A Method and System for Identifying Distribution Network Topology Errors Based on Section Switch Operation Diagrams

Abstract

This invention discloses a method and system for identifying distribution network topology errors based on section switch operation diagrams, belonging to the field of distribution network topology technology. The method for identifying power distribution network topology errors based on section switch operation diagrams involves constructing a section model, processing relevant power grid data to obtain a distribution network section switch diagram, and then using a distribution network section processing model to process the section switch diagram to obtain a distribution network optimization model for topology error identification. By solving the distribution network optimization model, the optimal result of the section switch operation diagram is output, and the identification of topology errors in distribution networks containing distributed power sources is completed. The solution is scientific, reasonable, and feasible. Furthermore, this invention utilizes section switch operation diagrams and distribution network optimization models to identify topology errors in distribution networks containing distributed power sources, effectively reducing the computational load of large-scale distribution networks, and offering high solution efficiency, making it easy to promote and implement on a large scale.

Description

Technical Field

[0001] This invention relates to a method and system for identifying distribution network topology errors based on section switch operation diagrams, belonging to the field of distribution network topology technology. Background Technology

[0002] Network topology is a necessary condition for power grid analysis, forecasting, and control. Numerous scholars both domestically and internationally have studied the problem of identifying errors in the main and distribution network topology. Emerging data-driven methods, on the other hand, utilize certain correlations to solve unknown data based on known data. This falls under the category of computational problems from data to data / model. Some data-driven methods still require the establishment of physical models, including the need to establish physical models for solving data during the data processing process.

[0003] Traditional network topology error identification methods typically involve first establishing a complete physical model, then using given boundary conditions and cross-sectional data to numerically calculate or optimize the solution to obtain the network topology identification result—essentially a computational problem from model to data.

[0004] Existing technologies achieve topology identification through feature information, which requires additional hardware or field-generated signals, making large-scale implementation difficult.

[0005] Existing technologies also rely on AMI and PMU measurement algorithms or linear power flow algorithms based on node power and branch power matching to achieve topology identification; however, AMI and PMU measurement algorithms require high investment, while linear power flow algorithms have approximation errors in their models; furthermore, the above solutions involve large computational loads and reduced solution efficiency for large-scale distribution networks, which is not conducive to widespread application. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the first objective of this invention is to provide a method for identifying distribution network topology errors based on segment switch operation diagrams. This method involves constructing a segment model, processing relevant power grid data to obtain a distribution network segment switch diagram, using a distribution network segment processing model to further process the switch diagram, and obtaining a distribution network optimization model for topology error identification. By solving the distribution network optimization model, the optimal result of the segment switch operation diagram is output, and the identification of topology errors in distribution networks containing distributed generation sources is completed. This method is scientific, reasonable, and feasible.

[0007] The second objective of this invention is to provide a distribution network topology error identification system based on section switch operation diagrams. This system is achieved by setting up a data acquisition module, a section switch operation diagram module, a topology error identification and optimization module, and an identification output module. The system processes the section switch diagram of the distribution network to obtain a distribution network optimization model for topology error identification. By solving the distribution network optimization model, the optimal result of the section switch operation diagram is output, and the identification of topology errors in the distribution network containing distributed power sources is completed. This system is scientific, reasonable, and feasible.

[0008] The third objective of this invention is to provide a method and system for identifying topology errors in distribution networks containing distributed power sources by utilizing the section switch operation diagram and the distribution network optimization model. This method effectively reduces the computational load of large-scale distribution networks, has high solution efficiency, is easy to promote and use, and is convenient for large-scale implementation.

[0009] To achieve one of the above objectives, the first technical solution of the present invention is as follows:

[0010] A method for identifying power distribution network topology errors based on section switch operation diagrams

[0011] Includes the following steps:

[0012] The first step is to obtain relevant power grid data for distribution networks containing distributed generation sources;

[0013] The second step involves processing the power grid-related data from the first step using a pre-built section model to obtain the distribution network section switch diagram.

