Power distribution network topology identification method and device

Abstract

The invention discloses a power distribution network topology identification method and device, and the method comprises the steps: obtaining the original measurement information of a power distribution network; based on the original measurement information, constructing a mixed integer nonlinear programming topology measurement identification model based on a branch current method; utilizing the target data physical fusion driving linearization model to perform linearization processing on a nonlinear constraint and measurement equation to be used in the topology measurement identification model of the mixed integer nonlinear programming based on the branch current method to obtain a processing result; and constructing a topology measurement identification model of mixed integer linearization programming based on a branch current method by using a processing result so as to output a topology identification result of the power distribution network. Therefore, the problems that in the related technology, the efficiency of maintaining the topology file based on a manual and data statistics method is low, a topology error identification model based on an optimization class method is poor in adaptability and low in accuracy, the accuracy of topology identification is reduced, and the operation stability of a power distribution network is reduced are solved.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network topology identification, and in particular to a distribution network topology identification method and device. Background Art

[0002] In related technologies, topology archives can be maintained through manual and data statistical methods, and based on set theory and optimization methods, the power and voltage measurement of the distribution network can be used to solve the problem of frequent topology changes and untimely topology updates caused by load transfer, grid planning and construction during the operation of the distribution network, which leads to large errors in subsequent advanced application analysis.

[0003] However, the maintenance efficiency of topology archives based on manual and data statistics methods in related technologies is low, and the topology error identification model based on optimization methods has poor adaptability and low accuracy, which reduces the accuracy of topology identification and the timeliness and stability of distribution network operation, which needs to be solved urgently. Summary of the invention

[0004] This application is based on the following problems and understandings made by the inventor:

[0005] During the operation of the distribution network, the network topology of the distribution network often changes due to reasons such as new energy grid connection, load transfer, and network reconstruction. In addition, due to various interferences in the acquisition and communication transmission of primary and secondary equipment, the telesignaling may malfunction and jitter, and the telesignaling information presented in the business system may be wrong. At the same time, the real-time telesignaling information of the distribution network itself is not enough. The maintenance of traditional topology archives depends on manual maintenance. When the switch status changes, the information cannot be updated in time. Even some switches are not equipped with acquisition devices, and the switch telesignaling information cannot be obtained. The known network topology may have matching errors compared to the actual situation.

[0006] In actual engineering, the changes in topological structure are not constantly changing compared to operating electrical quantities such as current and voltage. However, once a topological maintenance error occurs, the impact is more serious, which can lead to serious deviations in state estimation results or even non-convergence. Traditional methods based on manual maintenance are inefficient, and topological error identification models based on optimization methods have poor adaptability and low efficiency, which need to be improved urgently.

[0007] The present application provides a distribution network topology identification method and device to solve the problems in the related art that the maintenance of topology archives based on manual and data statistics methods is inefficient, and the topology error identification model based on optimization methods has poor adaptability and low accuracy, which reduces the accuracy of topology identification and reduces the timeliness and stability of distribution network operation.

[0008] The first aspect of the present application provides a method for identifying the topology of a distribution network, including the following steps: obtaining the original measurement information of the distribution network; constructing a topology measurement identification model of mixed-integer nonlinear programming based on the branch current method based on the original measurement information; using the target data physical fusion-driven linearization model to linearize the nonlinear constraints and measurement equations to be used in the topology measurement identification model of mixed-integer nonlinear programming based on the branch current method, obtaining a processing result, and using the processing result to construct a topology measurement identification model of mixed-integer linear programming based on the branch current method to output the topology identification result of the distribution network.

[0009] Optionally, in an embodiment of the present application, the nonlinear constraints and measurement equations to be used include zero-injection active power constraints, zero-injection reactive power constraints, node voltage amplitude measurement equations, node injection power measurement equations, and branch power measurement equations;

[0010] Among them, the zero-injection active power constraint and the zero-injection reactive power constraint are respectively:

[0011]

[0012]

[0013] Among them, U i represents the voltage amplitude of node i, U j represents the voltage amplitude of node j, θ ij represents the phase angle difference between node i and node j, G ij , B ij respectively represent the real part and the imaginary part of the element in the i-th row and the j-th column of the node admittance matrix, representing conductance and susceptance respectively;

[0014] The node voltage amplitude measurement equation is:

[0015]

[0016] Among them, represents the voltage amplitude measurement of node i, U i represents the voltage amplitude of node i, represents the voltage measurement error;

[0017] The node injection power measurement equation is:

[0018]

[0019]

[0020] Among them, P i m , are the corresponding measured values; P i , Q i respectively represent the unknowns of the active and reactive injection powers at node i; G ij , B ij respectively represent the real part and the imaginary part of the element in the i-th row and the j-th column of the node admittance matrix, representing conductance and susceptance respectively; respectively represent the measurement errors of the active and reactive injection powers at the nodes;

[0021] The measurement equation of the branch power is:

[0022]

[0023]

[0024]

[0025]

[0026] wherein, respectively correspond to the measured values of the active and reactive powers of the head-end branch, with the direction flowing out from node i and flowing into node j; respectively correspond to the measured values of the active and reactive powers of the tail-end branch, with the direction flowing out from node j and flowing into node i; y0 represents the susceptance of the compensating capacitor; P ij , Q ij respectively represent the unknowns of the active and reactive powers of the head-end of the branch, with the direction flowing from node i to node j; P ji , Q ji respectively represent the unknowns of the active and reactive powers of the tail-end of the branch, with the direction flowing from node j to node i; g ij , b ij respectively represent the conductance and susceptance of the branch where node i and node j are located; respectively represent the measurement errors of the active and reactive powers of the head-end branch and the active and reactive powers of the tail-end branch.

[0027] Optionally, in an embodiment of the present application, linearizing the nonlinear constraints and measurement equations in the topological measurement identification model of the mixed-integer nonlinear programming based on the branch current method to obtain a processing result, including: obtaining the basic principle of linear approximation according to the actual topological structure and operating characteristics of the distribution network; linearizing the nonlinear constraints and measurement equations in the topological measurement identification model of the mixed-integer nonlinear programming based on the branch current method according to the basic principle of linear approximation to obtain a physical linearization expression.

[0028] Optionally, in an embodiment of the present application, the basic principle of the linear approximation is that the phase difference approximation requirement is satisfied between the head node and the end node of the line when the voltage amplitudes of all nodes reach the preset voltage reference range.

