Power distribution network edge side topology identification method, device and equipment and readable storage medium

Abstract

The application relates to a power distribution network edge side topology identification method, device and equipment and a readable storage medium. The method comprises the following steps: establishing a voltage mutual information graph based on first voltage mutual information between each device in a target edge computing node, second voltage mutual information between devices in the target edge computing node and devices of adjacent edge computing nodes and each device in the target edge computing node; processing the voltage mutual information graph by using a distributed maximum spanning tree algorithm to establish a spanning tree; and obtaining a topology identification result based on the spanning tree and the voltage mutual information graph under the condition that all the devices in the voltage mutual information graph are contained in the spanning tree. The method can improve the topology identification efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric power, and particularly relates to a power distribution network edge side topology identification method and device, equipment and a readable storage medium. BACKGROUND

[0002] In recent years, with the advancement of modernization of power distribution networks, the digital level has rapidly improved, which not only significantly improves the reliability and operation efficiency of the power distribution system, but also promotes the efficient access of renewable energy and the intelligent progress of load management.

[0003] However, as the structure of the power distribution network becomes increasingly complex, the traditional topology identification method has the problem of low topology identification efficiency. SUMMARY

[0004] Therefore, it is necessary to provide a power distribution network edge side topology identification method, device, equipment and readable storage medium capable of improving the topology identification efficiency in view of the above technical problems.

[0005] In a first aspect, the present application provides a power distribution network edge side topology identification method, comprising:

[0006] Based on the first voltage mutual information between each device in the target edge computing node, the second voltage mutual information between the devices in the target edge computing node and the devices of the adjacent edge computing node, and each device in the target edge computing node, a voltage mutual information graph is established;

[0007] The voltage mutual information graph is processed by using a distributed maximum spanning tree algorithm to establish a spanning tree;

[0008] In the case that the spanning tree contains all devices in the voltage mutual information graph, a topology identification result is obtained based on the spanning tree and the voltage mutual information graph.

[0009] In one of the embodiments, the processing of the voltage mutual information graph by using the distributed maximum spanning tree algorithm to establish the spanning tree comprises:

[0010] For each device, the voltage mutual information meeting the preset condition is selected from the voltage mutual information graph as a candidate edge;

[0011] In the case that the two devices connected by the candidate edge belong to the same range of the target edge computing node, the candidate edge is added to the preset candidate edge list, and the spanning tree is established by using the candidate edge list;

[0012] In the case that the two devices connected by the candidate edge do not belong to the same range of the target edge computing node, the interaction information between the target edge computing node and the adjacent edge computing node is used to coordinate the update of the spanning tree.

[0013] In one of the embodiments, the selecting, from the voltage mutual information graph, the voltage mutual information meeting the preset condition as the candidate edge comprises:

[0014] In the case of selecting the candidate edge for the first time, the voltage mutual information with the maximum voltage mutual information is selected from the voltage mutual information graph as the candidate edge;

[0015] In the case of selecting the candidate edge for the first time, the voltage mutual information with the maximum voltage mutual information is selected from the voltage mutual information graph as the candidate edge;

[0016] In one of the embodiments, the obtaining the topology identification result based on the spanning tree and the voltage mutual information graph comprises:

[0017] In the case of the spanning tree being radial, the spanning tree is determined as the topology identification result;

[0018] In the case of the spanning tree not being radial, the topology identification result is generated based on the spanning tree and the conditional mutual information between the adjacent devices in the spanning tree.

[0019] In one of the embodiments, the obtaining the topology identification result based on the spanning tree and the conditional mutual information between the adjacent devices in the spanning tree comprises:

[0020] The conditional mutual information is compared and processed, and the minimum conditional mutual information is determined from the conditional mutual information;

[0021] The minimum conditional mutual information is broadcasted, and the comparison value returned by the other edge computing nodes is received, and in the case that the comparison value is greater than the minimum conditional mutual information, the loop topology is generated based on the device corresponding to the returned comparison value;

[0022] The topology identification result is generated based on the spanning tree and the loop topology.

[0023] In one of the embodiments, the determining process of the second voltage mutual information comprises:

[0024] The first voltage time sequence information of each device in the target edge computing node is obtained;

[0025] The second voltage time sequence information of each device in the adjacent edge computing node is obtained;

[0026] The second voltage mutual information is determined based on the first voltage time sequence information and the second voltage time sequence information.

[0027] In a second aspect, the application further provides a power distribution network edge side topology identification device, comprising:

[0028] The mutual information graph establishing module is configured to establish a voltage mutual information graph based on the first voltage mutual information between each device in the target edge computing node, the second voltage mutual information between the device in the target edge computing node and the device in the adjacent edge computing node, and each device in the target edge computing node.

[0029] The spanning tree establishing module is configured to process the voltage mutual information graph by using a distributed maximum spanning tree algorithm to establish a spanning tree.

[0030] The result determining module is configured to obtain a topology identification result based on the spanning tree and the voltage mutual information graph in a case where all the devices in the voltage mutual information graph are contained in the spanning tree.

[0031] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0032] The mutual information graph establishing module is configured to establish a voltage mutual information graph based on the first voltage mutual information between each device in the target edge computing node, the second voltage mutual information between the device in the target edge computing node and the device in the adjacent edge computing node, and each device in the target edge computing node.

[0033] The spanning tree establishing module is configured to process the voltage mutual information graph by using a distributed maximum spanning tree algorithm to establish a spanning tree.

[0034] The result determining module is configured to obtain a topology identification result based on the spanning tree and the voltage mutual information graph in a case where all the devices in the voltage mutual information graph are contained in the spanning tree.

[0035] In a fourth aspect, the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the following steps:

[0036] The mutual information graph establishing module is configured to establish a voltage mutual information graph based on the first voltage mutual information between each device in the target edge computing node, the second voltage mutual information between the device in the target edge computing node and the device in the adjacent edge computing node, and each device in the target edge computing node.

[0037] The spanning tree establishing module is configured to process the voltage mutual information graph by using a distributed maximum spanning tree algorithm to establish a spanning tree.

[0038] The result determining module is configured to obtain a topology identification result based on the spanning tree and the voltage mutual information graph in a case where all the devices in the voltage mutual information graph are contained in the spanning tree.

[0039] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, and the computer program is executed by a processor to implement the following steps:

[0040] establish a voltage mutual information graph based on the first voltage mutual information between each device in the target edge computing node, the second voltage mutual information between the device in the target edge computing node and the device of the adjacent edge computing node, and each device in the target edge computing node;

[0041] processing the voltage mutual information graph by using a distributed maximum spanning tree algorithm to establish a spanning tree;

[0042] in a case where all the devices in the voltage mutual information graph are contained in the spanning tree, obtaining a topology identification result based on the spanning tree and the voltage mutual information graph.

[0043] The power distribution network edge side topology identification method, device, equipment and readable storage medium have the following advantages. First, the voltage relationship between devices can be systematically captured by establishing the voltage mutual information graph. Second, the voltage mutual information graph is processed by using the distributed maximum spanning tree algorithm, which not only speeds up the construction process of the spanning tree, but also supports parallel computing, so that the topology identification can be quickly completed in a complex power distribution network. Finally, the topology identification result obtained based on the spanning tree and the voltage mutual information graph has high accuracy and can reflect the network state in time, thereby improving the topology identification efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other related drawings can also be obtained without creative labor.

