectral clustering algorithm, and reconstruct the topology of the distribution network in combination with the maximum spanning tree algorithm; The topology correction module is used to compare and correct the topology of the reconstructed distribution network with the existing distribution GIS system to obtain the topology of the corrected distribution GIS system; The model building module is used to establish an optimization model with the minimum upstream node voltage variance as the objective function based on the revised topology of the distribution GIS system; The result acquisition module is used to solve the optimization model using the optimization algorithm to obtain the resistance and reactance parameters of the distribution network line.

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 according to any one of claims 1 to 7 are implemented.

9. An electronic device, characterized in that: include: Memory, used to store computer programs / instructions; A processor configured to execute the computer program / instructions to implement the steps of the method according to any one of claims 1 to 7.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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