[0029] Optionally, in an embodiment of the present application, the physical linearization expression is the physical linearization expression of the linearized measurement equation and the linearized zero-injection equality constraint. Among them, the linearized measurement equation includes the physical linearization expression of the node injection power and the physical linearization expression of the branch power. Among them, the physical linearization expression of the node injection power is:

[0030]

[0031] The physical linearization expression of the branch power:

[0032]

[0033]

[0034] The physical linearization expression of the zero-injection equality constraint is:

[0035]

[0036] Optionally, in an embodiment of the present application, constructing the topological measurement identification model based on the branch current method by using the processing result includes: obtaining an objective function according to the linearized measurement equation, and using the objective function to determine the objective solution with the minimum residual between the branch current variable and the branch current measurement value; based on the minimum objective solution and the objective data physical fusion-driven linearization model, constructing the topological measurement identification model based on the branch current method of mixed-integer linear programming.

[0037] Optionally, in an embodiment of the present application, the objective data physical fusion-driven linearization model includes the data physical fusion-driven linearization expression of the node injection power, the data physical fusion-driven linearization expression of the branch power, and the data physical fusion-driven linearization expression of the zero-injection equality constraint. Among them, the data physical fusion-driven linearization expression of the node injection power is:

[0038]

[0039] Among them, P i 、Q i respectively represent the unknown quantities of the active and reactive injection powers of node i, and ΔP i and ΔQ i both represent the fitting errors obtained based on data driving, and are obtained by partial least squares regression fitting;

[0040] The data - physical fusion - driven linearized expression of the branch power is as follows:

[0041]

[0042]

[0043] Where P ij and Q ij respectively represent the quantities to be solved for the active and reactive powers at the head of the branch, with the direction flowing from node i to node j; P ji and Q ji respectively represent the quantities to be solved for the active and reactive powers at the end of the branch, with the direction flowing from node j to node i; g ij and b ij respectively represent the conductance and susceptance of the branch where nodes i and j are located; θ ji represents the phase - angle difference between nodes j and i; ΔP ij and ΔQ ij and ΔP ji and ΔQ ji all represent the fitting errors obtained based on data - driven, and are obtained by partial least - squares regression fitting;

[0044] The data - physical fusion - driven linearized expression of the zero - injection equality constraint is as follows:

[0045]

[0046] Optionally, in an embodiment of the present application, the topological measurement identification model of the mixed - integer linear programming based on the branch - current method is as follows:

[0047]

[0048] Where I ij represents the branch - current variable of the branch where nodes i and j are located; represents the measured value of the branch current of the branch where nodes i and j are located; Γ represents the set of branch - current measurements; I i represents the injected current at node i; represents the set of all nodes, represents the set of nodes connected to node i; s ij represents the state of the switch of the branch where nodes i and j are located. When it is connected, s ij = 1, and when it is disconnected, s ij = 0; g ij and b ij respectively represent the conductance and susceptance of the branch where nodes i and j are located; U i represents the voltage amplitude at node i; U jDenote the voltage magnitude of node j; P i and Q i respectively denote the active and reactive injection power quantities to be solved for node i, with the direction flowing from node i to node j; G ij and B ij respectively denote the real and imaginary parts of the elements in the i-th row and j-th column of the node admittance matrix, representing conductance and susceptance respectively; θ ij denotes the phase angle difference between node i and node j; ΔP i and ΔQ i both denote the fitting errors obtained based on data-driven.

[0049] Optionally, in an embodiment of the present application, the original measurement information includes branch current, telemetry information of load power measurement data, and telecommunication information of switch status.

[0050] An embodiment of the second aspect of the present application provides a distribution network topology identification device, including: an acquisition module for acquiring the original measurement information of the distribution network; a construction module for constructing a topology measurement identification model of mixed integer nonlinear programming based on the branch current method based on the original measurement information; a processing module for linearizing the nonlinear constraints and measurement equations to be used in the topology measurement identification model of mixed integer nonlinear programming based on the branch current method by using a target data physical fusion-driven linearization model, obtaining a processing result, and constructing a topology measurement identification model of mixed integer linear programming based on the branch current method by using the processing result to output the topology identification result of the distribution network.

[0051] Optionally, in an embodiment of the present application, the nonlinear constraints and measurement equations to be used include zero injection active power constraint, zero injection reactive power constraint, measurement equation of node voltage magnitude, measurement equation of node injection power, and measurement equation of branch power;

[0052] Among them, the zero injection active power constraint and zero injection reactive power constraint are respectively:

[0053]

[0054]

[0055] Among them, U i denotes the voltage magnitude of node i, U j denotes the voltage magnitude of node j, θ ij denotes the phase angle difference between node i and node j, G ij , B ij respectively denote the real and imaginary parts of the elements in the i-th row and j-th column of the node admittance matrix, representing conductance and susceptance respectively;

[0056] The measurement equation for the magnitude of the node voltage is as follows:

[0057]

[0058] Where, represents the measurement of the voltage magnitude at node i, U i represents the voltage magnitude at node i, represents the voltage measurement error;

[0059] The measurement equation for the injected power at the node is as follows:

[0060]

[0061]

[0062] Where, P i m and are the corresponding measured values respectively; P i and Q i represent the unknown quantities of the active and reactive injected powers at node i respectively; G ij and B ij represent the real part and the imaginary part of the element in the i-th row and j-th column of the node admittance matrix, representing conductance and susceptance respectively; represent the measurement errors of the active and reactive injected powers at the node respectively;

[0063] The measurement equation for the branch power is as follows:

[0064]

[0065]

[0066]

[0067]

[0068] Where, correspond to the measured values of the active and reactive powers at the head end of the branch respectively, with the direction flowing out from node i and flowing into node j; correspond to the measured values of the active and reactive powers at the tail end of the branch respectively, with the direction flowing out from node j and flowing into node i; y0 represents the susceptance of the compensating capacitor; P ij and Q ij represent the unknown quantities of the active and reactive powers at the head end of the branch respectively, with the direction flowing from node i to node j; P ji and Q ji represent the unknown quantities of the active and reactive powers at the tail end of the branch respectively, with the direction flowing from node j to node i; g ij and b ijRespectively represent the conductance and susceptance of the branch where node i and node j are located; They respectively represent the measurement errors of the active power and reactive power of the head-end branch and the active power and reactive power of the terminal branch.

[0069] Optionally, in one embodiment of the present application, the processing module includes: a first acquisition unit, used to acquire the basic principles of linear approximation based on the actual topological structure and operating characteristics of the distribution network; a second acquisition unit, used to linearize the stand-by nonlinear constraints and measurement equations in the topological measurement identification model of the mixed integer nonlinear programming based on the branch current method according to the basic principles of linear approximation to obtain a physical linearized expression.

