Transformer area topology identification method and system capable of being deployed in concentrator, equipment and medium

Abstract

The invention discloses a transformer area topology identification method and system capable of being deployed in a concentrator, equipment and a medium, and the method comprises the steps: solving an initial virtual impedance matrix based on historical electrical quantity data of a transformer area, and taking the minimum deviation between an optimization result and the initial matrix as an optimization target; and taking symmetry constraint, boundary constraint and collinear constraint as constraint conditions, and performing iterative solution to obtain an optimized virtual impedance matrix, so that the optimized virtual impedance matrix is closest to an original matrix on the basis of conforming to the actual topology, noise interference can be eliminated, the topology identification precision is improved, the method can dynamically adapt to a transformer area change scene, and the method is suitable for being applied to a transformer area. The method has high robustness, the unstructured virtual impedance matrix can be fitted into a unique solution conforming to physical topology, fuzziness of a traditional method is avoided, the whole recognition process only depends on historical electrical quantity data of the transformer area, no initial topology information is needed, and the high-precision transformer area topology can be constructed from zero.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of transformer area topology identification, in particular, to a transformer area topology identification method and system deployable on a concentrator, an electronic device and a computer readable storage medium. BACKGROUND

[0002] With the large number of new loads such as distributed photovoltaic and electric vehicle charging being connected to low-voltage distribution transformer areas, the traditional transformer area topology identification methods relying on manual survey or prior structural information have been difficult to meet the requirements of automation and real-time operation management. The current transformer area topology identification methods mainly include clustering analysis or virtual impedance modeling, but these methods are difficult to achieve high-precision topology identification from zero. For example, the article "Low-voltage distribution network topology identification method based on virtual impedance" uses the historical power and current data of the electric energy meter to calculate the virtual impedance matrix through multivariate linear regression, but it still uses a fixed threshold to determine the node connection relationship, which lacks deep modeling of the topology structure between nodes. When there are factors such as meter error, user fluctuation and line impedance change, it is easy to cause identification deviation or fail to restore the true topology, and the robustness of the algorithm is poor. SUMMARY

[0003] The present application provides a transformer area topology identification method and system deployable on a concentrator, an electronic device and a computer readable storage medium, which can fit the unstructured virtual impedance matrix into a unique solution conforming to the physical topology, realize high-precision transformer area topology from zero, and dynamically adapt to the changing scenarios of transformer areas, with strong robustness.

[0004] According to one aspect of the present application, a transformer area topology identification method deployable on a concentrator is provided, which includes the following contents:

[0005] An initial virtual impedance matrix of the transformer area is solved based on the historical electrical quantity data of the transformer area;

[0006] A target function is constructed with the minimum deviation between the optimized virtual impedance matrix and the initial virtual impedance matrix as the optimization objective, and symmetry constraint, boundary constraint and collinearity constraint as the constraint conditions;

[0007] The target function is iteratively solved to obtain the optimized virtual impedance matrix, and the transformer area topology structure is determined based on the optimized virtual impedance matrix.

[0008] Further, the expression of the target function is:

[0009]

[0010] Wherein, n represents the number of nodes of the transformer area, C ij(i = 1, 2, …, n; j = 1, 2, …, n) represents the virtual impedance of node j to node i in the initial virtual impedance matrix, represents the virtual impedance of node j to node i in the optimized virtual impedance.

[0011] Further, the symmetry constraint refers to the virtual impedance of node j to node i being consistent with the virtual impedance of node i to node j under the premise of the power factor being 1, and the optimized virtual impedance matrix being a symmetric matrix.

[0012] Further, the boundary constraint refers to the number of user nodes changing during the operation of the transformer area, resulting in the actual number of nodes of the transformer area being inconsistent with the row and column number of the initial virtual impedance matrix, and if a virtual impedance value is greater than a preset threshold value, the row and column corresponding to the virtual impedance value are deleted in the initial virtual impedance matrix.