[0014] The third step is to use the pre-built distribution network segment processing model to process the distribution network segment switching diagram in the second step, and obtain the distribution network optimization model for topology error identification.

[0015] The fourth step is to solve the distribution network optimization model from the third step, output the optimal result of the section switch operation diagram, and complete the identification of topology errors in the distribution network containing distributed generation.

[0016] Through continuous exploration and experimentation, this invention constructs a segment model, processes relevant power grid data, and obtains a distribution network segment switching diagram. Using the distribution network segment processing model, the switching diagram is further processed to obtain a distribution network optimization model for topology error identification. By solving the distribution network optimization model, the optimal result of the segment switching operation diagram is output, and the identification of topology errors in distribution networks containing distributed generation sources is completed. The solution is scientific, reasonable, and feasible.

[0017] Furthermore, this invention utilizes the section switch operation diagram and distribution network optimization model to identify topological errors in distribution networks containing distributed power sources, effectively reducing the computational load of large-scale distribution networks, and has high solution efficiency, making it easy to promote and use, and facilitating large-scale implementation.

[0018] As a preferred technical measure:

[0019] In the first step, the power grid related data includes at least power grid switch data and / or power grid node data and / or conductor segment and cable segment data and / or line model information and / or distribution transformer data and / or distribution transformer daily power consumption data.

[0020] As a preferred technical measure:

[0021] Power grid switch data, including device ID, description information, and connection relationships;

[0022] Power grid node data, including line ID, pole ID, and pole name;

[0023] Conductor segment and cable segment data, including basic information and connection information such as line segment ID, line segment name, line ID, connecting rod ID, line model, and length;

[0024] Line model information, including current carrying capacity, resistance information, and reactance information;

[0025] Distribution transformer data, including transformer ID, name, capacity, connecting rod ID, and transformer model;

[0026] Daily power consumption data for distribution transformers includes line code, distribution transformer ID, and daily power consumption information.

[0027] As a preferred technical measure:

[0028] In the second step, the method for constructing the segment model is as follows:

[0029] Step 21: Based on the connection point and conductor / cable segment data, merge the connection points connected by branches into one segment;

[0030] Step 22: Using the segments from Step 21 as vertices and the switches as edges, construct the segment switch graph:

[0031] Step 23: Use the wide-range probing method to analyze the section switch diagram in step 22 to obtain the connecting branches and single connecting branch circuits of the section switch diagram.

[0032] Step 24: Based on the switch information, the switch is identified as closed, thereby merging the connection points connected to the switch via the connecting branches and single connecting branch circuits in step 23 into one operating section;

[0033] Step 25: Using the running section in Step 24 as the vertex and the closed switch as the edge, construct the section switch running graph to complete the construction of the section model.

[0034] As a preferred technical measure:

[0035] The method for constructing the distribution network segment processing model in the third step is as follows:

[0036] Step 31: Based on the obtained section switch operation diagram, establish the active power flow balance equation and constraint relationship for each section;

[0037] Step 32: Determine the radial constraints based on the active power balance equations and constraint relationships from Step 31;

[0038] The radial constraints include connectivity constraints and branch-node number constraints.

[0039] Step 33: Based on the radial constraints in Step 32, establish the objective function for topology error identification:

[0040] The objective function for topology error identification is to minimize the sum of the weighted minimum absolute values ​​of the switch state adjustment and the measurement error.

[0041] Step 34: Add slack variables to the objective function in step 33 to obtain a relaxed objective function, which is used to eliminate the absolute value in the objective function;

[0042] Step 35: Using the remote signaling of switch status as a measurement, add the objective of minimizing the switch status adjustment amount to the relaxation objective function in step 34 to obtain the distribution network optimization model for topology error identification, thus completing the construction of the distribution network segment processing model.

[0043] As a preferred technical measure:

[0044] The expressions for the active power flow balance equations for each section are as follows:

[0045]

[0046] In the formula, P Gi and P Li These represent the active power output and active power demand of segment i, respectively.