[0045] Figure 1 The application environment diagram of the power distribution network edge side topology identification method in one embodiment;

[0046] Figure 2 The flowchart of the power distribution network edge side topology identification method in one embodiment;

[0047] Figure 3 The flowchart of the power distribution network edge side topology identification method in another embodiment;

[0048] Figure 4 The reference topology identification result conforming to the radial feature in one embodiment;

[0049] Figure 5 The actual topology identification result conforming to the radial feature in one embodiment;

[0050] Figure 6 The reference topology identification result conforming to the non-radial feature in one embodiment;

[0051] Figure 7 For the actual topology recognition result of the non-radiation characteristic in an embodiment;

[0052] Figure 8 For another embodiment, the flowchart of the power distribution network edge side topology recognition method is shown;

[0053] Figure 9 For another embodiment, the flowchart of the power distribution network edge side topology recognition method is shown;

[0054] Figure 10 For the purpose of Figure 4 The calculated second voltage interaction information of the No. 24 branch box with other transformers, branch boxes and users;

[0055] Figure 11 For the structure block diagram of the power distribution network edge side topology recognition device in an embodiment;

[0056] Figure 12 For the internal structure diagram of the computer device in an embodiment. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0058] The power distribution network edge side topology recognition method provided by the embodiments of the present application can be applied in the application environment as shown in Figure 1 The target edge computing node 01 realizes data sharing and collaborative computing through communication with adjacent edge computing node 02 and other edge computing node 03.

[0059] Edge computing node is a computing device or system located at the edge of the network, close to the data source or device. Its main function is to process and analyze data on site, thereby reducing delay, reducing network bandwidth occupation, and improving data processing efficiency. Compared with traditional cloud computing mode, edge computing node can perform calculation near the source of data generation, reducing the need for data transmission to remote data center. Its advantages include:

[0060] (1) Low latency: Since the calculation is completed near the data source, edge computing reduces the time to send data to a remote server, thereby reducing latency.

[0061] (2) Save bandwidth: Edge computing nodes can process a large amount of data locally, reducing the amount of raw data uploaded to the cloud.

[0062] (3) Enhanced privacy and security: Sensitive data can be processed locally, reducing the risk of data leakage during transmission.

[0063] (4) Reliability: Edge computing nodes can continue to work even when network connections are unstable or interrupted, without relying on remote servers.

[0064] In an exemplary embodiment, as shown in Figure 2 , a power distribution network edge side topology identification method is provided. Taking the target edge computing node in Figure 1 as an example, the method includes the following steps 201 to 203. Among them:

[0065] Step 201, based on the first voltage mutual information between each device in the target edge computing node, the second voltage mutual information between the devices in the target edge computing node and the devices of the adjacent edge computing node, and each device in the target edge computing node, a voltage mutual information graph is established.

[0066] In the embodiments of the present application, first, according to the overall layout and device distribution of the power distribution network, the area responsible for each edge computing node is determined. The jurisdiction of each edge computing node includes specific devices such as transformers, branch boxes and user meters, etc. The principle of area division is usually based on the geographical location, communication capability and load distribution of the computing node to ensure that each edge node can efficiently manage the devices under its responsibility.

[0067] Then, input all the device information in each divided area into the corresponding edge computing node. Then, for the devices in the area governed by the target edge computing node, calculate the first voltage mutual information. The first voltage mutual information represents the voltage correlation between devices under the same edge computing node. By analyzing the mutual information of voltage changes between devices, the voltage dependency of them is evaluated.

[0068] After calculating the voltage mutual information between devices, the second voltage mutual information is calculated for the devices between the target edge computing node and the adjacent edge computing node. The second voltage mutual information is used to measure the voltage relationship between the devices in the target edge node and other adjacent node devices, so as to identify the connection and dependency between cross-area devices in the topology structure.

[0069] Finally, the target edge computing node generates a voltage mutual information graph by integrating the voltage information of the devices in the area under its jurisdiction. In the voltage mutual information graph, the vertices represent the devices, and the weights of the edges correspond to the voltage mutual information values between the devices. The first voltage mutual information is used to describe the connection between internal devices, while the second voltage mutual information covers the voltage correlation between cross-area devices.

[0070] In some embodiments, the target edge computing node establishes and stores a voltage mutual information subgraph with devices in the region under its jurisdiction, such as transformers, branch boxes, and user meters, as vertices, and with edges as weights according to the first voltage mutual information and the second voltage mutual information, and the voltage mutual information graph formed by merging the voltage mutual information subgraphs obtained by all edge computing nodes is denoted as G V ; wherein the voltage mutual information graph G V is stored in a distributed manner on each edge computing node:

[0071]

[0072] ε V = ε V (1) U ε V (2) U … U ε V (N E )

[0073]

[0074] In the formula, E denotes a set of devices composed of transformers, branch boxes, and user meters, denotes a set of devices in the region under the jurisdiction of the i th edge computing node; E V denotes the mutual information value between each two devices in E calculated from voltage time series data; E V (i) (i = 1, 2, …, N E ) denotes the voltage mutual information between the devices in the region under the jurisdiction of the i th edge computing node and the devices in the region under the jurisdiction of the adjacent edge computing node; G V (i) is the voltage mutual information subgraph stored on the i th edge computing node; and N E denotes the number of edge computing nodes.

[0075] In step 202, a distributed maximum spanning tree algorithm is used to process the voltage mutual information graph to establish a spanning tree.

[0076] In the distributed maximum spanning tree algorithm, each edge computing node is responsible for processing the voltage mutual information graph of the devices in the region under its jurisdiction, and cooperates with adjacent edge nodes through parallel processing. Each edge computing node initializes the device vertices in its region. A unique identification (ID) is assigned to each device vertex, and attributes such as "level" are set to 0. The search state of all edges is initially marked as an initial state, indicating that they have not been processed. Subsequently, the edge computing node starts from each vertex to find the edge with the maximum voltage mutual information in the adjacent edges, and adds it to the spanning tree candidate edge list.

[0077] During the construction of the spanning tree, each edge computing node continuously communicates with adjacent nodes to obtain necessary topological information. For example, when two vertices of an edge belong to different edge computing nodes, the nodes will cooperate through message communication to determine whether the edge should be added to the spanning tree. If both nodes select the edge and meet the maximum mutual information standard, it will be added to the spanning tree, and the spanning tree structure of both parties will be updated.

[0078] After that, each edge computing node continues to search for other candidate edges along the edges that have been marked as having been added to the spanning tree. Each node prioritizes edges that meet certain conditions when searching.

[0079] When all edge computing nodes complete the construction of the spanning tree, a maximum spanning tree containing all devices in the voltage mutual information graph is obtained. In this spanning tree, each edge represents the optimal voltage mutual information connection between two devices.

[0080] Step 203, based on the spanning tree and the voltage mutual information graph, obtain the topological identification result under the condition that the spanning tree contains all devices in the voltage mutual information graph.

[0081] In the embodiments of the present application, first, the integrity of the spanning tree is verified. In the previous steps, a spanning tree covering all devices in the voltage mutual information graph has been constructed through the distributed maximum spanning tree algorithm. At this time, it is necessary to confirm whether the spanning tree has included all device nodes and to ensure that there are no missing or unprocessed devices.