[0070] Optionally, in one embodiment of the present application, the basic principle of the linear approximation is that the first node and the last node of the line meet the phase difference approximation requirement when the voltage amplitude of each node reaches a preset voltage reference range.

[0071] Optionally, in one embodiment of the present application, the physical linearization expression is a linearized measurement equation and a physical linearization expression of a linearized zero injection equation constraint, wherein the linearized measurement equation includes a physical linearization expression of a node injection power and a physical linearization expression of a branch power, wherein the physical linearization expression of the node injection power is:

[0072]

[0073] The physical linear expression of the branch power is:

[0074]

[0075] The physical linearization expression of the zero injection equality constraint is:

[0076]

[0077] Optionally, in one embodiment of the present application, the processing module includes: a determination unit, used to obtain an objective function based on the linearized measurement equation, and use the objective function to determine a target solution with the minimum residual between the branch current variable and the branch current measurement value; a construction unit, used to drive the linearization model based on the physical fusion of the minimum target solution and the target data, and construct the topological measurement identification model of the mixed integer linearization programming based on the branch current method.

[0078] Optionally, in an embodiment of the present application, the target data-physical fusion-driven linearization model includes a data-physical fusion-driven linearization expression for node injection power, a data-physical fusion-driven linearization expression for branch power, and a data-physical fusion-driven linearization expression for zero-injection equality constraint. Among them, the data-physical fusion-driven linearization expression for node injection power is:

[0079]

[0080] Among them, P i , Q i respectively represent the unknowns of the active and reactive injection powers of node i. ΔP i and ΔQ i both represent the fitting errors obtained based on data-driven, and are obtained by partial least squares regression fitting;

[0081] The data-physical fusion-driven linearization expression for branch power is:

[0082]

[0083]

[0084] Among them, P ij , Q ij respectively represent the unknowns of the active and reactive powers at the head of the branch, with the direction flowing from node i to node j; P ji , Q ji respectively represent the unknowns of the active and reactive powers at the end of the branch, with the direction flowing from node j to node i; g ij , b ij respectively represent the conductance and susceptance of the branch where node i and node j are located; θ ji represents the phase angle difference between node j and node i; ΔP ij , ΔQ ij , ΔP ji and ΔQ ji both represent the fitting errors obtained based on data-driven, and are obtained by partial least squares regression fitting;

[0085] The data-physical fusion-driven linearization expression for zero-injection equality constraint is:

[0086]

[0087] Optionally, in an embodiment of the present application, the topology measurement identification model of the mixed-integer linear programming based on the branch current method is:

[0088]

[0089] Among them, Iij Denotes the branch current variable where nodes i and j are located; Denotes the measured branch current value where nodes i and j are located; Γ denotes the set of branch current measurements; I i Denotes the injected current of node i; Denotes the set of all nodes, Denotes the set of nodes connected to node i; s ij Denotes the state of the switch of the branch where nodes i and j are located. When it is connected, s ij = 1, and when it is disconnected, s ij = 0; g ij and b ij Respectively denote the conductance and susceptance of the branch where nodes i and j are located; U i Denotes the voltage magnitude of node i; U j Denotes the voltage magnitude of node j; P i and Q i Respectively denote the unknown quantities of the active and reactive injected powers of node i, and the direction is from node i to node j; G ij and B ij Respectively denote the real part and the imaginary part of the elements in the i-th row and the j-th column of the node admittance matrix, and respectively denote the conductance and susceptance; θ ij Denotes the phase angle difference between nodes i and j; ΔP i and ΔQ i Both denote the fitting errors obtained based on data-driven.

[0090] Optionally, in an embodiment of the present application, the original measurement information includes telemetry information of branch current, load power measurement data, and telecontrol information of switch states.

[0091] An embodiment of the third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the program to implement the distribution network topology identification method as described in the above embodiment.

[0092] An embodiment of the fourth aspect of the present application provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program, which when executed by a processor implements the above distribution network topology identification method.

[0093] Embodiments of the present application can obtain the original measurement information of the distribution network to construct a topology measurement identification model based on the branch current method and mixed-integer nonlinear programming. The target data physical fusion-driven linearization model is used to linearize the nonlinear constraints and measurement equations to be used in the topology measurement identification model based on the branch current method and mixed-integer nonlinear programming, so as to construct a topology measurement identification model based on the branch current method and mixed-integer linear programming according to the processing results, thereby outputting the topology identification result of the distribution network, improving the accuracy of topology identification, and improving the timeliness and stability of the operation of the distribution network. Thus, the problems in the related art that the efficiency of maintaining the topology file based on the methods of manual work and data statistics is low, and the adaptability of the topology error identification model based on the optimization methods is poor, and the accuracy is not high, reducing the accuracy of topology identification, and reducing the timeliness and stability of the operation of the distribution network are solved.

[0094] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description of the embodiments in conjunction with the accompanying drawings, where:

[0096] Figure 1 is a flowchart of a method for identifying the topology of a distribution network according to an embodiment of the present application;

[0097] Figure 2 is a flowchart of a method for identifying the topology of a distribution network in a specific embodiment of the present application;

[0098] Figure 3 is a schematic diagram of the topology identification result in a specific embodiment of the present application;

[0099] Figure 4 is a schematic structural diagram of a device for identifying the topology of a distribution network according to an embodiment of the present application;

[0100] Figure 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0101] Embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present application and should not be construed as limiting the present application.

[0102] The method and device for identifying the topology of a distribution network according to the embodiments of the present application will be described below with reference to the accompanying drawings. In view of the problems in the related art mentioned in the above background technology, that is, the method based on manual and data statistics has low efficiency in maintaining the topology file, and the topology error identification model based on the optimization method has poor adaptability and low accuracy, which reduces the accuracy of topology identification and the timeliness of the operation of the distribution network. The present application provides a method for identifying the topology of a distribution network. In this method, the original measurement information of the distribution network can be obtained to construct a topology measurement identification model of mixed integer non-linear programming based on the branch current method, and the target data physical fusion-driven linearization model is used to linearize the non-linear constraints and measurement equations to be used in the topology measurement identification model of mixed integer non-linear programming based on the branch current method, so as to construct a topology measurement identification model of mixed integer linear programming based on the branch current method according to the processing results, thereby outputting the topology identification result of the distribution network, and further improving the accuracy of topology identification, as well as the timeliness and stability of the operation of the distribution network. Thus, the problems in the related art, such as the low efficiency of maintaining the topology file by the method based on manual and data statistics, the poor adaptability and low accuracy of the topology error identification model based on the optimization method, which reduce the accuracy of topology identification and the timeliness and stability of the operation of the distribution network, are solved.