[0047] P ij For the active power flow of segment i;

[0048] The constraint relationship is the constraint relationship between the active power flow and the switch state of each switch, and its expression is as follows:

[0049] -MS ij ≤P ij ≤MS ij

[0050] In the formula, M is a sufficiently large positive number.

[0051] As a preferred technical measure:

[0052] The connectivity constraint is a virtual requirement of one unit for each charged node, expressed as follows:

[0053]

[0054] Among them, f ij Let f be the virtual traffic flowing through branch ij, and f ji =-f ij ;

[0055] The expression for the branch-node number constraint is as follows:

[0056]

[0057] Where, N n N represents the total number of nodes in the distribution network. e This represents the number of power supply nodes in the substation.

[0058] As a preferred technical measure:

[0059] Measurements are used for power system state estimation, and their equations are as follows:

[0060]

[0061] In the formula: x is an n-dimensional state vector; z is an m-dimensional measurement vector; h(x) is a measurement function; r is a measurement error vector; g(x) is an l-dimensional function vector;

[0062] The formula for calculating the weighted minimum absolute value is as follows:

[0063]

[0064] str = zh(x)

[0065] g(x) = 0

[0066] In the formula, r i These are elements in the measurement error vector r;

[0067] σ i The standard deviation of the element;

[0068] The formula for calculating the relaxation objective function is as follows:

[0069]

[0070] stl-u=zh(x)l≥0,u≥0

[0071] g(x) = 0

[0072] In the formula: l is the slack variable of the m-dimensional measurement itself;

[0073] u is a slack variable represented by the m-dimensional measurement equation;

[0074] l i The elements in the slack variable l;

[0075] u i The elements in the slack variable u;

[0076] The calculation formula for the distribution network optimization model used for topology error identification is as follows:

[0077]

[0078] In the formula: The switch indicates that the remote signaling status is 0. W indicates that the remote signaling state is closed. sw This represents the penalty weight for errors in remote signaling status.

[0079] To achieve one of the above objectives, the second technical solution of the present invention is as follows:

[0080] A method for identifying power distribution network topology errors based on section switch operation diagrams includes the following:

[0081] Obtain grid-related data including distributed generation power distribution networks;

[0082] By processing the power grid-related data through a pre-built section model, the section switch operation diagram and operation branch sub-diagram are obtained;

[0083] Using a pre-built distribution network segment processing model, the distribution network segment switch diagram is processed to obtain a distribution network optimization model for topology error identification;

[0084] Solve the distribution network optimization model, output the optimal results of the section switch operation diagram, and complete the identification of topology errors in the distribution network containing distributed generation.

[0085] Through continuous exploration and experimentation, this invention constructs a segment model, processes relevant power grid data, and obtains a distribution network segment switching diagram. Using the distribution network segment processing model, the switching diagram is further processed to obtain a distribution network optimization model for topology error identification. By solving the distribution network optimization model, the optimal result of the segment switching operation diagram is output, and the identification of topology errors in distribution networks containing distributed generation sources is completed. The solution is scientific, reasonable, and feasible.

[0086] Furthermore, this invention utilizes the section switch operation diagram and distribution network optimization model to identify topological errors in distribution networks containing distributed power sources, effectively reducing the computational load of large-scale distribution networks, and has high solution efficiency, making it easy to promote and use, and facilitating large-scale implementation.

[0087] To achieve one of the above objectives, the third technical solution of the present invention is as follows:

[0088] The system for identifying topology errors in a power distribution network with distributed power sources based on the section switch operation diagram adopts the aforementioned method for identifying topology errors in a power distribution network based on the section switch operation diagram. It includes a data acquisition module, a section switch operation diagram module, a topology error identification optimization module, and an identification output module.

[0089] The data acquisition module is used to acquire grid-related data, including those related to distributed power generation networks.

