A method and apparatus for radial topology identification in low-voltage distribution networks

Abstract

This invention provides a method and apparatus for radial topology identification of low-voltage distribution networks, applicable to low-voltage distribution substations with a fully deployed electricity consumption information acquisition system operating in open-loop mode. The method first synchronously collects voltage consumption data from substation nodes, performs data standardization, calculates the mean and variance of each node voltage dataset, and constructs its Gaussian probability density function model; forms a structure factor matrix containing distribution network topology information; constructs a minimum spanning tree of the corresponding connected weighted graph, and outputs the radial topology structure of the low-voltage distribution network. The apparatus includes modules for implementing the above method, a terminal, and a readable storage medium for storing computer programs. This invention quantifies the similarity between node voltages through the calculation of JS divergence, which can improve the accuracy of radial topology identification of low-voltage distribution networks while enabling real-time and rapid analysis of the topology structure, further providing effective support for detecting topology changes and precise load control.

Description

Technical Field

[0001] This invention belongs to the field of smart grid power distribution and utilization, and specifically relates to a method and device for radial topology identification of low-voltage distribution networks. Background Technology

[0002] Low-voltage distribution networks, as a crucial component of the power system, are characterized by a large number of components, flexible line connections, and complex mathematical modeling. Currently, new electrical equipment such as distributed power sources and mobile charging stations are continuously being integrated into the distribution network. Over time, the topology of distribution areas undergoes complex and diverse changes. Manually created distribution area topology files often lack real-time updates, are costly to maintain, and suffer from errors such as incorrect equipment spatial mapping, missing data records for feeder length, material, wire diameter, and model, and users connecting to incorrect transformers or towers. These factors lead to low distribution network management efficiency, transformer overload, and ultimately threaten the safety of employees and public property. Therefore, intelligent distribution automation systems require the establishment of an online distribution area topology identification platform. This platform should ensure accurate identification while dynamically updating the low-voltage distribution network topology, providing support for the safe and stable operation and rapid dispatch control of the distribution network.

[0003] Data-driven low-voltage distribution network topology identification algorithms can achieve real-time identification and synchronous recording of transformer substation topology, thus gradually becoming a research hotspot for scholars both domestically and internationally. Currently, intelligent electricity consumption information acquisition systems are widely deployed in distribution networks, achieving high-density synchronous measurement and extraction of electrical data from distribution network nodes, providing a foundation for establishing more refined distribution network topology models and selecting more effective mathematical analysis methods. However, the optimization problem of identifying line switch states using a generalized state estimator often requires a nonlinear programming solver to find the optimal solution. Ensuring the observability of the state estimator requires a large amount of data, and the solution time is long, making it difficult to guarantee real-time and rapid analysis of transformer substation topology. While linear programming solvers based on DC power flow models improve the execution speed of topology identification algorithms, they require the line reactance to be much greater than the resistance, which is difficult to meet in actual low-voltage distribution networks, thus failing to guarantee the accuracy of topology identification results. Summary of the Invention

[0004] The purpose of this invention is to provide a method and apparatus for radial topology identification of low-voltage distribution networks, which can improve the accuracy of radial topology identification of low-voltage distribution networks, realize real-time and rapid analysis of topology structure, and further provide effective support for detecting topology changes and precise load control.

[0005] To achieve the above objectives, this invention discloses a radial topology identification method for low-voltage distribution networks based on probability distribution similarity assessment, comprising the following steps:

[0006] S1: Synchronously collect voltage data from transformer substation nodes to form dataset V;

[0007] S2: Standardize the dataset V to form V s ;

[0008] S3: Calculate the mean and variance of the voltage dataset for each node, and construct its Gaussian probability density function model;

[0009] S4: Calculate the JS divergence between the node probability distribution curves to form a structure factor matrix containing distribution network topology information;

[0010] S5: Construct the minimum spanning tree of the corresponding connected weighted graph and output the radial topology of the low-voltage distribution network.

[0011] Furthermore, the specific steps for synchronously collecting voltage data of transformer substation nodes in S1 to form dataset V are as follows:

[0012] S1.1: Count the subordinate meter box file numbers of all transformer nodes, branch box nodes, and user meter box nodes in the distribution area to be identified, and obtain the number of nodes N and the node set D in the distribution area to be identified;

[0013] S1.2: Collect voltage data of node i (i = 1, 2, ..., N) within the sampling period T, forming an n × 1 dimensional matrix V. i The formula for calculating n is as follows:

[0014]

[0015] Where f is the sampling frequency, f is greater than or equal to 6 sampling points per hour, and T is greater than or equal to 12 hours;

[0016] S1.3: Collect the node voltage matrix of all nodes in the transformer area to form a dataset V = [V1, V2, ... V N ].