[0103] Specifically, Figure 1 is a schematic flow chart of a method for identifying the topology of a distribution network provided by an embodiment of the present application.

[0104] As Figure 1 shown, the method for identifying the topology of the distribution network includes the following steps:

[0105] In step S101, the original measurement information of the distribution network is obtained.

[0106] It can be understood that the original measurement information of the distribution network in the following steps can be obtained in the embodiments of the present application. For example, in the embodiments of the present application, taking the medium-voltage distribution network as an example, by obtaining the original measurement information of the medium-voltage distribution network, the executability of the topology identification of the distribution network can be effectively improved, and the real-time performance and accuracy of the topology identification of the distribution network can be improved.

[0107] Among them, in an embodiment of the present application, the original measurement information includes telemetry information of branch current and load power measurement data and telecommunication information of switch states.

[0108] In the actual execution process, the original measurement information in the embodiments of the present application includes telemetry information of branch current and load power measurement data and telecommunication information of switch states, which can effectively improve the feasibility of the topology identification of the distribution network.

[0109] In step S102, based on the original measurement information, a topology measurement identification model based on mixed integer nonlinear programming of branch current method is constructed.

[0110] It can be understood that the embodiment of the present application can construct a topology measurement identification model based on the mixed integer nonlinear programming of the branch current method based on the original measurement information, so as to linearize the nonlinear constraints and measurement equations to be used in the following steps, thereby quickly and accurately identifying the distribution network topology errors, and effectively improving the accuracy of the distribution network topology identification.

[0111] In step S103, the target data physical fusion driven linearization model is used to linearize the nonlinear constraints and measurement equations to be used in the topological measurement identification model of the mixed integer nonlinear programming based on the branch current method to obtain the processing results, and the processing results are used to construct the topological measurement identification model of the mixed integer linearization programming based on the branch current method to output the topological identification result of the distribution network.

[0112] It can be understood that the embodiments of the present application can use the target data physical fusion driven linearization model to linearize the nonlinear constraints and measurement equations to be used in the topological measurement identification model of the mixed integer nonlinear programming based on the branch current method in the following steps to obtain processing results, and use the processing results in the following steps to construct a topological measurement identification model based on the mixed integer linearization programming of the branch current method to make full use of the large number of branch current measurements in the current distribution network, and output the topological identification results of the distribution network, thereby achieving efficient and accurate identification of topological error information and improving the safety and stability of distribution network operation.

[0113] In one embodiment of the present application, the target data physical fusion driven linearization model includes a data physical fusion driven linearization expression of node injection power, a data physical fusion driven linearization expression of branch power, and a data physical fusion driven linearization expression of zero injection equality constraint, wherein the data physical fusion driven linearization expression of node injection power is:

[0114]

[0115] Among them, P i , Q i They represent the active and reactive power injection quantities of node i, ΔP i and ΔQ i All represent the fitting errors based on data-driven fitting, obtained by partial least squares regression fitting;

[0116] The data physical fusion driven linearization expression of branch power is:

[0117]

[0118]

[0119] Among them, P ij , Q ij respectively represent the quantities to be solved for the active and reactive powers at the head of the branch, with the direction flowing from node i to node j; P ji , Q ji respectively represent the quantities to be solved for the active and reactive powers at the end of the branch, with the direction flowing from node j to node i; g ij , b ij respectively represent the conductance and susceptance of the branch where node i and node j are located; θ j i represents the phase angle difference between node j and node i; ΔPi j , ΔQ ij , ΔP ji and ΔQ ji all represent the fitting errors obtained based on data-driven, and are obtained by partial least squares regression fitting;

[0120] The data-physical fusion-driven linearization expression of the zero-injection equality constraint is:

[0121]

[0122] Among them, the data-driven principle based on partial least squares regression is as follows:

[0123] ΔZ 1 =C l ·Z l +D l

[0124] ΔZ l =[ΔP ij ΔQ ij ΔP ji ΔQ ji ΔP i ΔQ i

[0125] Z l =[P d Q d T

[0126] Among them, C l and D l both represent the fitting coefficients, and are obtained by partial least squares regression fitting; P d represents the non-zero active power column vector; Q d represents the non-zero reactive power column vector; Z l represents the load active and reactive power vector; ΔZ l represents the linearization compensation error vector.​​

[0127] Further, in an embodiment of the present application, the nonlinear constraints and measurement equations to be used include zero-injection active power constraints, zero-injection reactive power constraints, measurement equations of node voltage magnitudes, measurement equations of node injection powers, and measurement equations of branch powers;

[0128] Among them, the zero-injection active power constraint and the zero-injection reactive power constraint are respectively:

[0129]

[0130]

[0131] Among them, U i represents the voltage magnitude of node i, U j represents the voltage magnitude of node j, θ ij represents the phase angle difference between node i and node j, G ij , B ij respectively represent the real part and the imaginary part of the element in the i-th row and j-th column of the node admittance matrix, representing conductance and susceptance respectively;

[0132] The measurement equation of the node voltage magnitude is:

[0133]

[0134] Among them, represents the measurement of the voltage magnitude of node i, U i represents the voltage magnitude of node i, represents the voltage measurement error;

[0135] The measurement equation of the node injection power is:

[0136]

[0137]

[0138] Among them, P i m , are the corresponding measurement values respectively; P i , Q i respectively represent the unknown quantities of the active and reactive injection powers of node i; G ij , B ij respectively represent the real part and the imaginary part of the element in the i-th row and j-th column of the node admittance matrix, representing conductance and susceptance respectively; respectively represent the measurement errors of the node active and reactive injection powers;

[0139] The measurement equation of the branch power is:

[0140]

[0141]

[0142]

[0143]

[0144] Among them, respectively correspond to the measured values of the active and reactive powers of the head-end branch, with the direction flowing out from node i and flowing into node j; respectively correspond to the measured values of the active and reactive powers of the tail-end branch, with the direction flowing out from node j and flowing into node i; y0 represents the susceptance of the compensating capacitor; P ij and Q ij respectively represent the quantities to be determined of the active and reactive powers at the head end of the branch, with the direction flowing from node i to node j; P ji and Q ji respectively represent the quantities to be determined of the active and reactive powers at the tail end of the branch, with the direction flowing from node j to node i; g ij and b ij respectively represent the conductance and susceptance of the branch where node i and node j are located; respectively represent the measurement errors of the active and reactive powers of the head-end branch and the active and reactive powers of the tail-end branch.