[0013] Further, the collinearity constraint refers to, for any three nodes in the transformer area, if the three nodes are not connected to the same node, then the virtual impedance of two nodes to the third node is equal, and the virtual impedance between the two nodes is greater than the virtual impedance between the two nodes and the third node.

[0014] Further, if the three nodes are connected to the same node, then the virtual impedance between the three nodes is equal.

[0015] Further, a particle swarm optimization algorithm is used to iteratively solve the objective function, wherein the solving process includes the following contents:

[0016] Initialize the particle swarm, each particle in the particle swarm represents a possible optimized target matrix, the position vector of the particle represents the upper triangular elements of the optimized target matrix, and the velocity vector of the particle represents the change amount of the position vector;

[0017] Take the objective function as the fitness function to calculate the fitness value of each particle, and obtain the individual optimal and global optimal;

[0018] Iterate, each particle updates the speed according to the speed update formula and updates the position according to the position update formula, calculates the new fitness value after each iteration, and if the new fitness value is better than the individual optimal, the individual optimal is updated, and if the fitness value of a particle is better than the global optimal, the global optimal is updated, and the iteration is continuously performed until the iteration termination condition is met, and the final optimized target matrix is obtained.

[0019] In addition, the present application also provides a transformer area topology identification system deployable in a concentrator, comprising:

[0020] A virtual impedance solving module is configured to solve the initial virtual impedance matrix of the transformer area based on the historical electrical quantity data of the transformer area.

[0021] a target function construction module, configured to construct a target function with the minimum deviation between the optimized virtual impedance matrix and the initial virtual impedance matrix as an optimization target, and with symmetry constraint, boundary constraint and collinearity constraint as constraint conditions;

[0022] a transformer area topology identification module, configured to iteratively solve the target function to obtain the optimized virtual impedance matrix, and determine the transformer area topology structure based on the optimized virtual impedance matrix.

[0023] In addition, the present application also provides an electronic device comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to execute the steps of the method described above by invoking the computer program stored in the memory.

[0024] In addition, the present application also provides a computer readable storage medium for storing a computer program which can be deployed on a concentrator to identify transformer area topology, wherein the computer program executes the steps of the method described above when running on a computer.

[0025] The present application has the following advantages:

[0026] The transformer area topology identification method which can be deployed on a concentrator according to the present application, after obtaining the initial virtual impedance matrix of the transformer area based on the historical electrical quantity data of the transformer area, constructs a target function with the minimum deviation between the optimization result and the initial virtual impedance matrix as an optimization target, and with symmetry constraint, boundary constraint and collinearity constraint as constraint conditions, and obtains the optimized virtual impedance matrix after iterative solving, so that the optimized virtual impedance matrix is closest to the original matrix on the basis of conforming to the actual topology, and the three constraint conditions can eliminate noise interference (such as meter error, impedance time variation, etc.), improve the accuracy of topology identification, and automatically eliminate abnormal nodes (such as user increase or decrease), which can dynamically adapt to transformer area change scenarios and has strong robustness. Combined with iterative optimization and constraint conditions, the unstructured virtual impedance matrix can be fitted into a unique solution conforming to the physical topology, avoiding the ambiguity of traditional methods. In addition, the entire identification process only relies on the historical electrical quantity data of the transformer area, without any initial topology information, and can realize the construction of high-precision transformer area topology from zero.

[0027] In addition, the transformer area topology identification system which can be deployed on a concentrator according to the present application also has the above advantages.

[0028] In addition to the purposes, features and advantages described above, the present application has other purposes, features and advantages. The present application will be further described in detail below with reference to the drawings. BRIEF DESCRIPTION OF DRAWINGS

[0029] The accompanying drawings, which form a part of this application, are included to provide a further understanding of the application, illustrate the preferred embodiment of the application and assist in

[0030] Figure 1 is a flowchart of the method for identifying the feeder topology deployable in the concentrator according to the preferred embodiment of the present application;

[0031] Figure 2 is a schematic diagram of the virtual impedance satisfying the symmetry constraint according to the preferred embodiment of the present application;