[0082] Next, the voltage mutual information of each edge in the spanning tree is analyzed. Each edge in the spanning tree represents the voltage correlation between two devices, and the voltage mutual information of each edge is obtained through previous calculations. At this time, the strength of the correlation between devices can be further analyzed in combination with the voltage mutual information of all devices in the voltage mutual information graph.

[0083] Then, the topological structure is constructed based on the spanning tree. The connection relationship of all devices in the spanning tree has been determined, and at this time the topological structure of the power distribution network can be constructed based on these connection relationships. The edges in the spanning tree reflect the strongest voltage correlation between devices and ensure that there is no loop. The target edge computing node converts the connection information of all devices and edges in the spanning tree into the actual topological structure of the power distribution network, thereby forming a logical connection graph between devices.

[0084] During the construction of the topological structure, the voltage mutual information graph can be adjusted in combination. Although the spanning tree provides the core device connection relationship, in some special cases, reference needs to be made to information in the voltage mutual information graph that is not included in the spanning tree, such as some auxiliary connections or backup paths.

[0085] Finally, through comprehensive analysis of spanning tree and voltage mutual information graphs, the topology identification result of the distribution network is obtained. The generated topology will serve as the basis for the operation and maintenance of the distribution network, providing support for subsequent network optimization, fault diagnosis, and power dispatch.

[0086] In the aforementioned distribution network edge-side topology identification method, firstly, by establishing a voltage mutual information graph, the voltage relationships between devices can be systematically captured. Secondly, the distributed maximum spanning tree algorithm is applied to process the voltage mutual information graph, which not only accelerates the construction process of the spanning tree but also supports parallel computing, enabling rapid topology identification in complex distribution networks. Finally, the topology identification results obtained based on the spanning tree and the voltage mutual information graph have high accuracy and can reflect the network status in a timely manner, thereby improving the efficiency of topology identification.

[0087] In one exemplary embodiment, such as Figure 3 As shown, the distributed maximum spanning tree algorithm is used to process the voltage mutual information graph and establish a spanning tree, including steps 301 to 303. Wherein:

[0088] Step 301: For each device, select voltage mutual information that meets the preset conditions from the voltage mutual information diagram as the candidate edge.

[0089] The preset conditions can be that the mutual information value is greater than a certain threshold or that the correlation is strong. The candidate edges represent potential connections between two devices, and these candidate edges will be used in the subsequent construction of the spanning tree.

[0090] In this embodiment, for each device node, it is necessary to obtain the voltage mutual information between the device and other devices from the voltage mutual information graph. Then, the voltage mutual information edges that meet the conditions are selected by filtering according to preset conditions.

[0091] Step 302: If the two devices connected by the candidate edge belong to the same target edge computing node, add the candidate edge to the preset candidate edge list and use the candidate edge list to build a spanning tree.

[0092] In this embodiment, the edge is categorized based on the edge computing nodes to which the two devices connected by the candidate edge belong. If both devices connected by the candidate edge belong to the same edge computing node's jurisdiction, the edge can be directly considered a candidate edge for local connection and added to the candidate edge list of that edge computing node. The purpose of this is to minimize cross-node coordination operations and prioritize the construction of the spanning tree within each edge computing node.

[0093] Then, the edges in the candidate edge list are used to build the spanning tree step by step inside the edge computing node. The building process follows the principle of the distributed maximum spanning tree algorithm, that is, the edge with the largest mutual information value is preferentially selected, and it is ensured that the spanning tree does not form a loop. As the candidate edges are gradually added to the spanning tree, the edge computing node can obtain a local spanning tree covering all devices in its jurisdiction. This process is carried out independently inside each edge computing node, ensuring the effectiveness of the local topology.

[0094] In some embodiments, each edge computing node traverses the spanning tree candidate edge list stored by itself, and for each edge in the spanning tree candidate edge list, if both ends of the edge, s and t, are selected as spanning tree candidate edges, the edge is added to the spanning tree G V(T) , and the search state of the edge is set to an edge that has been added to the spanning tree.

[0095] In some embodiments, according to the edge newly added to the spanning tree G V(T) , the edge computing node to which one of the vertices of the edge belongs starts to visit each vertex in turn along the edge whose search state is an edge that has been added to the spanning tree. When the vertex s is visited, if the vertex s is not in , the edge computing node to which the vertex s belongs sends a start message containing ID(k) and Level(k) to the edge computing node to which the vertex s belongs. At this time, the edge computing node to which the vertex s belongs receives the message and performs the maximum mutual information edge search process in the jurisdiction, and returns the maximum mutual information edge that it can search to the master node in the report message. If the vertex s is in , the maximum mutual information edge search process in the jurisdiction is performed to obtain the maximum mutual information edge that can be searched in the jurisdiction. After the master node receives all the report messages returned by the slave nodes in the maximum mutual information edge search process in the jurisdiction, the edge with the largest mutual information among all edges is selected as a spanning tree candidate edge and added to the spanning tree candidate edge list. Wherein, represents the set of devices in the jurisdiction of the i-th edge computing node, ID(k) represents the identity information of the device k vertex, and Level(k) represents the level information of the device k vertex.

[0096] Step 303, in the case where the two devices connected by the candidate edge do not belong to the jurisdiction of the same target edge computing node, the mutual information between the target edge computing node and the adjacent edge computing node is used to coordinate the update of the spanning tree.

[0097] In the embodiments of the present application, for the case where the candidate edge involves cross-node connection, that is, the two devices connected by the candidate edge belong to different target edge computing node jurisdictions respectively, cross-node coordination update is needed. At this time, the candidate edge connecting the two devices cannot be directly added to the spanning tree of a certain target edge computing node, but is processed collaboratively through information interaction between the target edge computing node and the adjacent edge computing node. The target edge computing node sends an interaction message containing the candidate edge information to the adjacent edge computing node, and the two sides coordinate whether to add the edge to the spanning tree. If both nodes confirm that the candidate edge meets the conditions, the candidate edge is added to the spanning tree, and the local spanning tree structures of the two sides are updated.

[0098] Finally, the complete spanning tree is generated and updated. With the local spanning trees built in each edge computing node and the collaborative processing across nodes, the spanning tree of the entire power distribution network is gradually formed. Through communication and cooperation, each edge computing node finally aggregates all local spanning trees and cross-node connected edges to form a complete spanning tree covering all global devices. In this process, the update of the spanning tree is dynamic, and as new candidate edges are added, the topology of the power distribution network is continuously optimized and adjusted.

[0099] In some embodiments, the edge computing node i (referred to as "node i" for short) traverses the spanning tree candidate edge list stored by itself, and for each edge connected to two vertices, the vertex belonging to itself is denoted as s, and the vertex belonging to another edge computing node j (referred to as "node j" for short) is denoted as t. The node i sends a connection message containing ID(s) and Level(s) to the node j and waits for a response; when the node j receives the connection message, it selects different processing methods according to the content of the connection message.

[0100] If t does not select this edge in its spanning tree candidate edge list or Level(s) < level(t), the node j does not respond to this message temporarily, and places this message in the message queue.

[0101] In an exemplary embodiment, the voltage mutual information that meets the preset condition is selected from the voltage mutual information graph as a candidate edge, including:

[0102] Case one, in the case of selecting a candidate edge for the first time, the voltage mutual information with the maximum voltage mutual information is selected from the voltage mutual information graph as a candidate edge.