[0145] Among them, in an embodiment of the present application, linearization processing is performed on the nonlinear constraints and measurement equations to be used in the topological measurement identification model of the mixed-integer nonlinear programming based on the branch current method, and the processing result is obtained, including: obtaining the basic principle of linear approximation according to the actual topological structure and operating characteristics of the distribution network; performing linearization processing on the nonlinear constraints and measurement equations to be used in the topological measurement identification model of the mixed-integer nonlinear programming based on the branch current method according to the basic principle of linear approximation to obtain a physical linearization expression.

[0146] In some embodiments, in order to obtain the linearized constraints driven by the physical model, the embodiments of the present application can obtain the basic principle of linear approximation according to the actual topological structure and operating characteristics of the distribution network, where the basic principle of linear approximation is that the head node and the tail node of the line satisfy the phase difference approximation requirement when the voltage amplitudes of each node reach a certain voltage reference range. For example, first, when the distribution network is operating normally, the voltage amplitudes of each node are close to the voltage reference value, and the per-unit value is expressed as:

[0147] U i ≈1.0 p.u.

[0148] Next, the phase difference between the head and tail nodes of the line is small, and the approximate treatment is:

[0149] cosθ ij ≈ 1, sinθ ij ≈ θ ij

[0150] where θ ij = θ i - θ j represents the phase angle difference between node i and node j.

[0151] Furthermore, the embodiment of the present application can linearize the to-be-used non-linear constraints and measurement equations in the topological measurement identification model of the mixed-integer non-linear programming based on the branch current method according to the basic principle of the linear approximation in the above steps to obtain a physical linearization expression, where the physical linearization expression is the measurement equation after linearization, and the physical linearization expression of the zero-injection equality constraint.

[0152] Among them, the measurement equation after linearization includes the physical linearization expression of the node injection power and the physical linearization expression of the branch power. Among them, the physical linearization expression of the node injection power is:

[0153]

[0154] The physical linearization expression of the branch power:

[0155]

[0156]

[0157] The physical linearization expression of the zero-injection equality constraint is:

[0158]

[0159] Thus, the embodiment of the present application can realize the rapid and accurate identification of the topological errors of the distribution network, providing technical support for realizing the precise perception of the distribution network.

[0160] Among them, in an embodiment of the present application, a topological measurement identification model of the mixed-integer linearization programming based on the branch current method is constructed by using the processing result, including: obtaining an objective function according to the measurement equation after linearization, and using the objective function to determine the objective solution with the minimum residual between the branch current variable and the branch current measurement value; driving the linearization model based on the minimum objective solution and the objective data physical fusion to construct a topological measurement identification model of the mixed-integer linearization programming based on the branch current method.

[0161] In the actual implementation process, due to the large number and wide distribution of distribution network points, there are overall problems of insufficient measurement and low quality. However, the branch current measurement accounts for a relatively high proportion. The FTU (Feeder Terminal Unit, distribution switch monitoring terminal) equipped at the feeder outlet and pole-mounted switch, the TTU (distribution Transformer supervisory Terminal Unit, distribution transformer monitoring terminal) equipped on the transformer, etc., can all collect branch current measurements. Therefore, in order to effectively utilize the current measurements in the distribution network, the embodiment of this application proposes a topological measurement identification model based on the mixed-integer linearization programming of the branch current method. Among them, the objective function is as follows:

[0162]

[0163] Among them, I ij represents the branch current variable, represents the branch current measurement value, and Γ represents the set of branch current measurements, that is, corresponding to the switch set with FTU measurements in actual engineering.

[0164] Among them, the physical meaning of the objective function in the above steps is to find the optimal solution (target solution) that satisfies the minimum residual between the current variable and the current measurement value, and the constraints are as follows:

[0165]

[0166] Among them, I i represents the injection current of node i; represents the set of all nodes, represents the set of nodes connected to node i; s ij represents the state of the switch on the branch where node i and node j are located. When it is connected, s ij =1, and when it is disconnected, s ij =0; (I i ) * represents the conjugate of the node injection current I i ; S i represents the load power of node i. Among them, S i =P i +j·Q i , P i , Q i respectively represent the active and reactive injection power quantities to be determined of node i.

[0167] In addition, the node injection power in the above steps contains non-linear terms, that is, it belongs to a mixed-integer non-linear optimization problem. Therefore, in the embodiments of the present application, the data physical fusion-driven linearization model in the above steps can be substituted into the above formula, and a mixed-integer topology error identification model can be constructed. That is, the topology measurement identification model based on mixed-integer linear programming of the branch current method is:

[0168]

[0169] where I ij represents the branch current variable between node i and node j; represents the measured value of the branch current between node i and node j; Γ represents the set of branch current measurements; I i represents the injection current of node i; represents the set of all nodes, represents the set of nodes connected to node i; s ij represents the state of the switch on the branch between node i and node j. When it is connected, s ij =1, and when it is disconnected, s ij =0; g ij and b ij respectively represent the conductance and susceptance of the branch between node i and node j; U i represents the voltage amplitude of node i; U j represents the voltage amplitude of node j; P i and Q i respectively represent the unknown quantities of the active and reactive injection powers of node i, and the direction is from node i to node j; G ij and B ij respectively represent the real part and the imaginary part of the elements in the i-th row and the j-th column of the node admittance matrix, representing conductance and susceptance respectively; θ ij represents the phase angle difference between node i and node j; ΔP i and ΔQ i both represent the fitting errors obtained based on data driving.

[0170] Therefore, the embodiments of the present application can make full use of the relatively large number of branch current measurements in the current distribution network and have a high solution efficiency on the premise of accurately identifying the switch state.

[0171] For example, as Figure 2 shown, the working principle of the embodiments of the present application will be elaborated in detail below with a specific embodiment.

[0172] Step S201: Input the original measurement information, that is, input the telemetry information of the branch current and load power measurement data and the telecommunication information of the switch state.

[0173] Step S202: constructing a topology measurement identification model based on mixed integer nonlinear programming of branch current method to linearize the nonlinear constraints and measurement equations to be used in the following steps, so as to quickly and accurately identify the topology errors of the distribution network.

[0174] Step S203: Data-physical fusion drives linearization, that is, the basic principle of linear approximation is obtained according to the actual topological structure and operating characteristics of the distribution network, and the linearization constraints driven by the physical model are obtained.

[0175] Step S204: constructing a topology measurement and identification model based on mixed integer linear programming of branch current method to achieve rapid and accurate identification of distribution network topology errors.