[0032] Figure 3 is a schematic diagram of the new node m added to the branch of the old node j to form a three-node topology according to the preferred embodiment of the present application;

[0033] Figure 4 is a schematic diagram of the new node m added to the trunk of the old node i and the old node j to form a three-node topology according to the preferred embodiment of the present application;

[0034] Figure 5 is a schematic diagram of the new node m added to the branch of the old node i to form a three-node topology according to the preferred embodiment of the present application;

[0035] Figure 6 is a schematic diagram of the new node m connected to the same node as the old node i and the old node j to form a three-node topology according to the preferred embodiment of the present application;

[0036] Figure 7 is a schematic diagram of the method for identifying the feeder topology deployable in the concentrator according to another embodiment of the present application; Figure 1 is a sub-flowchart of step S3 in the method for identifying the feeder topology deployable in the concentrator according to the preferred embodiment of the present application;

[0037] Figure 8 is a schematic diagram of the module structure of the feeder topology identification system deployable in the concentrator according to another embodiment of the present application. DETAILED DESCRIPTION

[0038] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0039] With reference to Figure 1 , the preferred embodiment of the present application provides a method for identifying the feeder topology deployable in the concentrator, which includes the following contents:

[0040] Step S1: obtaining the initial virtual impedance matrix of the feeder based on the historical electrical quantity data of the feeder;

[0041] Step S2: constructing a target function with the minimum deviation between the optimized virtual impedance matrix and the initial virtual impedance matrix as the optimization objective, and taking the symmetry constraint, boundary constraint and collinearity constraint as the constraint conditions;

[0042] Step S3: iteratively solving the target function to obtain the optimized virtual impedance matrix, and determining the transformer area topology based on the optimized virtual impedance matrix.

[0043] It can be understood that the transformer area topology identification method deployable to the concentrator in the embodiment, after obtaining the initial virtual impedance matrix of the transformer area based on the historical electrical quantity data of the transformer area, a target function is constructed with the minimum deviation between the optimization result and the initial virtual impedance matrix as the optimization objective, and the symmetry constraint, boundary constraint and collinearity constraint are taken as the constraint conditions, and the optimized virtual impedance matrix is obtained after iterative solving, so that the optimized virtual impedance matrix is closest to the original matrix on the basis of conforming to the actual topology, and the three constraint conditions can eliminate noise interference (such as meter error, impedance time variation, etc.), improve the accuracy of topology identification, and automatically eliminate abnormal nodes (such as user increase or decrease), which can dynamically adapt to the transformer area change scene and has strong robustness. Combined with iterative optimization and constraint conditions, the unstructured virtual impedance matrix can be fitted into a unique solution conforming to the physical topology, avoiding the ambiguity of the traditional method. In addition, the entire identification process only relies on the historical electrical quantity data of the transformer area, without any initial topology information, and can realize the construction of high-precision transformer area topology from zero.

[0044] In the step S1, based on the historical measurement data of the electric energy meter aggregated by the low-voltage transformer area concentrator, an initial virtual impedance matrix between the nodes of the transformer area can be constructed. Specifically, in the low-voltage transformer area, there is a virtual impedance between any two nodes, and the virtual impedance can be solved by the historical electrical quantity data of the electric energy meter aggregated by the concentrator through a multivariate linear regression equation to obtain the virtual impedance value between each node. The specific calculation formula is:

[0045]

[0046] wherein P1 represents the power at the outlet end of the concentrator monitoring meter, P i represents the power at the outlet end of the electric energy meter of node i in the transformer area, n represents the number of nodes in the transformer area, C ij (i = 1, 2, …, n; j = 1, 2, …, n) represents the coefficient of the current of node j in the regression result with the power of node i as the target, i.e. the virtual impedance of node j to node i, I iThe current at the outlet end of the electric energy meter of node i is represented, and the n*n coefficient matrix obtained by solving is the initial virtual impedance matrix of the transformer area. Generally, the smaller the virtual impedance between two nodes, the closer the connection between them, and the larger the virtual impedance, the weaker or non-existent the connection between the two nodes. In addition, the specific process of solving by multivariate linear regression equation belongs to the prior art, and can be referred to the article "Low-voltage distribution network topology identification method based on virtual impedance", which will not be repeated here.