[0103] In the embodiments of the present application, when selecting a candidate edge for the first time, the edge with the maximum voltage mutual information value can be directly selected from the voltage mutual information graph as a candidate edge. The basis for this selection is that the voltage mutual information value reflects the strong correlation between two devices, and preferentially selecting the edge with the maximum mutual information value can ensure that the initial construction of the spanning tree is based on the most reliable connection, thereby enhancing the effectiveness of subsequent topology identification.

[0104] In some embodiments, the search process of the maximum voltage mutual information can include the following steps:

[0105] 1) When edge computing node i searches from vertex k to s along the edge whose search state is the edge that has been added to the spanning tree, update ID(s) = ID(k), Level(s) = Level(k)

[0106] 2) Select an edge with the maximum voltage mutual information from the edges connected to s whose search state is the initial state, make conditional judgment and perform corresponding operations:

[0107] If ID(s) = ID(t), mark this edge as rejected, select an edge with the maximum voltage mutual information from the edges connected to s whose search state is the initial state, and repeat step 2);

[0108] If ID(s) ≠ ID(t) and level(t) ≥ level(s), select this edge as the pending maximum mutual information edge;

[0109] If ID(s) ≠ ID(t) and level(t) < level(s) and t takes this edge as the spanning tree pending edge, update the search state of this edge to the edge that has been added to the spanning tree, and also search along this edge in the subsequent search of edge computing node i, select an edge with the maximum voltage mutual information from the edges connected to s whose search state is the initial state, and repeat step 2);

[0110] If ID(s) ≠ ID(t) and level(t) < level(s) and t does not take this edge as the spanning tree pending edge, wait until level(t) is updated to be not less than level(s) or t takes this edge as the spanning tree pending edge, and then repeat step 2);

[0111] 3) Compare the mutual information values of the pending maximum mutual information edges of each vertex, and select the maximum mutual information edge as the search result;

[0112] 4) After edge computing node i receives the start message of edge computing node j, it performs this search process, and then sends a report message containing the search result to edge computing node j. Wherein, Level(s) represents the level information of device s vertex, Level(t) represents the level information of device t vertex, ID(s) represents the identity information of device s vertex, ID(t) represents the identity information of device t vertex,

[0113] In the second scenario, when the candidate edge is not selected for the first time, the maximum mutual information edge collaborative search algorithm is used to select candidate edges that meet the cutoff conditions from the voltage mutual information graph. The cutoff conditions include: the candidate edge is not in the spanning tree, the spanning tree is still a tree graph after the candidate edge is added, and the candidate edge has the largest voltage mutual information in the voltage mutual information graph.

[0114] In this embodiment of the application, when selecting candidate edges before the first time, all candidate edges that are not yet included in the current spanning tree are first identified from the voltage mutual information graph. These candidate edges can become potential candidate edges.

[0115] Next, for each potential candidate edge, check whether it meets the preset cutoff condition:

[0116] (1) The edge must not be in the current spanning tree to ensure that the newly added edge is independent.

[0117] (2) After adding this edge, the spanning tree must still maintain the structure of the tree diagram and cannot form a loop.

[0118] (3) The voltage mutual information value of the edge to be selected must be the largest among all edges in the voltage mutual information graph to ensure that the edge with the highest correlation is selected.

[0119] If a candidate edge that meets all cutoff conditions is found, it is added to the spanning tree. Through this process, the spanning tree can be progressively expanded to cover more devices and maintain the highest voltage mutual information connectivity throughout the topology.

[0120] In some embodiments, each edge computing node executes a distributed maximum spanning tree algorithm on the voltage mutual information graph G in parallel. V The spanning tree G that maximizes the sum of voltage mutual information. V(T) ;in,

[0121] The distributed maximum spanning tree algorithm includes the following steps:

[0122] 1) The i-th edge computing node, based on the device information, provides... Each vertex is initialized with a unique ID, its level attribute is set to 0, and the search state of all edges is set to the initial state.

[0123] 2) The i-th edge computing node will Each vertex in the array is set to a found state, based on... For each vertex, select the edge with the highest voltage mutual information among its adjacent edges and add it to the candidate edge list for the spanning tree. To add a candidate edge to the spanning tree, if the two vertices connected by an edge in the list belong to the same vertex group... Then, the autonomous update process of the spanning tree within the jurisdiction is executed, and the candidate edges are added to the spanning tree G only within the current edge computing node.V(T) Otherwise, the spanning tree collaborative update process within the jurisdiction is executed, and the two edge computing nodes coordinate to add the candidate edge to the spanning tree G. V(T) ;

[0124] 3) The i-th edge computing node is added to G according to 2). V(T) Starting from one of the two vertices connected by the edge, the search proceeds along the edges whose state is already in the spanning tree, visiting each vertex in turn, performing a maximum mutual information edge cooperative search, and searching G. V Edges that meet the following three conditions will be added to the candidate edge list of the spanning tree:

[0125] This edge is not in G V(T) middle;

[0126] After adding this edge, G V(T) It still resembles a tree;

[0127] The voltage mutual information search state of this edge is the largest among the edges in the initial state;

[0128] 4) Based on the candidate edge list of the spanning tree obtained in 3), if the two vertices connected by an edge in the list belong to the same edge computing node, the autonomous update process of the spanning tree within the jurisdiction is executed; otherwise, the collaborative update process of the spanning tree between the jurisdictions is executed.

[0129] 5) Return to 3). If no candidate edge for the spanning tree that meets the requirements of 3) can be found, the algorithm stops. In each edge computing node, the edge whose search status is "added to the spanning tree" represents the connection relationship of each device in its jurisdiction. If a candidate edge for the spanning tree that meets the requirements of 3) can be found, then execute 4).

[0130] In one exemplary embodiment, based on the spanning tree and voltage mutual information graph, the topology identification result is obtained, including:

[0131] In the first case, when the spanning tree is radial, the spanning tree is determined to be the result of topological identification.

[0132] In this embodiment, when the target edge computing node receives an input instruction from the user indicating that the spanning tree should be radial, the target edge computing node will parse the user's input instruction to confirm the validity of the instruction content and format. After ensuring that the instruction accurately conveys the user's intent, the target edge computing node will begin to analyze the structure of the current spanning tree. At this time, the target edge computing node will check the connection relationship of the spanning tree to confirm whether all devices form a radial layout around a central node.

[0133] If the spanning tree conforms to the radial shape, the target edge computing node will identify the spanning tree as the topology identification result.

[0134] likeFigure 4 and Figure 5 as shown in FIG. 2, Figure 4 the reference topology recognition result of the radial feature, Figure 5 the actual topology recognition result of the radial feature, from Figure 4 and Figure 5 It can be seen that the actual topology recognition result obtained by the embodiment of the present application is approximately the same as the reference topology recognition result.

[0135] Case two, in the case where the spanning tree is not radial, based on the spanning tree and the conditional mutual information between adjacent devices in the spanning tree, a topology recognition result is generated.

[0136] In the embodiment of the present application, when the target edge computing node receives the input instruction of the user indicating that the spanning tree is not radial, the target edge computing node will analyze the instruction input by the user to confirm the validity of the instruction content and format. After ensuring that the instruction accurately conveys the user's intention, the conditional mutual information between adjacent devices in the spanning tree is analyzed first to quantify the information flow and correlation strength between these devices.