[0176] Step S205: output the topology identification result of the distribution network, thereby improving the safety and stability of the distribution network operation.

[0177] For example, Figure 3 As shown, the embodiment of the present application can select a certain actual 4-node system for testing, and verify it with the actual situation through algorithm detection, obtain measurement-related data based on the acquisition terminal, that is, topology information, perform power flow calculation based on historical measurement data, generate training samples, use partial least squares method to train the data-driven model, and finally use data physics fusion driven linearization method to linearize the nonlinear measurement equations and constraints. The simulation in the embodiment of the present application is carried out on the GAMS platform, and the Knitro solver is called for testing. Using 1 month of measurement data, the topology identification test results of the actual 4-node system are shown in Table 1. Table 1 is a topology identification result table, which is as follows:

[0178] Table 1

[0179] Switch True value Identification result S12 1 1 S23 1 1 S34 1 1 S24 0 0

[0180] Furthermore, combined with Figure 3 It can be seen from the topology identification result table that the remote signal of switch S24 is wrong. The topology measurement and identification model based on the mixed integer linearization programming of the branch current method constructed according to the embodiment of the present application can correctly identify the switch state, and the identification result is consistent with the actual situation, thereby realizing fast and accurate identification of distribution network topology errors, improving the accuracy of topology identification, and improving the safety and stability of distribution network operation.

[0181] According to the distribution network topology identification method proposed by the embodiments of the present application, the original measurement information of the distribution network can be obtained to construct a topology measurement identification model of mixed-integer nonlinear programming based on the branch current method. The target data physical fusion-driven linearization model is used to linearize the nonlinear constraints and measurement equations to be used in the topology measurement identification model of mixed-integer nonlinear programming based on the branch current method, so as to construct a topology measurement identification model of mixed-integer linear programming based on the branch current method according to the processing results, thereby outputting the topology identification result of the distribution network, improving the accuracy of topology identification, and improving the timeliness and stability of the operation of the distribution network. Thus, it solves the problems that the efficiency of maintaining the topology file by the methods based on manual work and data statistics in the related art is low, and the adaptability of the topology error identification model based on the optimization methods is poor, and the accuracy is not high, reducing the accuracy of topology identification and the timeliness and stability of the operation of the distribution network.

[0182] Next, a distribution network topology identification device according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0183] Figure 4 It is a block diagram of the distribution network topology identification device according to an embodiment of the present application.

[0184] As Figure 4 shown, the distribution network topology identification device 10 includes: an acquisition module 100, a construction module 200, and a processing module 300.

[0185] Specifically, the acquisition module 100 is used to acquire the original measurement information of the distribution network.

[0186] The construction module 200 is used to construct a topology measurement identification model of mixed-integer nonlinear programming based on the branch current method based on the original measurement information.

[0187] The processing module 300 is used to linearize the nonlinear constraints and measurement equations to be used in the topology measurement identification model of mixed-integer nonlinear programming based on the branch current method by using the target data physical fusion-driven linearization model, obtain the processing results, and use the processing results to construct a topology measurement identification model of mixed-integer linear programming based on the branch current method to output the topology identification result of the distribution network.

[0188] Optionally, in an embodiment of the present application, the nonlinear constraints and measurement equations to be used include zero-injection active power constraints, zero-injection reactive power constraints, measurement equations of node voltage amplitudes, measurement equations of node injection powers, and measurement equations of branch powers;

[0189] Among them, the zero-injection active power constraint and the zero-injection reactive power constraint are respectively:

[0190]

[0191]

[0192] Among them, U i represents the voltage amplitude of node i, and U j represents the voltage amplitude of node j. θ ij represents the phase angle difference between node i and node j. G ij and B ij respectively represent the real part and the imaginary part of the element in the i-th row and j-th column of the nodal admittance matrix, representing conductance and susceptance respectively;

[0193] The measurement equation of the nodal voltage amplitude is:

[0194]

[0195] Among them, represents the measurement of the voltage amplitude of node i, and U i represents the voltage amplitude of node i, represents the voltage measurement error;

[0196] The measurement equation of the nodal injected power is:

[0197]

[0198]

[0199] Among them, P i m and respectively are the corresponding measured values; P i and Q i respectively represent the unknown active and reactive injected powers of node i; G ij and B ij respectively represent the real part and the imaginary part of the element in the i-th row and j-th column of the nodal admittance matrix, representing conductance and susceptance respectively; respectively represent the measurement errors of the nodal active and reactive injected powers;

[0200] The measurement equation of the branch power is:

[0201]

[0202]

[0203]

[0204]

[0205] Among them, They respectively correspond to the measured values of the active and reactive powers of the head-end branch, with the direction flowing out from node i and flowing into node j; They respectively correspond to the measured values of the active and reactive powers of the tail-end branch, with the direction flowing out from node j and flowing into node i; y0 represents the susceptance of the compensating capacitor; P ij 、Q ij respectively represent the quantities to be determined of the active and reactive powers at the head-end of the branch, with the direction flowing from node i to node j; P ji 、Q ji respectively represent the quantities to be determined of the active and reactive powers at the tail-end of the branch, with the direction flowing from node j to node i; g ij 、b ij respectively represent the conductance and susceptance of the branch where node i and node j are located; They respectively represent the measurement errors of the active and reactive powers of the head-end branch and the active and reactive powers of the tail-end branch.

[0206] Optionally, in an embodiment of the present application, the processing module 300 includes: a first acquisition unit and a second acquisition unit.

[0207] Among them, the first acquisition unit is used to obtain the basic principle of linear approximation according to the actual topological structure and operating characteristics of the distribution network.

[0208] The second acquisition unit is used to linearly process the nonlinear constraints and measurement equations to be used in the topological measurement identification model of the mixed-integer nonlinear programming based on the branch current method according to the basic principle of linear approximation, and obtain a physical linearization expression.

[0209] Optionally, in an embodiment of the present application, the basic principle of linear approximation is that the head node and the tail node of the line meet the phase difference approximation requirement when the voltage amplitudes of each node reach the preset voltage reference range.

[0210] Optionally, in an embodiment of the present application, the physical linearization expression is the physical linearization expression of the linearized measurement equation and the linearized zero-injection equality constraint. Among them, the linearized measurement equation includes the physical linearization expression of the node injection power and the physical linearization expression of the branch power. Among them, the physical linearization expression of the node injection power is:

[0211]

[0212] The physical linearization expression of the branch power:

[0213]

[0214] The physical linearization expression of the zero-injection equality constraint is:

[0215]

[0216] Optionally, in an embodiment of the present application, the processing module 300 includes: a determination unit and a construction unit.