[0047] In the step S2, a target function is constructed with the minimum deviation between the optimized virtual impedance matrix (i.e. the optimization result) and the initial virtual impedance matrix as the optimization objective, and the symmetry constraint, the boundary constraint and the collinearity constraint as the constraint conditions. The new optimized virtual impedance matrix that meets the constraint conditions can be calculated under the premise of minimizing the deviation as much as possible. Optionally, the target function needs to be able to express the deviation between the optimization result and the original virtual impedance matrix, so the Frobenius norm function is used to construct the target function, wherein the expression of the target function is:

[0048]

[0049] Wherein, n represents the number of nodes of the transformer area, C ij (i = 1, 2, …, n; j = 1, 2, …, n) represents the virtual impedance of node j to node i in the initial virtual impedance matrix, represents the virtual impedance of node j to node i in the optimized virtual impedance. Of course, in other embodiments of the present application, other functions (such as mean square error function, cross-entropy loss function, etc.) can also be used to construct the target function.

[0050] It can be understood that the present application takes the minimum deviation between the optimization result and the initial virtual impedance matrix as the optimization objective, and uses the Frobenius norm function to construct the target function, so that the optimized virtual impedance matrix is closest to the original matrix on the basis of meeting the actual topology.

[0051] Optionally, the present application considers that the virtual impedance matrix is evolved from the actual topology data, which is constrained by the actual topology graph parameters, so the present application adds the symmetry constraint, the boundary constraint and the collinearity constraint as the three constraint conditions of the target function.

[0052] Wherein, the symmetry constraint means that under the premise that the power factor is 1, the virtual impedance of node j to node i is consistent with the virtual impedance of node i to node j, and the optimized virtual impedance matrix is a symmetric matrix. Specifically, according to the definition of virtual impedance, C ijis equal to the product of the impedance value of each section upstream of the upstream intersection of the two nodes and the square of the reciprocal of the power factor, and the power factor is generally taken as 1, so the virtual impedance is the impedance value upstream of the intersection of the two nodes, and C ij and the upstream node of C ji is consistent, as shown in Figure 2 , the virtual impedance matrix obtained by optimization solution is a symmetric matrix, that is,

[0053] In addition, the boundary constraint refers to that during the operation of the low-voltage distribution network in the transformer area, the number of user nodes will change, resulting in that the actual number of nodes in the transformer area is inconsistent with the row and column number of the initial virtual impedance matrix, that is, part of the nodes do not belong to the transformer area, and the rows and columns of these nodes in the matrix need to be deleted, therefore, if a virtual impedance value is greater than a preset threshold value, it means that the electrical distance between the node and the transformer of the transformer area is too far, and it should belong to the adjacent transformer area, so the row and column corresponding to the virtual impedance value are deleted in the initial virtual impedance matrix.

[0054] In addition, the collinear constraint refers to the constraint relationship of any three nodes in the transformer area, specifically, for any three nodes in the transformer area, if the three nodes are not connected to the same node, then the virtual impedance of two nodes to the third node is equal, and the virtual impedance between the two nodes is greater than the virtual impedance between the two nodes and the third node. The virtual impedance values between nodes have mutual constraint relationship, for example, assuming a simplest three-node topology, nodes i and j form a fixed topology, and a new node m is added, if the new node m is added to the branch of node j, as shown in Figure 3 , at this time, two nodes A and B will appear in the topology, B is farther away from the electrical distance of the transformer of the transformer area, then there will be , that is, the virtual impedance of node i to node m is equal to the virtual impedance of node i to node j, and is less than the virtual impedance of node j to node m. If the new node m is added to the main road, as shown in Figure 4 , then there will be , that is, the virtual impedance of node i to node m is equal to the virtual impedance of node j to node m, and is less than the virtual impedance of node i to node j. If the new node m is added to the branch of node i, as shown in Figure 5 , then there will be , that is, the virtual impedance of node i to node j is equal to the virtual impedance of node j to node m, and is less than the virtual impedance of node i to node m.