[0017] Furthermore, in step S2, the dataset V is standardized to form V0. s The specific steps are as follows:

[0018] S2.1: To eliminate the influence of dimensions and ensure the accuracy of the analysis results, the voltage matrix V of each node is... i The standardization calculation formula is as follows:

[0019]

[0020] Among them, V is This is the standardized node voltage matrix, where mean(.) is the mean operator and sd(.) is the standard deviation operator.

[0021] S2.2: Aggregate the standardized node voltage matrices of all nodes in the distribution transformer area to form a data set V s = [V 1s , V 2s ,..., V Ns .

[0022] Furthermore, the specific steps for calculating the mean and variance of each node voltage data set and constructing its Gaussian probability density function model in S3 are as follows:

[0023] S3.1: Calculate the mean μ i and variance σ i of the voltage matrix of node i. The calculation formula is as follows:

[0024]

[0025]

[0026] where V isk represents the k-th standardized node voltage matrix of node i;

[0027] S3.2: Assume that the voltage curve of node i follows a normal distribution with a mean of μ i and a variance of σ i . The calculation formula of its Gaussian probability density function is as follows:

[0028]

[0029] Furthermore, the specific steps for calculating the JS divergence between node probability distribution curves and forming a structure factor matrix containing the distribution network topology information in S4 are as follows:

[0030] S4.1: Initialize the structure factor matrix Г as an N×N upper triangular zero matrix;

[0031] S4.2: Calculate the JS divergence Г ij between all node pairs <i, j> (i, j = 1, 2,..., N, and i < j) in the to-be-identified distribution transformer area. The calculation formula is as follows:

[0032]

[0033] where P(V is ), P(V js ) are the Gaussian probability density functions of node i and node j respectively;

[0034] S4.3: Put the calculation results of the JS divergence between all node pairs into the structure factor matrix Г.

[0035] Furthermore, the specific steps in S5 for constructing the minimum spanning tree of the corresponding connected weighted graph and outputting the radial topology of the low-voltage distribution network are as follows:

[0036] S5.1: Construct a connected weighted graph δ = (D, Г) from the set of nodes D and the structural factor matrix Г in the area to be identified;

[0037] S5.2: Construct the minimum spanning tree using Prim's algorithm and output the set B containing all edges of the minimum spanning tree;

[0038] S5.3: Based on set B, obtain the connection relationship of the nodes in the distribution area to be identified, and output the radial topology of the low-voltage distribution network.

[0039] Furthermore, the specific steps of S5.2 are as follows:

[0040] S5.2.1: Set two sets A and B, where set A stores the vertices in the minimum spanning tree of δ, and set B stores the edges in the minimum spanning tree of δ;

[0041] S5.2.2: Let the initial value of set A be A = {d1}, and the initial value of set B be an empty set, where d1 represents the root node in the distribution network;

[0042] S5.2.3: Select the edge with the minimum weight Г from all edges a∈A, d∈DA. ad Given an edge ad, add vertex d to set A and add edge ad to set B;

[0043] S5.2.4: Repeat S5.2.3 continuously until A = D, at which point the minimum spanning tree is constructed.

[0044] The present invention also proposes a radial topology identification device for low-voltage distribution networks, comprising:

[0045] Data acquisition module: Synchronously collects voltage data from transformer substation nodes to form dataset V;

[0046] Standardization module: Performs standardization processing on dataset V to form Vstandardization s ;

[0047] Function building module: Calculates the mean and variance of the voltage dataset for each node and constructs its Gaussian probability density function model;

[0048] The structure factor matrix construction module calculates the JS divergence between the node probability distribution curves to form a structure factor matrix containing distribution network topology information.

[0049] Topology Output Module: Constructs the minimum spanning tree of the corresponding connected weighted graph and outputs the radial topology of the low-voltage distribution network.

[0050] Furthermore, the present invention also proposes a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method of the present invention according to claims 1-7.

[0051] Furthermore, the present invention also proposes a computer-readable storage medium having a computer program stored thereon, characterized in that the program, when executed by a processor, implements the steps of the method described in the present invention.

[0052] Compared with the prior art, the present invention has the following significant advantages: The present invention identifies the transformer area topology by evaluating the similarity of the node voltage probability distribution curves, without relying on mathematical models with strict constraints and approximate optimization; at the same time, it quantifies the similarity between node voltages by calculating JS divergence, which can improve the accuracy of radial topology identification of low-voltage distribution networks and realize real-time and rapid analysis of topology structure, further providing effective support for detecting topology changes and precise load control. Attached Figure Description

[0053] Figure 1 This is a general flowchart of the method described in this invention; Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0055] like Figure 1 As shown, this invention proposes a radial topology identification method for low-voltage distribution networks based on probability distribution similarity evaluation, comprising the following steps:

[0056] (1) Synchronously collect voltage data of transformer substation nodes to form dataset V; the specific steps include the following:

[0057] (1.1) Count the subordinate meter box file numbers of all transformer nodes, branch box nodes and user meter box nodes in the area to be identified, and obtain the number of nodes N and the node set D in the area to be identified.