[0137] Then, using the calculated conditional mutual information value, it is evaluated which connections between devices can be optimized to reduce the complexity and potential redundant connections in the spanning tree. Finally, according to the evaluation result, the structure of the spanning tree is adjusted, the connections with higher mutual information are retained, and the connections with lower mutual information or redundancy are removed, thereby generating a new topology recognition result.

[0138] Finally, after these adjustments, the target edge computing node will generate a new topology recognition result and save it.

[0139] In some embodiments, for each pair of devices i and j that are not connected by an edge in the spanning tree G V(T) , the conditional mutual information of device i with device j when the voltages of adjacent devices in G V(T) are known is calculated The conditional mutual information of device j with device i when the voltages of adjacent devices in G V(T) are known is calculated If both are greater than γ, then the edge ij is included in the loop subgraph G V(O) ; the calculation formula of the conditional mutual information and is as follows:

[0140]

[0141] In the formula, V i , V j represent the voltages of devices i and j in G V(T) , N T (i) and N T(j) the set of neighboring devices of device i and device j in G V(T) , respectively denote the set of neighboring device voltages of device i and j, respectively denote the joint distribution of the voltage of device i and the voltage of device j with the respective neighboring device voltages, respectively denote the joint distribution of the set of neighboring device voltages of device i and device j, denote the joint distribution of the voltage of device i, the voltage of device j, and the set of neighboring device voltages of device i, denote the joint distribution of the voltage of device i, the voltage of device j, and the set of neighboring device voltages of device i, denote the joint distribution of the voltage of device i, the voltage of device j, and the set of neighboring device voltages of device j, is an intermediate parameter, and log2 denotes the logarithm with base 2.

[0142] When device i and j do not belong to the same edge computing node, the edge computing nodes to which device i and j belong can only exchange the time series data of the voltages of device i and j to cooperatively complete the calculation of the conditional mutual information.

[0143] As shown in Figure 6 and Figure 7 , Figure 6 is the reference topology identification result conforming to the characteristic of non-radiation, Figure 7 is the actual topology identification result conforming to the characteristic of non-radiation, and it can be seen from Figure 6 and Figure 7 that the actual topology identification result obtained by the embodiment of the present application is approximately the same as the reference topology identification result.

[0144] In an exemplary embodiment, as shown in Figure 8 , the voltage mutual information graph is processed by using a distributed maximum spanning tree algorithm to establish a spanning tree, including steps 401 to 403. Among them:

[0145] Step 401, comparing and processing each conditional mutual information to determine the minimum conditional mutual information from the conditional mutual information.

[0146] Among them, the minimum conditional mutual information reflects the minimum limit of the information correlation degree between devices.

[0147] In the embodiment of the present application, first, the target edge computing node can collect the conditional mutual information related to the adjacent relationship between each device in the spanning tree to form a list containing all the conditional mutual information. Then, the target edge computing node compares each conditional mutual information one by one by traversing the list to determine the minimum conditional mutual information therein.

[0148] Step 402: Broadcast the minimum conditional mutual information and receive comparison values ​​returned by other edge computing nodes. If the comparison value is greater than the minimum conditional mutual information, generate a ring topology based on the device corresponding to the returned comparison value.

[0149] In this embodiment, the target edge computing node first broadcasts the determined minimum conditional mutual information to all other edge computing nodes.

[0150] Next, upon receiving the broadcast, each of the other edge computing nodes will compare its internally stored conditional mutual information. If the conditional mutual information value of one of the other edge computing nodes is greater than the minimum value broadcast by the target node, that other edge computing node will send that value and the corresponding device information back to the target node.

[0151] After receiving comparison values ​​from other nodes, the target edge computing node performs further analysis. If the comparison value is greater than the minimum conditional mutual information, it indicates that the connections between these devices have low mutual information correlation, and there may be redundant or unnecessary connections; only when the comparison value is greater than the minimum conditional mutual information can a link exist between the two devices. Based on this information, the target node selects these devices to generate a ring topology to optimize the network structure and reduce unnecessary complexity.

[0152] Ultimately, by combining the generated loop topology with the existing spanning tree, the target edge computing node will be able to generate more accurate topology identification results.

[0153] Step 403: Generate topology identification results based on the spanning tree and the loop topology.

[0154] In this embodiment, the target edge computing node first analyzes the connection relationships between devices in the spanning tree and the redundant connections shown in the ring topology. By comparing these two, the target edge computing node can identify which connections between devices are necessary and which may be redundant. Based on this, the target edge computing node will perform structural adjustments to ensure that necessary connections are maintained while removing redundant or low-mutual-information connections to optimize the overall topology. Finally, the target edge computing node will integrate the spanning tree and the ring topology to form a comprehensive topology identification result. This topology identification result not only reflects the actual connection status between devices in the distribution network but also effectively reflects the information flow and interconnection relationships between devices, thus providing an important basis for subsequent power system management and optimization.

[0155] In one exemplary embodiment, such as Figure 9 As shown, the distributed maximum spanning tree algorithm is used to process the voltage mutual information graph and establish a spanning tree, including steps 501 to 502. Wherein:

[0156] Step 501, obtaining first voltage time series information of each device in the target edge computing node.

[0157] In the embodiments of the present application, the target edge computing node monitors the voltage of each device, records the voltage value and its corresponding timestamp, and forms the first voltage time series.

[0158] Step 502, obtaining second voltage time series information of each device in the adjacent edge computing node.

[0159] In the embodiments of the present application, first, the target edge computing node establishes a connection with the adjacent edge computing node through a communication protocol for data interaction. Then, the target edge computing node sends a request to the adjacent edge computing node to obtain the first voltage time series information of each device in its jurisdiction. The first voltage time series information usually includes the voltage value of the device and the corresponding timestamp to ensure the consistency of the time series. After receiving the request, the adjacent edge computing node organizes the required data and sends it back to the target edge computing node.

[0160] Step 503, determining the second voltage mutual information based on the first voltage time series information and the second voltage time series information.

[0161] In the embodiments of the present application, the target edge computing node obtains the first voltage time series information from the devices in its jurisdiction, and at the same time obtains the second voltage time series information provided by the adjacent edge computing node. Next, the target node preprocesses the two sets of time series data, including denoising, standardization and alignment, to ensure the consistency of the data on the time axis. Based on the first voltage time series information and the second voltage time series information, the second voltage mutual information is determined.

[0162] In some embodiments, according to the selected power distribution network, the jurisdiction of each edge computing node is divided and the devices in the jurisdiction are input, including transformers, branch boxes, and user meters; the first voltage time series information of each device is input;

[0163] Each edge computing node calculates the second voltage mutual information of the transformers to the branch boxes, between the branch boxes, and from the branch boxes to the user meters in its jurisdiction and the jurisdiction of the adjacent edge computing node based on the second voltage time series information of the devices in the jurisdiction of each edge computing node and the jurisdiction of the adjacent edge computing node; wherein the second voltage mutual information of the transformers to the branch boxes, between the branch boxes, and from the branch boxes to the user meters is represented as:

[0164]

[0165] When calculating the voltage mutual information of the transformer to the branch box, the formula is M(V i ;V jrepresents mutual information between the i th transformer and the j th branch box, p(v i ,v j ) represents the joint distribution of voltage of the i th transformer and the j th branch box, p(v i ), p(v j ) respectively represent the voltage distribution of the i th transformer and the j th branch box.