[0055] In addition, there is a special case, that is, nodes A and B coincide, that is, nodes i, j and m are connected to the same node, as shown in Figure 6 , the virtual impedance between any two nodes in the three nodes is equal, that is,

[0056] It can be understood that the application can effectively eliminate noise interference such as meter error, impedance time variation, etc. through the above three constraints, improve the accuracy of topology identification, automatically eliminate abnormal nodes (such as user increase or decrease), dynamically adapt to the changing scenarios of the transformer area, and have strong robustness.

[0057] In addition, in the step S3, the particle swarm optimization algorithm is preferably used to iteratively solve the objective function. The particle swarm optimization algorithm (PSO) is a numerical optimization algorithm based on swarm intelligence proposed by Kennedy and Eberhart in 1995. The algorithm idea is derived from the study of the hunting behavior of bird flocks. Each "particle" in the search space represents a potential solution in the optimization space. All particles have an adaptive value determined by the objective function. The optimal solution is obtained by continuously optimizing the iterative updates of the position and speed of a single particle. The algorithm has the advantages of fast convergence speed and strong robustness, and is suitable for various complex optimization problems, including nonlinear, multimodal and non-differentiable optimization problems. In the application, the Frobenius norm is used as the fitness function to describe the matrix with the minimum error. The function can evaluate the advantages and disadvantages of each particle in the solution space, guide and evaluate the search process and performance of the particle population, and is determined by the optimization objective function. In the particle swarm algorithm, the position of each particle represents a set of solutions, and the fitness function calculates an adaptive value according to the set of solutions, which is used to measure the advantages and disadvantages of the solution. In this paper, the optimal matrix is obtained by matrix difference calculation, so the fitness function of the particle is the Frobenius norm, which is defined as the square root of the sum of the squares of all elements in the matrix, representing the distance between the optimized virtual impedance matrix and the initial virtual impedance matrix. The optimization goal is to make the matrix closest to the original matrix on the basis of meeting the actual topology.

[0058] As shown in Figure 7 The process of iteratively solving the objective function by the particle swarm optimization algorithm includes the following contents:

[0059] Step S31: initialize the particle swarm, each particle in the particle swarm represents a possible optimization target matrix, the position vector of the particle represents the upper triangular elements of the optimization target matrix, and the speed vector of the particle represents the change amount of the position vector;

[0060] Step S32: take the objective function as the fitness function to calculate the fitness value of each particle, and obtain the individual optimal and global optimal;

[0061] Step S33: iteration is performed, each particle updates the speed according to the speed update formula and updates the position according to the position update formula, a new fitness value is calculated after each iteration, if the new fitness value is better than the individual optimal value, the individual optimal value is updated, if the fitness value of a particle is better than the global optimal value, the global optimal value is updated, iteration is continuously performed until the iteration termination condition is met, and the final optimization target matrix is obtained.

[0062] Specifically, step S2 has defined the target function S, an n*n matrix C is input, and an n*n symmetric matrix C* is output. First, the position and speed of each particle in the particle swarm are randomly initialized, each particle in the particle swarm represents a possible optimization target matrix C*, the position vector of the particle represents the upper triangular elements of the optimization target matrix C*, because the optimization target matrix C* is a symmetric matrix, the lower triangular elements thereof can be derived from the upper triangular elements, and the speed vector of the particle represents the change amount of the position vector. Then, the target function S is taken as the fitness function, the fitness value of each particle is calculated, and the initial individual optimal value and the global optimal value are obtained. Then, iteration is performed, each particle updates the speed according to the speed update formula and updates the position according to the position update formula, wherein the speed update formula and the position update formula belong to the prior art and will not be described here. After each iteration, a new fitness value is calculated, if the new fitness value is better than the individual optimal value, the individual optimal value is updated, if the fitness value of a particle is better than the global optimal value, the global optimal value is updated, iteration is continuously performed until the iteration termination condition is met, for example, the maximum number of iterations is reached and / or the fitness value meets the threshold condition, and the final optimization target matrix is obtained.