[0090] The section switch operation diagram module is used to process power grid-related data to obtain the distribution network section switch diagram;

[0091] The topology error identification and optimization module is used to process the switch diagram of the distribution network section to obtain the distribution network optimization model for topology error identification.

[0092] The identification output module is used to solve the distribution network optimization model and output the topology error identification results.

[0093] Through continuous exploration and experimentation, this invention sets up a data acquisition module, a section switch operation diagram module, a topology error identification and optimization module, and an identification output module. It processes the distribution network section switch diagram to obtain a distribution network optimization model for topology error identification. By solving the distribution network optimization model, it outputs the optimal result of the section switch operation diagram and completes the identification of topology errors in the distribution network containing distributed power sources. The solution is scientific, reasonable, and feasible.

[0094] Furthermore, this invention utilizes the section switch operation diagram and distribution network optimization model to identify topological errors in distribution networks containing distributed power sources, effectively reducing the computational load of large-scale distribution networks, and has high solution efficiency, making it easy to promote and use, and facilitating large-scale implementation.

[0095] Compared with the prior art, the present invention has the following beneficial effects:

[0096] Through continuous exploration and experimentation, this invention constructs a segment model, processes relevant power grid data, and obtains a distribution network segment switching diagram. Using the distribution network segment processing model, the switching diagram is further processed to obtain a distribution network optimization model for topology error identification. By solving the distribution network optimization model, the optimal result of the segment switching operation diagram is output, and the identification of topology errors in distribution networks containing distributed generation sources is completed. The solution is scientific, reasonable, and feasible.

[0097] Through continuous exploration and experimentation, this invention sets up a data acquisition module, a section switch operation diagram module, a topology error identification and optimization module, and an identification output module. It processes the distribution network section switch diagram to obtain a distribution network optimization model for topology error identification. By solving the distribution network optimization model, it outputs the optimal result of the section switch operation diagram and completes the identification of topology errors in the distribution network containing distributed power sources. The solution is scientific, reasonable, and feasible.

[0098] Furthermore, this invention utilizes the section switch operation diagram and distribution network optimization model to identify topological errors in distribution networks containing distributed power sources, effectively reducing the computational load of large-scale distribution networks, and has high solution efficiency, making it easy to promote and use, and facilitating large-scale implementation. Attached Figure Description

[0099] Figure 1 This is a flowchart of a power distribution network topology error identification method according to the present invention;

[0100] Figure 2 This is a structural diagram illustrating one aspect of the tree branches and connecting branches of the present invention;

[0101] Figure 3 This is a ring-shaped branch diagram of the present invention. Detailed Implementation

[0102] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0103] Conversely, this invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of the invention as defined in the claims. Furthermore, to provide a better understanding of the invention, certain specific details are described in detail below. However, those skilled in the art will fully understand the invention even without these detailed descriptions.

[0104] Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention.

[0105] like Figure 1 As shown, this invention provides a first specific embodiment of the power distribution network topology error identification method based on section switch operation diagrams:

[0106] The power distribution network topology error identification method based on the section switch operation diagram includes the following steps:

[0107] The first step is to obtain relevant power grid data for distribution networks containing distributed generation sources;

[0108] The second step involves processing the power grid-related data from the first step using a pre-built section model to obtain the distribution network section switch diagram.

[0109] The third step is to use the pre-built distribution network segment processing model to process the distribution network segment switching diagram in the second step, and obtain the distribution network optimization model for topology error identification.

[0110] The fourth step is to solve the distribution network optimization model from the third step, output the optimal result of the section switch operation diagram, and complete the identification of topology errors in the distribution network containing distributed generation.

[0111] A second specific embodiment of the power distribution network topology error identification method based on section switch operation diagram of the present invention:

[0112] The power distribution network topology error identification method based on section switch operation diagrams includes the following:

[0113] Obtain grid-related data including distributed generation power distribution networks;

[0114] By processing the power grid-related data through a pre-built section model, the section switch operation diagram and operation branch sub-diagram are obtained;

[0115] Using a pre-built distribution network segment processing model, the distribution network segment switch diagram is processed to obtain a distribution network optimization model for topology error identification;

[0116] Solve the distribution network optimization model, output the optimal results of the section switch operation diagram, and complete the identification of topology errors in the distribution network containing distributed generation.