[0217] Wherein, the determination unit is configured to obtain an objective function according to the linearized measurement equation, and use the objective function to determine an objective solution with the minimum residual between the branch current variable and the branch current measurement value.

[0218] The construction unit is configured to construct a topological measurement identification model based on the branch current method and mixed integer linear programming based on the minimum objective solution and the objective data physical fusion-driven linearization model.

[0219] Optionally, in an embodiment of the present application, the objective data physical fusion-driven linearization model includes a data physical fusion-driven linearization expression of node injection power, a data physical fusion-driven linearization expression of branch power, and a data physical fusion-driven linearization expression of zero injection equality constraint. Among them, the data physical fusion-driven linearization expression of node injection power is:

[0220]

[0221] Wherein, P i , Q i respectively represent the unknown quantities of active and reactive injection power of node i. ΔP i and ΔQ i both represent the fitting errors obtained based on data-driven and are obtained by partial least squares regression fitting;

[0222] The data physical fusion-driven linearization expression of branch power is:

[0223]

[0224]

[0225] Wherein, P ij , Q ij respectively represent the unknown quantities of active and reactive power at the head end of the branch, and the direction is from node i to node j; P ji , Q ji respectively represent the unknown quantities of active and reactive power at the end of the branch, and the direction is from node j to node i; g ij , b ij respectively represent the conductance and susceptance of the branch where node i and node j are located; θ ji represents the phase angle difference between node j and node i; ΔP ij , ΔQ ij , ΔP ji and ΔQ jiBoth represent the fitting errors obtained based on data-driven, and are obtained by partial least squares regression fitting;

[0226] The data-physical fusion-driven linearized expression with zero injection equality constraint is:

[0227]

[0228] Optionally, in an embodiment of the present application, the topological measurement identification model based on the branch current method for mixed integer linear programming is:

[0229]

[0230] where, I ij represents the branch current variable of the branch where node i and node j are located; represents the branch current measurement value of the branch where node i and node j are located; Γ represents the set of branch current measurements; I i represents the injection current of node i; represents the set of all nodes, represents the set of nodes connected to node i; s ij represents the state of the switch of the branch where node i and node j are located, s ij =1 when connected, s ij =0 when disconnected; g ij and b ij respectively represent the conductance and susceptance of the branch where node i and node j are located; U i represents the voltage amplitude of node i; U j represents the voltage amplitude of node j; P i and Q i respectively represent the unknown quantities of the active and reactive injection powers of node i, and the direction is from node i to node j; G ij and B ij respectively represent the real part and the imaginary part of the elements in the i-th row and the j-th column of the node admittance matrix, representing conductance and susceptance respectively; θ ij represents the phase angle difference between node i and node j; ΔP i and ΔQ i both represent the fitting errors obtained based on data-driven.

[0231] Optionally, in an embodiment of the present application, the original measurement information includes the telemetry information of branch current, load power measurement data, and the telecommunication information of switch states.

[0232] It should be noted that the foregoing explanations of the embodiments of the distribution network topology identification method also apply to the distribution network topology identification device of this embodiment, and will not be elaborated here.

[0233] The distribution network topology identification device proposed according to the embodiments of the present application can obtain the original measurement information of the distribution network to construct a topology measurement identification model of mixed-integer nonlinear programming based on the branch current method, and use the target data physical fusion-driven linearization model to linearize the nonlinear constraints and measurement equations to be used in the topology measurement identification model of mixed-integer nonlinear programming based on the branch current method, so as to construct a topology measurement identification model of mixed-integer linear programming based on the branch current method according to the processing results, thereby outputting the topology identification result of the distribution network, further improving the accuracy of topology identification, and improving the timeliness and stability of the operation of the distribution network. Thus, it solves the problems in the related technologies that the efficiency of maintaining the topology file by the method based on manual and data statistics is low, and the topology error identification model based on the optimization method has poor adaptability and low accuracy, reducing the accuracy of topology identification, and reducing the timeliness and stability of the operation of the distribution network.

[0234] Figure 5 It is a schematic structural diagram of the electronic device provided by the embodiments of the present application. The electronic device may include:

[0235] A memory 501, a processor 502, and a computer program stored on the memory 501 and executable on the processor 502.

[0236] When the processor 502 executes the program, it implements the distribution network topology identification method provided in the above embodiments.

[0237] Further, the electronic device further includes:

[0238] A communication interface 503 for communication between the memory 501 and the processor 502.

[0239] The memory 501 is used to store a computer program executable on the processor 502.

[0240] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0241] If the memory 501, the processor 502, and the communication interface 503 are implemented independently, the communication interface 503, the memory 501, and the processor 502 can be interconnected through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 only a thick line is used in Figure 5 , but it does not mean that there is only one bus or one type of bus.

[0242] Optionally, in a specific implementation, if the memory 501, the processor 502, and the communication interface 503 are integrated on a single chip, the memory 501, the processor 502, and the communication interface 503 can communicate with each other through an internal interface.

[0243] The processor 502 may be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0244] This embodiment also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned power distribution network topology identification method is implemented.

[0245] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or N embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples.

[0246] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the technical features indicated. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0247] Any process or method description represented in a flowchart or described otherwise herein may be understood to represent a module, segment, or portion of code including one or N executable instructions for implementing a customized logical function or process, and the scope of the preferred embodiments of the present application includes additional implementations, where the functions may be performed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0248] The logic and / or steps represented in a flowchart or described otherwise herein, for example, may be considered a sequenced list of executable instructions for implementing a logical function and may be embodied specifically in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection portion having one or N wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable medium on which the program can be printed, as the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpretation, or otherwise processing as appropriate, and then stored in a computer memory.

[0249] It should be understood that each part of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0250] Those of ordinary skill in the art can understand that all or part of the steps carried by the method of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0251] In addition, in each embodiment of the present application, the functional units can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. When the above integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0252] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disc, or the like. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for identifying the topology of a distribution network, characterized in that, It includes the following steps: Obtain the original measurement information of the distribution network; Based on the original measurement information, construct a topological measurement identification model of mixed-integer nonlinear programming based on the branch current method; And Use the target data physical fusion-driven linearization model to linearize the nonlinear constraints and measurement equations to be used in the topological measurement identification model of mixed-integer nonlinear programming based on the branch current method, obtain the processing result, and use the processing result to construct a topological measurement identification model of mixed-integer linear programming based on the branch current method to output the topological identification result of the distribution network.