[0166] When calculating the voltage mutual information between each branch box, in the formula, M(v i ; v j ) represents the mutual information between the i th branch box and the j th branch box, p(v i ,v j ) represents the joint distribution of voltage of the i th branch box and the j th branch box, p(v i ), p(v j ) respectively represent the voltage distribution of the i th branch box and the j th branch box.

[0167] When calculating the branch box to the user meter, in the formula, M(v i ; v j ) represents the mutual information between the i th branch box and the j th user meter, p(v i ,v j ) represents the joint distribution of voltage of the i th branch box and the j th user meter, p(v i ), p(v j ) respectively represent the voltage distribution of the i th branch box and the j th user meter.

[0168] As shown in Figure 10 , Figure 10 is the second voltage interaction information of the 24 th branch box with other transformers, branch boxes and users calculated according to Figure 4 . In Figure 10 , the mutual information in the vertical axis is the second voltage interaction information in the embodiment of the application.

[0169] A detailed embodiment is given below to explain the process of the power distribution network edge side topology identification method in the application. On the basis of the above embodiment, the implementation process of the method can include the following contents:

[0170] Step 1, obtaining the first voltage time sequence information of each device in the target edge computing node.

[0171] Step 2, obtaining the second voltage time sequence information of each device in the adjacent edge computing node.

[0172] Step 3, determining the second voltage mutual information based on the first voltage time sequence information and the second voltage time sequence information.

[0173] Step 4, based on the first voltage mutual information between each device in the target edge computing node, the second voltage mutual information between the device in the target edge computing node and the device in the adjacent edge computing node, and each device in the target edge computing node, a voltage mutual information graph is established.

[0174] Step 5, for each device, voltage mutual information meeting preset conditions is selected from the voltage mutual information graph as a candidate edge.

[0175] In some embodiments, in the case of first selection of the candidate edge, the voltage mutual information with the largest voltage mutual information is selected from the voltage mutual information graph as the candidate edge.

[0176] In some embodiments, in the case of non-first selection of the candidate edge, the candidate edge meeting the cutoff condition is selected from the voltage mutual information graph by using the maximum mutual information edge collaborative search algorithm; the cutoff condition includes that the candidate edge is not in the spanning tree, the spanning tree is still a tree graph after the candidate edge is added, and the candidate edge is the largest voltage mutual information in the voltage mutual information graph.

[0177] Step 6, in the case that the two devices connected by the candidate edge belong to the same range of the target edge computing node, the candidate edge is added to the preset candidate edge list, and the spanning tree is established by using the candidate edge list. In the case that the two devices connected by the candidate edge do not belong to the same range of the target edge computing node, the interaction information between the target edge computing node and the adjacent edge computing node is used to coordinate and update the spanning tree.

[0178] Step 7, in the case that the spanning tree contains all devices in the voltage mutual information graph, the topology identification result is obtained based on the spanning tree and the voltage mutual information graph.

[0179] Step 8, in the case that the spanning tree is radial, the spanning tree is determined as the topology identification result.

[0180] Step 9, in the case that the spanning tree is not radial, the conditional mutual information is compared and processed, and the minimum conditional mutual information is determined from the conditional mutual information.

[0181] Step 10, the minimum conditional mutual information is broadcasted, and the comparison value returned by other edge computing nodes is received, in the case that the comparison value is greater than the minimum conditional mutual information, the loop topology is generated based on the device corresponding to the returned comparison value.

[0182] Step 11, based on the spanning tree and the loop topology, the topology identification result is generated.

[0183] It should be understood that although the steps in the flowcharts involved in the above embodiments are shown in sequence according to the arrows, the steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of the steps is not strictly limited in sequence, and the steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the above embodiments can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of the steps or stages is not necessarily sequential, but can be alternately or alternately executed with at least part of other steps or steps or stages in other steps.

[0184] Based on the same inventive concept, the embodiments of the present application also provide a power distribution network edge side topology identification device for implementing the above-mentioned power distribution network edge side topology identification method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more power distribution network edge side topology identification device embodiments provided below can refer to the limitations of the power distribution network edge side topology identification method described above, which will not be repeated here.

[0185] In one exemplary embodiment, as shown in Figure 11 A power distribution network edge side topology identification device is provided, comprising: a mutual information graph establishing module 601, a spanning tree establishing module 602 and a result determining module 603, wherein:

[0186] The mutual information graph establishing module 601 is configured to establish a voltage mutual information graph based on the first voltage mutual information between each device in the target edge computing node, the second voltage mutual information between the devices in the target edge computing node and the devices of the adjacent edge computing node, and each device in the target edge computing node;

[0187] The spanning tree establishing module 602 is configured to process the voltage mutual information graph using a distributed maximum spanning tree algorithm to establish a spanning tree;

[0188] The result determining module 603 is configured to obtain a topology identification result based on the spanning tree and the voltage mutual information graph in the case that the spanning tree contains all the devices in the voltage mutual information graph.

[0189] In some embodiments, the spanning tree establishing module 602 is specifically configured to, for each device, select a voltage mutual information meeting a preset condition from the voltage mutual information graph as a candidate edge; in a case where two devices connected by the candidate edge belong to a same range of a target edge computing node, add the candidate edge to a preset candidate edge list, and establish the spanning tree by using the candidate edge list; in a case where the two devices connected by the candidate edge do not belong to the same range of the target edge computing node, coordinate and update the spanning tree by using interaction information between the target edge computing node and a neighboring edge computing node.

[0190] In some embodiments, the spanning tree establishing module 602 is specifically configured to, in a case of first selecting the candidate edge, select a voltage mutual information with a maximum voltage mutual information from the voltage mutual information graph as the candidate edge; in a case of non-first selecting the candidate edge, select the candidate edge meeting a cutoff condition from the voltage mutual information graph by using a maximum mutual information edge collaborative search algorithm; the cutoff condition includes that the candidate edge is not in the spanning tree, after adding the candidate edge, the spanning tree is still a tree graph, and the candidate edge is a voltage mutual information with a maximum voltage mutual information in the voltage mutual information graph.

[0191] In some embodiments, the result determining module 603 is specifically configured to, in a case where the spanning tree is a radial structure, determine that the spanning tree is a topology identification result; in a case where the spanning tree is not a radial structure, generate a topology identification result based on the spanning tree and conditional mutual information between neighboring devices in the spanning tree.

[0192] In some embodiments, the result determining module 603 is specifically configured to compare each conditional mutual information, and determine a minimum conditional mutual information from the conditional mutual information.

[0193] broadcast the minimum conditional mutual information, receive a comparison value returned by another edge computing node, and in a case where the comparison value is greater than the minimum conditional mutual information, generate a loop topology based on a device corresponding to the returned comparison value.

[0194] generate a topology identification result based on the spanning tree and the loop topology.

[0195] In some embodiments, the mutual information graph establishing module 601 is specifically configured to obtain first voltage time sequence information of each device in the target edge computing node; obtain second voltage time sequence information of each device in a neighboring edge computing node; and determine second voltage mutual information based on the first voltage time sequence information and the second voltage time sequence information.

[0196] The modules in the power distribution network edge side topology identification device can be implemented by software, hardware, or a combination thereof. The modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory in the computer device in software form, so that the processor can call and execute the operations of the modules.