[0058] (1.2) Collect voltage data of node i (i = 1, 2, ..., N) within the sampling period T to form an n × 1 dimensional matrix V. i The formula for calculating n is as follows:

[0059]

[0060] Where f is the sampling frequency. To ensure the reliability of the identification results, f must be greater than or equal to 6 sampling points per hour, and T must be greater than or equal to 12 hours.

[0061] (1.3) Collect the node voltage matrix of all nodes in the transformer area to form a dataset V = [V1, V2, ... V N ].

[0062] (2) Standardize the dataset V to form V s The specific steps include the following:

[0063] (2.1) To eliminate the influence of dimensions and ensure the accuracy of the analysis results, the voltage matrix V of each node is... i The standardization calculation formula is as follows:

[0064]

[0065] Among them, V is This is the standardized node voltage matrix, where mean(.) is the mean operator and sd(.) is the standard deviation operator.

[0066] (2.2) Collect the standardized node voltage matrix of all nodes in the transformer area to form the dataset V. s =[V 1s V 2s ,...,V Ns ].

[0067] (3) Calculate the mean and variance of the voltage dataset for each node, and construct its Gaussian probability density function model; the specific steps include the following:

[0068] (3.1) Calculate the mean μ of the voltage matrix at node i. i and variance σ i The calculation formula is as follows:

[0069]

[0070]

[0071] Among them, V isk This represents the k-th normalized node voltage matrix of node i;

[0072] (3.2) Assume that the voltage curve of node i follows a mean of μ i The variance is σ i The Gaussian probability density function of the normal distribution is calculated as follows:

[0073]

[0074] (4) Calculate the Jensen-Shannon divergence between the node probability distribution curves to form a structure factor matrix containing the topological information of the distribution network. The specific steps are as follows:

[0075] (4.1) Initialize the structure factor matrix Г as an upper triangular zero matrix of dimension N×N.

[0076] (4.2) Calculate the Jensen-Shannon divergence Г between all node pairs <i, j> (i, j = 1, 2,..., N, and i < j) in the to-be-identified area. ij The calculation formula is as follows:

[0077]

[0078] where P(V is ), P(V js ) are the Gaussian probability density functions of node i and node j respectively.

[0079] (4.3) Put the calculation results of the Jensen-Shannon divergence between all node pairs into the structure factor matrix Г.

[0080] (5) Construct the minimum spanning tree of the corresponding connected weighted graph and output the radial topological structure of the low-voltage distribution network. The specific steps are as follows:

[0081] (5.1) Construct a connected weighted graph δ = (D, Г) from the node set D and the structure factor matrix Г in the to-be-identified area.

[0082] (5.2) Use the prim algorithm to construct the minimum spanning tree and output the set B containing all the edges of the minimum spanning tree.

[0083] The specific steps are as follows:

[0084] (5.2.1) Set two sets A and B, where set A stores the vertices in the minimum spanning tree of δ, and set B stores the edges in the minimum spanning tree of δ.

[0085] (5.2.2) Let the initial value of set A be A = {d1}, the initial value of set B be an empty set, and d1 represents the root node in the distribution network.

[0086] (5.2.3) Select the edge ad with the minimum weight Г from all edges where a ∈ A and d ∈ D - A, add the vertex d to set A, and add the edge ad to set B. ad

[0087] (5.2.4) Continuously repeat step (5.2.3) until A = D, and the minimum spanning tree construction is completed.

[0088] (5.3) Based on set B, obtain the connection relationship of the nodes in the distribution area to be identified, and output the radial topology of the low-voltage distribution network.

[0089] The present invention also proposes a radial topology identification device for low-voltage distribution networks, comprising:

[0090] Data acquisition module: Synchronously collects voltage data from transformer substation nodes to form dataset V.

[0091] Standardization module: Performs standardization processing on dataset V to form Vstandardization s .

[0092] Function building module: Calculates the mean and variance of the voltage dataset for each node and constructs its Gaussian probability density function model.

[0093] The structure factor matrix construction module calculates the JS divergence between node probability distribution curves to form a structure factor matrix containing distribution network topology information. Specific steps include the following:

[0094] Topology Output Module: Constructs the minimum spanning tree of the corresponding connected weighted graph and outputs the radial topology of the low-voltage distribution network.

[0095] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0096] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0097] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0098] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions to implement various aspects of this disclosure.