2. The power distribution network topology identification method according to claim 1, characterized in that The nonlinear constraints and measurement equations to be used include zero-injection active power constraints, zero-injection reactive power constraints, node voltage magnitude measurement equations, node injection power measurement equations, and branch power measurement equations; Among them, the zero-injection active power constraint and the zero-injection reactive power constraint are respectively: Among them, U i represents the voltage amplitude of node i, U j represents the voltage amplitude of node j, θ ij represents the phase angle difference between node i and node j, G ij , B ij respectively represent the real part and the imaginary part of the element in the i-th row and the j-th column of the nodal admittance matrix, representing conductance and susceptance respectively; The node voltage magnitude measurement equation is: Among them, represents the voltage magnitude measurement of node i, U i represents the voltage magnitude of node i, represents the voltage measurement error; The node injection power measurement equation is: Among them, P i m and are the corresponding measured values respectively; P i and Q i represent the unknowns of the active and reactive injection powers of node i respectively; G ij and B ij represent the real part and the imaginary part of the element in the i-th row and the j-th column of the nodal admittance matrix, representing the conductance and susceptance respectively; represent the measurement errors of the active and reactive injection powers of the nodes respectively; The branch power measurement equation is: Among them, correspond to the measured values of the active and reactive powers of the head-end branch respectively, with the direction flowing out from node i and flowing into node j; correspond to the measured values of the active and reactive powers of the tail-end branch respectively, with the direction flowing out from node j and flowing into node i; y0 represents the susceptance of the compensating capacitor; P ij 、Q ij represent the unknown quantities of the active and reactive powers at the head-end of the branch respectively, with the direction flowing from node i to node j; P ji 、Q ji represent the unknown quantities of the active and reactive powers at the tail-end of the branch respectively, with the direction flowing from node j to node i; g ij 、b ij represent the conductance and susceptance of the branch where node i and node j are located respectively; represent the measurement errors of the active and reactive powers of the head-end branch, the active and reactive powers of the tail-end branch respectively.

3. The method for identifying the topology of a distribution network according to claim 1, characterized in that The linearization of the nonlinear constraints and measurement equations to be used in the topological measurement identification model of mixed-integer nonlinear programming based on the branch current method to obtain the processing result includes: Obtain the basic principle of linear approximation according to the actual topological structure and operating characteristics of the distribution network; Linearize the nonlinear constraints and measurement equations to be used in the topological measurement identification model of mixed-integer nonlinear programming based on the branch current method according to the basic principle of linear approximation to obtain a physical linearization expression.

4. The method for identifying the distribution network topology according to claim 3, wherein The basic principle of linear approximation is that the head and end nodes of the line meet the phase difference approximation requirement when the node voltage magnitudes reach the preset voltage reference range.

5. The method for identifying the distribution network topology according to claim 3, wherein The physical linearization expression is the physical linearization expression of the linearized measurement equation and the linearized zero-injection equality constraint. Among them, the linearized measurement equation includes the physical linearization expression of node injection power and the physical linearization expression of branch power. Among them, the physical linearization expression of node injection power is: The physical linearization expression of branch power: The physical linearization expression of the zero-injection equality constraint is:

6. The method for identifying the distribution network topology according to claim 5, characterized in that The construction of the topological measurement identification model of mixed-integer linear programming based on the branch current method using the processing result includes: Obtain the objective function according to the linearized measurement equation, and use the objective function to determine the objective solution with the smallest residual between the branch current variable and the branch current measurement value; Based on the smallest objective solution and the target data physical fusion-driven linearization model, construct the topological measurement identification model of mixed-integer linear programming based on the branch current method.

7. The method for identifying the topology of a distribution network according to claim 1, wherein The target data physical fusion-driven linearization model includes the data physical fusion-driven linearization expression of node injection power, the data physical fusion-driven linearization expression of branch power, and the data physical fusion-driven linearization expression of the zero-injection equality constraint. Among them, the data physical fusion-driven linearization expression of node injection power is: Among them, P i , Q i respectively represent the active and reactive injection power quantities to be solved for node i, ΔP i and ΔQ i both represent the fitting errors obtained based on data-driven methods and are obtained by partial least squares regression fitting; The data physical fusion-driven linearization expression of branch power is: Among them, P ij , Q ij respectively represent the quantities to be determined of the active and reactive powers at the head of the branch, with the direction flowing from node i to node j; P ji , Q ji respectively represent the quantities to be determined of the active and reactive powers at the end of the branch, with the direction flowing from node j to node i; g ij , b ij respectively represent the conductance and susceptance of the branch where node i and node j are located; θ ji represents the phase angle difference between node j and node i; ΔP ij , ΔQ ij , ΔP ji and ΔQ ji all represent the fitting errors obtained based on data-driven, and are obtained by partial least squares regression fitting; The data-physical fusion-driven linearization expression with zero injection equality constraint is as follows:

8. The method for identifying the topology of a distribution network according to claim 1, characterized in that The topological measurement identification model of the mixed-integer linear programming based on the branch current method is as follows: Among them, I ij represents the branch current variable between node i and node j; represents the measured value of the branch current between node i and node j; Γ represents the set of branch current measurements; I i represents the injected current of node i; represents the set of all nodes, represents the set of nodes connected to node i; s ij represents the state of the switch of the branch between node i and node j. When it is connected, s ij = 1, and when it is disconnected, s ij = 0; g ij and b ij respectively represent the conductance and susceptance of the branch between node i and node j; U i represents the voltage magnitude of node i; U j represents the voltage magnitude of node j; P i and Q i respectively represent the unknown active and reactive power injection powers of node i, with the direction from node i to node j; G ij and B ij respectively represent the real part and the imaginary part of the elements in the i-th row and j-th column of the nodal admittance matrix, representing conductance and susceptance respectively; θ ij represents the phase angle difference between node i and node j; ΔP i and ΔQ i both represent the fitting errors obtained based on data-driven.

9. The method for identifying the topology of a distribution network according to any one of claims 1-8, characterized in that, The original measurement information includes telemetry information of branch current and load power measurement data, and telecommunication information of switch states.

10. A distribution network topology identification device, characterized in that, It includes: An acquisition module for acquiring the original measurement information of the distribution network; A construction module for constructing a topological measurement identification model of the mixed-integer nonlinear programming based on the branch current method based on the original measurement information; And A processing module for linearly processing the nonlinear constraints and measurement equations to be used in the topological measurement identification model of the mixed-integer nonlinear programming based on the branch current method by using the target data-physical fusion-driven linearization model, obtaining a processing result, and constructing a topological measurement identification model of the mixed-integer linearization programming based on the branch current method by using the processing result to output the topological identification result of the distribution network.

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