[0197] In an exemplary embodiment, a computer device is provided, which can be a target edge computing node, and an internal structure diagram of the computer device can be as shown in Figure 12 The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is configured to store data in the process of identifying the topology of the power distribution network edge side. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals through a network connection. The computer program is executed by the processor to implement a method for identifying the topology of the power distribution network edge side.

[0198] Those skilled in the art can understand that Figure 12 The structure shown in the above description is only a block diagram of part of the structure related to the scheme of the present application, and does not limit the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0199] In an exemplary embodiment, a computer device is provided, which includes a memory and a processor, and the memory stores a computer program. When the processor executes the computer program, the following steps are implemented:

[0200] Based on the first voltage mutual information between each device in the target edge computing node, the second voltage mutual information between the devices in the target edge computing node and the devices in the adjacent edge computing node, and each device in the target edge computing node, a voltage mutual information graph is established;

[0201] The voltage mutual information graph is processed using a distributed maximum spanning tree algorithm to establish a spanning tree;

[0202] In a case where all devices in the voltage mutual information graph are contained in the spanning tree, the topology identification result is obtained based on the spanning tree and the voltage mutual information graph.

[0203] In one embodiment, the processor, when executing the computer program, also implements the following steps:

[0204] For each device, a voltage mutual information meeting a preset condition is selected from the voltage mutual information graph as a candidate edge;

[0205] In a case where the two devices connected by the candidate edge belong to a range of the same target edge computing node, the candidate edge is added to a preset candidate edge list, and the spanning tree is established by using the candidate edge list;

[0206] In a case where the two devices connected by the candidate edge do not belong to a range of the same target edge computing node, the spanning tree is updated by using interaction information between the target edge computing node and an adjacent edge computing node.

[0207] In one embodiment, the processor, when executing the computer program, also implements the following steps:

[0208] In a case of first selecting the candidate edge, a voltage mutual information with the largest voltage mutual information is selected from the voltage mutual information graph as the candidate edge;

[0209] In a case of not first selecting the candidate edge, the candidate edge meeting a cutoff condition is selected from the voltage mutual information graph by using a maximum mutual information edge collaborative search algorithm; the cutoff condition includes that the candidate edge is not in the spanning tree, the spanning tree is still a tree graph after the candidate edge is added, and the candidate edge is the largest voltage mutual information in the voltage mutual information graph.

[0210] In one embodiment, the processor, when executing the computer program, also implements the following steps:

[0211] In a case where the spanning tree is a radial tree, the spanning tree is determined as the topology identification result;

[0212] In a case where the spanning tree is not a radial tree, the topology identification result is generated based on the spanning tree and conditional mutual information between adjacent devices in the spanning tree.

[0213] In one embodiment, the processor, when executing the computer program, also implements the following steps:

[0214] The conditional mutual information is compared and processed, and the minimum conditional mutual information is determined from the conditional mutual information;

[0215] The minimum conditional mutual information is broadcasted, and a comparison value returned by another edge computing node is received; in a case where the comparison value is greater than the minimum conditional mutual information, a loop topology is generated based on a device corresponding to the returned comparison value;

[0216] Based on the spanning tree and the looped topology, a topology identification result is generated.

[0217] In one embodiment, the processor, when executing the computer program, also implements the following steps:

[0218] Obtain first voltage time series information of each device in the target edge computing node;

[0219] Obtain second voltage time series information of each device in the adjacent edge computing node;

[0220] Based on the first voltage time series information and the second voltage time series information, determine the second voltage mutual information.

[0221] In one embodiment, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program, and the computer program, when executed by a processor, implements the following steps:

[0222] Based on the first voltage mutual information between each device in the target edge computing node, the second voltage mutual information between the device in the target edge computing node and the device in the adjacent edge computing node, and each device in the target edge computing node, establish a voltage mutual information graph;

[0223] Process the voltage mutual information graph by using a distributed maximum spanning tree algorithm to establish a spanning tree;

[0224] In a case where the spanning tree contains all devices in the voltage mutual information graph, based on the spanning tree and the voltage mutual information graph, obtain a topology identification result.

[0225] In one embodiment, the computer program, when executed by the processor, also implements the following steps:

[0226] For each device, select voltage mutual information meeting a preset condition from the voltage mutual information graph as a candidate edge;

[0227] In a case where the two devices connected by the candidate edge belong to the same range of the target edge computing node, add the candidate edge to a preset candidate edge list, and establish a spanning tree by using the candidate edge list;

[0228] In a case where the two devices connected by the candidate edge do not belong to the same range of the target edge computing node, coordinate and update the spanning tree by using the interaction information between the target edge computing node and the adjacent edge computing node.

[0229] In one embodiment, the computer program, when executed by the processor, also implements the following steps:

[0230] In a case of first selecting the candidate edge, select the voltage mutual information with the largest voltage mutual information from the voltage mutual information graph as the candidate edge;

[0231] In the case of non-first selection of the candidate edge, a maximum mutual information edge collaborative search algorithm is used to select a candidate edge meeting a cutoff condition from the voltage mutual information graph; the cutoff condition includes that the candidate edge is not in the spanning tree, the spanning tree is still a tree graph after the candidate edge is added, and the candidate edge is the largest voltage mutual information in the voltage mutual information graph.

[0232] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0233] In the case of the spanning tree being radial, it is determined that the spanning tree is the topology identification result;

[0234] In the case of the spanning tree not being radial, a topology identification result is generated based on the spanning tree and the conditional mutual information between adjacent devices in the spanning tree.

[0235] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0236] The conditional mutual information is compared and processed to determine the minimum conditional mutual information from the conditional mutual information;

[0237] The minimum conditional mutual information is broadcast, and a comparison value returned by the other edge computing node is received, and in the case that the comparison value is greater than the minimum conditional mutual information, a loop topology is generated based on the device corresponding to the returned comparison value;

[0238] Based on the spanning tree and the loop topology, a topology identification result is generated.

[0239] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0240] First voltage time series information of each device in the target edge computing node is obtained;

[0241] Second voltage time series information of each device in the adjacent edge computing node is obtained;

[0242] Second voltage mutual information is determined based on the first voltage time series information and the second voltage time series information.

[0243] In one embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the following steps:

[0244] A voltage mutual information graph is established based on first voltage mutual information between each device in the target edge computing node, second voltage mutual information between devices in the target edge computing node and devices in the adjacent edge computing node, and each device in the target edge computing node;

[0245] The voltage mutual information graph is processed by using a distributed maximum spanning tree algorithm to establish a spanning tree;

[0246] In a case where all devices in the voltage mutual information graph are contained in the spanning tree, a topology identification result is obtained based on the spanning tree and the voltage mutual information graph.

[0247] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0248] For each device, a voltage mutual information meeting a preset condition is selected from the voltage mutual information graph as a candidate edge;

[0249] In a case where the two devices connected by the candidate edge belong to a same target edge computing node, the candidate edge is added to a preset candidate edge list, and the spanning tree is established by using the candidate edge list;

[0250] In a case where the two devices connected by the candidate edge do not belong to a same target edge computing node, the spanning tree is updated by using interaction information between the target edge computing node and an adjacent edge computing node.