[0063] It can be understood that the particle swarm algorithm is used to iteratively optimize the target function, the unstructured virtual impedance matrix can be fitted into a unique solution conforming to the physical topology, the ambiguity of the traditional method is avoided, and the optimized virtual impedance matrix is closest to the original matrix on the basis of conforming to the actual topology. In addition, the entire identification process only relies on the historical electrical quantity data of the transformer area, without any initial topology information, and a high-precision transformer area topology can be constructed from zero.

[0064] In addition, the transformer area topology identification algorithm of the present application can be packaged as an independent module and embedded in the existing concentrator platform in the form of an app, combined with the edge computing capability thereof, to realize online and adaptive topology identification of the transformer area.

[0065] In addition, as Figure 8 indicated, another embodiment of the present application also provides a transformer area topology identification system deployable in a concentrator, which preferably adopts the transformer area topology identification method deployable in a concentrator as described above, and comprises:

[0066] a virtual impedance solving module, configured to solve an initial virtual impedance matrix of the transformer area based on historical electrical quantity data of the transformer area;

[0067] a target function construction module, configured to construct a target function with the minimum deviation between the optimized virtual impedance matrix and the initial virtual impedance matrix as an optimization target, and with the symmetry constraint, the boundary constraint and the collinearity constraint as constraint conditions;

[0068] a transformer area topology identification module, configured to iteratively solve the target function to obtain the optimized virtual impedance matrix, and determine the transformer area topology structure based on the optimized virtual impedance matrix.

[0069] It can be understood that the transformer area topology identification system deployable on the concentrator in the embodiment, after obtaining the initial virtual impedance matrix of the transformer area based on the historical electrical quantity data of the transformer area, constructs a target function with the minimum deviation between the optimization result and the initial virtual impedance matrix as an optimization target, and with the symmetry constraint, the boundary constraint and the collinearity constraint as constraint conditions, and obtains the optimized virtual impedance matrix after iterative solving, so that the optimized virtual impedance matrix is closest to the original matrix on the basis of conforming to the actual topology, and the three constraint conditions can eliminate noise interference (such as meter error, impedance time variation, etc.), improve the accuracy of topology identification, and automatically eliminate abnormal nodes (such as user increase or decrease), can dynamically adapt to transformer area change scenarios, has strong robustness, and can fit the unstructured virtual impedance matrix into a unique solution conforming to the physical topology by combining iterative optimization and constraint conditions, avoid the ambiguity of the traditional method, and in addition, the entire identification process only relies on the historical electrical quantity data of the transformer area, without any initial topology information, and can realize the construction of high-precision transformer area topology from zero.

[0070] In addition, another embodiment of the present application further provides an electronic device comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to execute the steps of the method described above by invoking the computer program stored in the memory.

[0071] In addition, another embodiment of the present application further provides a computer readable storage medium for storing a computer program for identifying transformer area topology deployable on a concentrator, wherein the computer program executes the steps of the method described above when running on a computer.

[0072] Generally, computer readable storage media includes any media that can be accessed by a computer. By way of example, and not limitation, computer readable storage media can comprise storage media such as RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other storage medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, or twisted pair, then the coaxial cable, fiber optic cable, or twisted pair are included in the definition of medium. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, Blu-ray® disc, and floppy disk used to store software.

[0073] Those skilled in the art will appreciate that embodiments of the present application can be devised for a method, a system, or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer readable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code thereon for use by or in connection with an instruction execution system. Program code embodied on one or more computer readable storage media can be transmitted using any

[0074] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. Figure 1 The flowchart and / or block diagram in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart and / or block diagrams can represent a module, segment, or portion of code, which comprises one or more executable Figure 1 The means can be hardware means or software means. The hardware means can be for example an apparatus or a circuitry of an apparatus. The software means can comprise code to be executed by a processor or a controller of the apparatus.