[0117] A specific embodiment of the section switch operation diagram of the present invention:

[0118] The section switch operation diagram mathematically describes the connectivity of the tree. Based on the operating characteristics of the distribution network, every load point needs to be connected to a power source point of a substation, ensuring a path between any energized node and a power source point of a substation. Virtual flow is defined; in each ring-shaped branch subgraph, one vertex is arbitrarily selected as the source point, and the other points are load points. The virtual load demand of each load point is defined as 1, meaning the virtual flow balance constraint of each load point can be described as:

[0119]

[0120] In the formula, N i F represents the set of vertices adjacent to vertex i; ij This represents the virtual flow passing through the switch between vertex i and vertex j.

[0121] For any circular branch subgraph, the relationship between the number of vertices and edges satisfies n = m - 1, and the number of closed switches is equal to the number of vertices minus 1, i.e.

[0122] ∑S ij =N-1.

[0123] A third specific embodiment of the power distribution network topology error identification method based on section switch operation diagram of the present invention:

[0124] A method for identifying power distribution network topology errors based on section switch operation diagrams includes the following:

[0125] The first step is to read in the distribution network model and construct a tree-structured section switch operation diagram. This mainly includes the following sub-steps:

[0126] A. Obtain the power distribution network model and related data:

[0127] 1) Read in the distribution network switch model: including basic information such as device ID, description, and connection relationship.

[0128] 2) Read in the distribution network node model: including basic information such as line ID, pole ID, and pole name.

[0129] 3) Read in conductor segment and cable segment data: This mainly includes basic information and connection information such as line segment ID, line segment name, line ID, connecting rod ID, line model, and length.

[0130] 4) Read in the line model information: including current carrying capacity, resistance, reactance and other information.

[0131] 5) Read in transformer data: including transformer ID, name, capacity, connecting rod ID, and transformer model.

[0132] 6) Read in the daily power consumption data of the distribution transformer: including information such as line code, distribution transformer ID, and daily power consumption.

[0133] B forms the section switch operation diagram and operation branch sub-diagram:

[0134] 1) Based on the data of connection points, conductor segments, cable segments, etc., merge the connection points connected by branches into one section.

[0135] 2) Based on the switch information, it is assumed that the knife switch is closed, thus merging the connection points connected by the branch and the knife switch into one section.

[0136] 3) Construct a segment-switch graph with segments as vertices and switches as edges:

[0137] 4) Construct a segment switch operation diagram with the segment as the vertex and the switch in the closed state as the edge.

[0138] 5) For a radially operating distribution network, the section switch operation diagram of each power source point will form a tree. By merging the power source sections of the distribution network into a single vertex, the entire section switch operation diagram will constitute a tree.

[0139] 6) By using the wide-range probing method to analyze the section switch diagram of the distribution network, a tree structure can be obtained on the diagram, along with the connecting branches and single-branch circuits. (See [reference needed]). Figure 2 .

[0140] 7) The switching states of a radial structure are reliable. A connected region formed by loops sharing a common edge is defined as a loop branch subgraph; see [reference needed]. Figure 3 .

[0141] The second step is to establish a mathematical model based on the obtained section switch operation diagram. This mainly includes:

[0142] 1) Establish the active balance equation and constraint relationships:

[0143] The active power flow balance equation for each section can be described as follows:

[0144]

[0145] In the formula, P Gi and P Li These represent the active power output and active power demand of segment i, respectively.

[0146] The constraint relationship between the active power flow and the switch state of each switch can be described as follows:

[0147] -MS ij ≤P ij ≤MS ij

[0148] In the formula, M is a sufficiently large positive number.