[0251] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0252] In a case of first selecting the candidate edge, a voltage mutual information with the largest voltage mutual information is selected from the voltage mutual information graph as the candidate edge;

[0253] In a case of not first selecting the candidate edge, the candidate edge meeting a cutoff condition is selected from the voltage mutual information graph by using a maximum mutual information edge collaborative search algorithm; the cutoff condition includes that the candidate edge is not in the spanning tree, the spanning tree is still a tree graph after the candidate edge is added, and the candidate edge is the largest voltage mutual information in the voltage mutual information graph.

[0254] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0255] In a case where the spanning tree is a radial tree, the spanning tree is determined as the topology identification result;

[0256] In a case where the spanning tree is not a radial tree, the topology identification result is generated based on the spanning tree and conditional mutual information between adjacent devices in the spanning tree.

[0257] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0258] The conditional mutual information is compared and processed to determine the minimum conditional mutual information from the conditional mutual information;

[0259] The minimum conditional mutual information is broadcasted, and a comparison value returned by the other edge computing nodes is received, and in a case where the comparison value is greater than the minimum conditional mutual information, a ring topology is generated based on a device corresponding to the returned comparison value;

[0260] Based on the spanning tree and the ring topology, a topology identification result is generated.

[0261] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0262] Obtain first voltage time sequence information of each device in the target edge computing node;

[0263] Obtain second voltage time sequence information of each device in the adjacent edge computing node;

[0264] Determine the second voltage mutual information based on the first voltage time sequence information and the second voltage time sequence information.

[0265] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0266] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0267] The technical features of the above embodiments can be combined arbitrarily. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combinations of the technical features do not exist contradictory, they should be considered as the scope of the present application.

[0268] The above-described embodiments are merely illustrative of several embodiments of the present application, which are described in more detail and in a specific manner, but should not be construed as limiting the scope of the patent of the present application. It should be noted that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for topology identification on the edge side of a distribution network, characterized in that, The method includes: Based on the first voltage mutual information between each device in the target edge computing node, the second voltage mutual information between the device in the target edge computing node and the device in the adjacent edge computing node, and each device in the target edge computing node, a voltage mutual information subgraph is established and stored in each target edge computing node, and the voltage mutual information graph is constructed by combining them. The voltage mutual information graph is processed using the distributed maximum spanning tree algorithm to establish a spanning tree; If the spanning tree contains all devices in the voltage mutual information graph, the topology identification result is obtained based on the spanning tree and the voltage mutual information graph; The process of establishing the voltage mutual information diagram includes: The target edge computing node uses the first voltage mutual information and the second voltage mutual information as edge weights, and the devices in its jurisdiction as vertices, to establish and store the voltage mutual information subgraph. The voltage mutual information subgraph formed by merging the voltage mutual information subgraphs obtained by all edge computing nodes is denoted as G. V ; wherein, the voltage mutual information diagram G V Distributed storage across edge computing nodes: e V =e V (1)∪e V (2)∪…∪e V (N E ) In the formula, This refers to a collection of equipment consisting of transformers, branch boxes, and user meters. ε represents the set of devices within the area covered by the i-th edge computing node; V express The mutual information value ε between each two devices is calculated from voltage timing data. V (i)(i = 1, 2, ..., N) E ) represents the voltage mutual information between the regions covered by the i-th edge computing node and the devices in the regions covered by adjacent edge computing nodes; G V (i) represents the voltage mutual information subgraph stored on the i-th edge computing node; N E This indicates the number of edge computing nodes.

2. The method according to claim 1, characterized in that, The process of using the distributed maximum spanning tree algorithm to process the voltage mutual information graph and establish a spanning tree includes: For each of the aforementioned devices, voltage mutual information that meets preset conditions is selected as candidate edges from the voltage mutual information graph; If the two devices connected by the candidate edge belong to the same target edge computing node, the candidate edge is added to a preset candidate edge list, and the spanning tree is built using the candidate edge list. If the two devices connected by the candidate edge do not belong to the same target edge computing node, the spanning tree is updated in a coordinated manner using the interaction information between the target edge computing node and the adjacent edge computing node.

3. The method according to claim 2, characterized in that, The step of selecting voltage mutual information that meets preset conditions as candidate edges from the voltage mutual information graph includes: In the case of selecting the candidate edge for the first time, the voltage mutual information with the largest voltage mutual information is selected as the candidate edge from the voltage mutual information graph; In cases where the candidate edge is not selected for the first time, the maximum mutual information edge collaborative search algorithm is used to select candidate edges that meet the cutoff conditions from the voltage mutual information graph. The cutoff conditions include: the candidate edge is not in the spanning tree, the spanning tree is still a tree graph after the candidate edge is added, and the candidate edge has the largest voltage mutual information in the voltage mutual information graph.

4. The method according to claim 1, characterized in that, The topology identification result obtained based on the spanning tree and the voltage mutual information graph includes: In the case that the spanning tree is radial, the spanning tree is determined to be the topology identification result; If the spanning tree is not radial, the topology identification result is generated based on the conditional mutual information between the spanning tree and adjacent devices in the spanning tree.

5. The method according to claim 4, characterized in that, The generation of the topology identification result based on the spanning tree and the conditional mutual information between adjacent devices in the spanning tree includes: The conditional mutual information is compared and processed to determine the minimum conditional mutual information. Broadcast the minimum conditional mutual information and receive comparison values ​​returned by other edge computing nodes. If the comparison value is greater than the minimum conditional mutual information, generate a ring topology based on the device corresponding to the returned comparison value. Based on the spanning tree and the loop topology, the topology identification result is generated.

6. The method according to claim 1, characterized in that, The process of determining the second voltage mutual information includes: Obtain the first voltage time series information of each device within the target edge computing node; Obtain the second voltage time series information of each device within adjacent edge computing nodes; The second voltage mutual information is determined based on the first voltage time series information and the second voltage time series information.

7. A distribution network edge-side topology identification device, characterized in that, The device includes: The mutual information graph establishment module is used to establish and store a voltage mutual information subgraph in each target edge computing node based on the first voltage mutual information between each device in the target edge computing node, the second voltage mutual information between the device in the target edge computing node and the device in the adjacent edge computing node, and each device in the target edge computing node, and combine them to establish a voltage mutual information graph. The spanning tree building module is used to process the voltage mutual information graph using the distributed maximum spanning tree algorithm to build a spanning tree; The result determination module is used to obtain a topology identification result based on the spanning tree and the voltage mutual information graph when the spanning tree contains all devices in the voltage mutual information graph; The mutual information graph building module is specifically used by the target edge computing node to build and store the voltage mutual information subgraph based on the first voltage mutual information and the second voltage mutual information as edge weights, and with the devices in its jurisdiction as vertices. The voltage mutual information subgraph formed by merging the voltage mutual information subgraphs obtained by all edge computing nodes is denoted as G. V ; wherein, the voltage mutual information diagram G V Distributed storage across edge computing nodes: e V =e V (1)∪e V (2)∪…∪e V (N E ) In the formula, This refers to a collection of equipment consisting of transformers, branch boxes, and user meters. ε represents the set of devices within the area covered by the i-th edge computing node; V express The mutual information value ε between each two devices is calculated from voltage timing data. V (i)(i = 1, 2, ..., N) E ) represents the voltage mutual information between the regions covered by the i-th edge computing node and the devices in the regions covered by adjacent edge computing nodes; G V (i) represents the voltage mutual information subgraph stored on the i-th edge computing node; N E This indicates the number of edge computing nodes.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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