[0099] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0100] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0101] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0102] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0103] 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 it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for radial topology identification in a low-voltage distribution network, characterized in that... Includes the following steps: S1: Synchronously collect voltage data from transformer substation nodes to form dataset V; S2: Standardize the dataset V to form V s ; S3: Calculate the mean and variance of the voltage dataset for each node, and construct its Gaussian probability density function model; S4: Calculate the JS divergence between the node probability distribution curves to form a structure factor matrix containing distribution network topology information; the specific steps are as follows: S4.1: Initialize the structural factor matrix Г as an N×N dimensional upper triangular zero matrix; S4.2: Calculate the JS divergence Γ between all node pairs <i, j> (i, j = 1, 2,..., N, and i < j) in the to-be-recognized power distribution area ij , and the calculation formula is as follows: Among them, P(V is ), P(V js V represents the Gaussian probability density functions for nodes i and j, respectively. is V js The standardized node voltage matrix; S4.3: Put the JS divergence calculation results between all node pairs into the structure factor matrix Г; S5: Construct the minimum spanning tree of the corresponding connected weighted graph and output the radial topology of the low-voltage distribution network. The specific steps are as follows: S5.1: Construct a connected weighted graph δ = (D, Г) from the set of nodes D and the structural factor matrix Г in the area to be identified; S5.2: Construct the minimum spanning tree using Prim's algorithm, and output the set B containing all edges of the minimum spanning tree; including: S5.2.1: Set two sets A and B, where set A stores the vertices in the minimum spanning tree of δ, and set B stores the edges in the minimum spanning tree of δ; S5.2.2: Let the initial value of set A be A = {d1}, and the initial value of set B be an empty set, where d1 represents the root node in the distribution network; S5.2.3: Select the edge with the minimum weight Г from all edges a∈A, d∈DA. ad Given an edge ad, add vertex d to set A and add edge ad to set B; S5.2.4: Repeat S5.2.3 continuously until A = D, at which point the minimum spanning tree is constructed; S5.3: Based on set B, obtain the connection relationship of the nodes in the distribution area to be identified, and output the radial topology of the low-voltage distribution network.

2. The radial topology identification method for low-voltage distribution networks according to claim 1, characterized in that: The specific steps in S1 to synchronously collect voltage data of transformer substation nodes and form dataset V are as follows: S1.1: Count the subordinate meter box file numbers of all transformer nodes, branch box nodes, and user meter box nodes in the distribution area to be identified, and obtain the number of nodes N and the node set D in the distribution area to be identified; S1.2: Collect voltage data of node i (i = 1, 2, ..., N) within the sampling period T, and form an n×1 dimensional matrix V. i Where n is the number of sampling points within the sampling period T, the calculation formula is as follows: Where f is the sampling frequency, f is greater than or equal to 6 sampling points per hour, and T is greater than or equal to 12 hours; S1.3: Collect the node voltage matrix of all nodes in the transformer area to form a dataset V = [V1, V2, ... V N ].

3. The radial topology identification method for low-voltage distribution networks according to claim 2, characterized in that: In step S2, the dataset V is standardized to form V s The specific steps are as follows: S2.1: To eliminate the influence of dimensions and ensure the accuracy of the analysis results, the voltage matrix V of each node is... i The standardization calculation formula is as follows: Among them, V is This is the standardized node voltage matrix, where mean(.) is the mean operator and sd(.) is the standard deviation operator. S2.2: Collect the standardized node voltage matrix of all nodes in the transformer area to form the dataset V. s =[V 1s V 2s ,...,V Ns ].

4. The radial topology identification method for low-voltage distribution networks according to claim 3, characterized in that: The specific steps in S3 to calculate the mean and variance of the voltage dataset for each node and construct its Gaussian probability density function model are as follows: S3.1: Calculate the mean μ of the voltage matrix at node i. i and variance σ i The calculation formula is as follows: Among them, V isk This represents the k-th normalized node voltage matrix of node i; S3.2: Assume that the voltage curve at node i follows a mean of μ i The variance is σ i The Gaussian probability density function of the normal distribution is calculated as follows:

5. A low-voltage distribution network radial topology identification device, used to implement the method as described in any one of claims 1-4, comprising: Data acquisition module: Synchronously collects voltage data from transformer substation nodes to form dataset V; Standardization module: Performs standardization processing on dataset V to form Vstandardization s ; Function building module: Calculates the mean and variance of the voltage dataset for each node and constructs its Gaussian probability density function model; The structure factor matrix construction module calculates the JS divergence between the node probability distribution curves to form a structure factor matrix containing distribution network topology information. Topology Output Module: Constructs the minimum spanning tree of the corresponding connected weighted graph and outputs the radial topology of the low-voltage distribution network.

6. A terminal, characterized in that, It includes a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to perform the steps of the method according to any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-4.

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