[0149] 2) Establish radial constraints

[0150] a. Connectivity constraints

[0151] In addition to the substation power supply nodes, each energized node requires one unit of virtual demand:

[0152]

[0153] Among them, f ij Let f be the virtual traffic flowing through branch ij, and f ji =-f ij .

[0154] b. Branch-Node Number Constraints

[0155]

[0156] Where, N n N represents the total number of nodes in the distribution network. e This represents the number of power supply nodes in the substation.

[0157] 3) Establish the objective function:

[0158] The objective of topology error identification can be described as minimizing the sum of the weighted minimum absolute values ​​of switch state adjustment and measurement error.

[0159] The measurement equations for power system state estimation are generally as follows:

[0160]

[0161] In the formula: x is an n-dimensional state vector; z is an m-dimensional measurement vector; h(x) is a measurement function; r is a measurement error vector; g(x) is an l-dimensional function vector.

[0162] Compared to WLS, weighted least absolute value (WLAV) estimation can automatically suppress the influence of bad data during the estimation process, exhibiting better robustness. The WLAV state estimation model is as follows:

[0163]

[0164] str = zh(x)

[0165] g(x) = 0

[0166] To eliminate the absolute value in the objective function, slack variables are added, and their calculation formula is as follows:

[0167]

[0168] stl-u=zh(x)l≥0,u≥0

[0169] g(x) = 0

[0170] In the formula: l and u are both m-dimensional slack variables.

[0171] For the topology error identification problem, considering the remote signaling of switch states as a measurement, an objective of minimizing the switch state adjustment needs to be added to the objective function. The calculation formula is as follows:

[0172]

[0173] In the formula: The switch indicates that the remote signaling status is 0. W indicates that the remote signaling state is closed. sw This represents the penalty weight for errors in remote signaling status.

[0174] The third step is to solve the problem.

[0175] 1) The mathematical model is solved by weighted least absolute value (WLAV) estimation.

[0176] 2) Switches with adjusted remote signaling status outputs are the topology error identification results, see Table 1 and Table 2.

[0177] This invention proposes a topology error identification method for power distribution networks based on section switch operation diagrams. This method addresses the structural characteristics of distribution networks—"closed-loop design, open-loop operation"—by defining the concept of sections and using section switch operation diagrams to describe the radial operating structure of the distribution network. A topology error identification method for distribution networks with distributed power sources is designed to adapt to this scenario. This method defines the concept of "virtual flow" to describe the connectivity between any energized node and a power node in a substation. Furthermore, the constraint that the number of closed branches equals the total number of nodes minus the number of power nodes in the substation ensures the radial structure of the distribution network. An optimization model for topology error identification and measurement errors is established, aiming to minimize the sum of the weighted minimum absolute values ​​of switch state adjustments and measurement errors. The weighted minimum absolute value method is used for power grid topology error identification. This method uses an integer linear optimization model to describe the actual topological connection relationship of the distribution network and employs a weighted minimum absolute value method to solve the problem. The objective is to minimize the sum of the weighted minimum absolute values ​​of switch state adjustment and measurement error. By solving the actual topological state of the distribution network under the current state and comparing it with the current real-time topology, topological error identification can be achieved.

[0178] A specific embodiment of the present invention is a topology error identification system for distribution networks with distributed power sources based on section switch operation diagrams:

[0179] The system for identifying topology errors in a power distribution network with distributed power sources based on a section switch operation diagram adopts the aforementioned method for identifying topology errors in a power distribution network based on a section switch operation diagram. It includes a data acquisition module, a section switch operation diagram module, a topology error identification optimization module, and an identification output module.

[0180] The data acquisition module is used to acquire grid-related data, including those related to distributed power generation networks.

[0181] The section switch operation diagram module is used to process power grid-related data to obtain the distribution network section switch diagram;

[0182] The topology error identification and optimization module is used to process the switch diagram of the distribution network section to obtain the distribution network optimization model for topology error identification.

[0183] The identification output module is used to solve the distribution network optimization model and output the topology error identification results.