[0075] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0076] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0077] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0078] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

[0079] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for identifying a substation topology that can be deployed on a concentrator, characterized in that: Includes the following: The initial virtual impedance matrix of the substation is obtained based on the historical electrical quantity data of the substation; The objective function is constructed with the minimum deviation between the optimized virtual impedance matrix and the initial virtual impedance matrix as the optimization goal, and symmetry constraints, boundary constraints and collinearity constraints are used as constraints. The objective function is iteratively solved to obtain the optimized virtual impedance matrix, and the substation topology is determined based on the optimized virtual impedance matrix.

2. The method for identifying a substation topology that can be deployed in a concentrator according to claim 1, wherein: The expression of the objective function is: Among them, n represents the number of nodes in the area, C ij (i=1,2,…,n;j=1,2,…,n) represents the virtual impedance of node j to node i in the initial virtual impedance matrix, represents the virtual impedance of node j to node i in the optimized virtual impedance.

3. The method for identifying a substation topology that can be deployed in a concentrator according to claim 2, wherein: The symmetry constraint means that under the premise of a power factor of 1, the virtual impedance of node j to node i is consistent with the virtual impedance of node i to node j, and the optimized virtual impedance matrix is ​​a symmetric matrix.

4. The method for identifying a substation topology that can be deployed in a concentrator according to claim 2, wherein: The boundary constraint means that the number of user nodes will change during the operation of the substation, resulting in inconsistency between the actual number of nodes in the substation and the number of rows and columns of the initial virtual impedance matrix. If a virtual impedance value is greater than a preset threshold, the row and column corresponding to the virtual impedance value will be deleted from the initial virtual impedance matrix.

5. The method for identifying a substation topology that can be deployed in a concentrator according to claim 2, wherein: The collinearity constraint means that for any three nodes in the substation, if the three nodes are not connected to the same node, the virtual impedance of two of the nodes to the third node is equal, and the virtual impedance between the two nodes is greater than the virtual impedance between the two nodes and the third node.

6. The method for identifying a substation topology that can be deployed in a concentrator according to claim 5, wherein: If the three nodes are connected to the same node, the virtual impedances between the three nodes are equal.

7. The method for identifying a substation topology that can be deployed in a concentrator according to claim 1, wherein: The particle swarm optimization algorithm is used to iteratively solve the objective function, where the solution process includes the following: Initialize the particle swarm. Each particle in the particle swarm represents a possible optimization target matrix. The particle's position vector represents the upper triangular element of the optimization target matrix, and the particle's velocity vector represents the change in the position vector. Using the objective function as the fitness function, the fitness value of each particle is calculated to obtain the optimal individual initialization and the global optimal value. Iterate, each particle updates its speed according to the speed update formula and its position according to the position update formula. After each iteration, a new fitness value is calculated. If the new fitness value is better than the individual optimal value, the individual optimal value is updated. If the fitness value of a particle is better than the global optimal value, the global optimal value is updated. Iterate continuously until the iteration termination condition is met and the final optimization target matrix is ​​obtained.

8. A substation topology identification system that can be deployed on a concentrator, characterized in that: include: The virtual impedance solution module is used to obtain the initial virtual impedance matrix of the substation based on the historical electrical quantity data of the substation; An objective function construction module is used to construct an objective function with the minimum deviation between the optimized virtual impedance matrix and the initial virtual impedance matrix as the optimization goal, and with symmetry constraints, boundary constraints and collinearity constraints as constraints; The substation topology identification module is used to iteratively solve the objective function to obtain the optimized virtual impedance matrix, and determine the substation topology structure based on the optimized virtual impedance matrix.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program, and the processor is configured to execute the steps of the method according to any one of claims 1 to 7 by calling the computer program stored in the memory.

10. A computer-readable storage medium for storing a computer program that can be deployed in a concentrator to perform area topology identification, characterized in that: When the computer program is run on a computer, the steps of the method according to any one of claims 1 to 7 are executed.

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