[0184] A specific embodiment of the single-branch circuit of the present invention:

[0185] Figure 2 The node labeled 'a' represents the starting node of the network search, and the node labeled 'bn' represents the node numbered 'n' in the second level of the search. The numbering of other nodes follows the same meaning. In the diagram, the thick black lines represent tree branches, and the thin dashed lines represent connecting branches. l1-l7 represent the seven connecting branches in the network, and each connecting branch can form a unique single-branch loop with a tree branch.

[0186] A specific embodiment of the ring branch of the present invention:

[0187] Obviously, if the radial branch between node b4 and node c8 is broken, node c8 becomes an isolated node, and in topology error identification, its switching state in the radial structure can be considered reliable. A connected region formed by loops sharing a common edge is defined as a branch subgraph. For example, loops l1, l2, l4, l5, and l6 form a large ring-shaped branch subgraph because they share a common edge. Figure 3 As shown.

[0188] A specific embodiment of the present invention is as follows:

[0189] Taking the SCADA measurement section and power measurement data of a local power grid in July 2021 as an example, this invention identifies bad data in distribution network topology measurement based on the method of this invention. The model includes 2 power sources, 3 power source sections, 3 optimized closed switches, 545 model states, 150 measurements, 153 nodes, and 482 equality constraints. The model for regional topology error identification includes 15 variables, 2 equality constraints, 12 function inequality constraints, and 14 variable inequality constraints. The entire optimization calculation took 0.84 seconds. Specific calculation results are shown in Tables 1 and 2 below.

[0190] Table 1. Topology error identification results

[0191] Topology error switch name Measurement status Identify state Time elapsed (seconds) European and American Plaza Special Transformer Switch point combine European and American H0014 switches point combine Financial H0134 switch point combine 0.84

[0192] Table 2 Results of Defective Data Identification

[0193] Number of bad data Total error Maximum weighted residual Time elapsed (seconds) 14 30.7155 2.0089 0.018

[0194] An embodiment of a device applying the method of the present invention:

[0195] A computer device comprising:

[0196] One or more processors;

[0197] Storage device for storing one or more programs;

[0198] When the one or more programs are executed by the one or more processors, the one or more processors implement the above-described method for identifying power distribution network topology errors based on section switch operation diagrams.

[0199] An embodiment of a computer medium applying the method of the present invention:

[0200] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for identifying power distribution network topology errors based on section switch operation diagrams.

[0201] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0202] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0203] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0204] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1The steps of the function specified in one or more boxes.

[0205] 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 identifying power distribution network topology errors based on section switch operation diagrams. Its features are, Includes the following steps: The first step is to obtain relevant power grid data for distribution networks containing distributed generation sources; The second step involves processing the power grid-related data from the first step using a pre-built section model to obtain the distribution network section switch diagram. The method for constructing the section model is as follows: Step 21: Based on the connection point and conductor / cable segment data, merge the connection points connected by branches into one segment; Step 22: Using the segments from Step 21 as vertices and the switches as edges, construct the segment switch graph: Step 23: Use the wide-range probing method to analyze the section switch diagram in step 22 to obtain the connecting branches and single connecting branch circuits of the section switch diagram. Step 24: Based on the switch information, the switch is identified as closed, thereby merging the connection points connected to the switch via the connecting branches and single connecting branch circuits in step 23 into one operating section; Step 25: Using the running section in Step 24 as the vertex and the closed switch as the edge, construct the section switch running graph to complete the construction of the section model; The third step is to use the pre-built distribution network segment processing model to process the distribution network segment switching diagram in the second step, and obtain the distribution network optimization model for topology error identification. The method for constructing a distribution network section processing model is as follows: Step 31: Based on the obtained section switch operation diagram, establish the active power flow balance equation and constraint relationship for each section; Step 32: Determine the radial constraints based on the active power balance equations and constraint relationships from Step 31; The radial constraints include connectivity constraints and branch-node number constraints. Step 33: Based on the radial constraints in Step 32, establish the objective function for topology error identification: The objective function for topology error identification is to minimize the sum of the weighted minimum absolute values ​​of the switch state adjustment and the measurement error. Step 34: Add slack variables to the objective function in step 33 to obtain a relaxed objective function, which is used to eliminate the absolute value in the objective function; Step 35: Using the remote signaling of switch status as a measurement, add the objective of minimizing the switch status adjustment amount to the relaxation objective function in step 34 to obtain the distribution network optimization model for topology error identification, and complete the construction of the distribution network section processing model. The fourth step is to solve the distribution network optimization model from the third step, output the optimal result of the section switch operation diagram, and complete the identification of topology errors in the distribution network containing distributed generation.

2. The power distribution network topology error identification method based on section switch operation diagram as described in claim 1, characterized in that, In the first step, the power grid related data includes at least power grid switch data and / or power grid node data and / or conductor segment and cable segment data and / or line model information and / or distribution transformer data and / or distribution transformer daily power consumption data.

3. The power distribution network topology error identification method based on section switch operation diagram as described in claim 2, characterized in that, Power grid switch data, including device ID, description information, and connection relationships; Power grid node data, including line ID, pole ID, and pole name; Conductor segment and cable segment data, including basic information and connection information such as line segment ID, line segment name, line ID, connecting rod ID, line model, and length; Line model information, including current carrying capacity, resistance information, and reactance information; Distribution transformer data, including transformer ID, name, capacity, connecting rod ID, and transformer model; Daily power consumption data for distribution transformers includes line code, distribution transformer ID, and daily power consumption information.

4. The power distribution network topology error identification method based on section switch operation diagram as described in claim 1, characterized in that, The expressions for the active power flow balance equations for each section are as follows: In the formula, and Sections The active power output of the power supply and the active power demand of the load; P ij For section The meritorious trend; The constraint relationship is the constraint relationship between the active power flow and the switch state of each switch, and its expression is as follows: In the formula, It is a sufficiently large positive number.

5. The power distribution network topology error identification method based on section switch operation diagram as described in claim 4, characterized in that, The connectivity constraint is a virtual requirement of one unit for each charged node, expressed as follows: (1) in, branch road Virtual traffic flowing through, and has ; The expression for the branch-node number constraint is as follows: (2) in, The total number of distribution network nodes. This represents the number of power supply nodes in the substation.

6. The power distribution network topology error identification method based on section switch operation diagram as described in claim 5, characterized in that, Measurements are used for power system state estimation, and their equations are as follows: In the formula: for 3D state vector; for Dimensional measurement vector; For measurement functions; This is the measurement error vector; for dimensional function vector; The formula for calculating the weighted minimum absolute value is as follows: In the formula, r i Measurement error vector Elements in; σ i The standard deviation of the elements; The formula for calculating the relaxation objective function is as follows: In the formula: for The slack variables of the measurement itself; for The slack variables represented by the dimensional measurement equation; l i For slack variables l Elements in; u i For slack variables u Elements in; The calculation formula for the distribution network optimization model used for topology error identification is as follows: In the formula: The switch indicates that the remote signaling status is 0. This indicates that the switch is in the closed state for remote signaling. This represents the penalty weight for errors in remote signaling status.

7. A topology error identification system for distribution networks with distributed generation based on section switch operation diagrams, characterized in that, The power distribution network topology error identification method based on the section switch operation diagram as described in any one of claims 1-6 includes a data acquisition module, a section switch operation diagram module, a topology error identification optimization module, and an identification output module. The data acquisition module is used to acquire grid-related data, including those related to distributed power generation networks. The section switch operation diagram module is used to process power grid-related data to obtain the distribution network section switch diagram; The topology error identification and optimization module is used to process the switch diagram of the distribution network section to obtain the distribution network optimization model for topology error identification. The identification output module is used to solve the distribution network optimization model and output the topology error identification results.

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