Phase-household relation identification method and device of low-voltage power distribution network and electronic equipment

Abstract

The invention discloses a phase-household relation identification method and device of a low-voltage power distribution network and electronic equipment. The low-voltage distribution network topology identification method comprises the steps of collecting node current of each node and phase line current of each phase line on a target low-voltage distribution network; determining an initial weight of the node current, calculating an initial regression coefficient and an initial adjustment residual value between the node current and the phase line current, updating the initial weight, calculating the regression coefficient and the adjustment residual value based on the updated weight, and performing iterative calculation for multiple times until a difference value of the regression coefficients of two adjacent times of iterative calculation is smaller than a preset threshold value. Determining the regression coefficient of the last iteration as a target regression coefficient; and determining a target regression coefficient between the target node and each phase line, determining the phase line corresponding to the maximum target regression coefficient as the target phase line to which the target node belongs, and obtaining a user set of each phase line. According to the invention, the problem of low phase-household relation identification accuracy of the low-voltage power distribution network in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of low-voltage distribution network topology identification, and specifically to a method, device and electronic equipment for identifying phase-to-household relationships in a low-voltage distribution network. Background Art

[0002] With the massive integration of renewable energy, the randomness and uncertainty of renewable energy load fluctuations, as well as the dispersed nature of load distribution, have made the topology of low-voltage distribution networks complex and difficult to identify. This has impacted their management and planning, and has made applications such as fault location, line loss management, and power theft detection difficult. Related technologies primarily include model-driven, data-driven, and graph analysis methods for identifying inter-connected relationships in low-voltage distribution networks. Model-driven methods combine real-time and historical data, using mathematical models to estimate network status and achieve topology identification. Data-driven methods use historical data and algorithms (such as neural networks) for topology identification. Graph analysis applies graph theory principles to analyze network structure and effectively identify topology. However, these related-art inter-connected relationship identification methods rely on models and historical data for analysis, which can introduce errors and result in low identification accuracy.

[0003] Currently, no effective solution has been proposed to the problem of low accuracy in identifying phase-to-household relationships in low-voltage distribution networks in related technologies. Summary of the Invention

[0004] The main purpose of this application is to provide a method, device and electronic equipment for identifying phase-to-household relationships in a low-voltage distribution network, so as to solve the problem of low accuracy in phase-to-household relationship identification in low-voltage distribution networks in related technologies.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for identifying the phase-to-user relationship of a low-voltage distribution network is provided. The method comprises: collecting the node current of each node on the target low-voltage distribution network, and the phase line current of each phase line of the target low-voltage distribution network, and generating a node current matrix based on the node currents of all nodes, and generating a phase line current sequence of the phase line based on the phase line current of each phase line, wherein the node is used to characterize the user using the target low-voltage distribution network; for each phase line, determining the initial weight of the node current of each node, calculating the initial regression coefficient and initial adjustment residual value between the node current of each node and the phase line current of the target phase line based on the initial weight and weighted least squares method, updating the initial weight by a preset weight function and the initial adjustment residual value, and updating the initial weight based on the updated The regression coefficient and the adjusted residual value are calculated based on the weights after the iteration, and the calculation is performed iteratively for multiple times until the difference between the regression coefficients calculated in two adjacent iterations is less than the preset threshold value. The regression coefficient obtained in the last iteration is determined as the target regression coefficient between the node current of each node and the phase line current of the target phase line, wherein the adjusted residual value obtained in each iteration is used to update the weight of the next iteration; the target regression coefficient between the node current of the target node and the phase line current of each phase line is determined, and the phase line corresponding to the maximum target regression coefficient is determined as the target phase line to which the target node belongs. The phase lines of all nodes are combined according to the phase line type to obtain the user set of each phase line.

[0006] Optionally, the node current of each node on the target low-voltage distribution network and the phase line current of each phase line of the target low-voltage distribution network are collected, and a node current matrix is ​​generated based on the node currents of all nodes. Generating a phase line current sequence of the phase line based on the phase line current of each phase line includes: for each node, collecting T node currents of the target node at preset time intervals to generate a node current sequence of the target node, and generating a node current matrix based on the node current sequences of N nodes, wherein T and N are both positive integers; for each phase line, collecting T phase line currents of the target phase line at preset time intervals to generate a phase line current sequence of the target phase line.

[0007] Optionally, calculating the initial regression coefficient and initial adjusted residual value between the node current of each node and the phase line current of the target phase line based on the initial weight and weighted least squares method includes: determining the initial weight matrix based on the initial weights of all nodes, inputting the initial weight matrix, the node current matrix and the phase line current sequence of the target phase line into the regression coefficient calculation formula to obtain the initial regression coefficient; calculating the fitting residual value of each node through the initial regression coefficient, the node current matrix and the phase line current sequence of the target phase line; and calculating the initial adjusted residual value based on the fitting residual value of each node.

[0008] Optionally, calculating the fitting residual value of each node through the initial regression coefficient, the node current matrix and the phase line current sequence of the target phase line includes: calculating the product of the initial regression coefficient and the node current matrix to obtain the current prediction value sequence of each node; calculating the difference between the phase line current sequence of the target phase line and the current prediction value sequence of each node to obtain the fitting residual value of each node.

[0009] Optionally, calculating the initial adjusted residual value based on the fitted residual value of each node includes: determining the adjustment constant parameter and the preset parameter, and calculating the standard deviation of the error term based on the fitted residual values ​​of all nodes; calculating the product of the adjustment constant parameter, the preset parameter and the standard deviation of the error term, and calculating the ratio of the fitted residual value of each node to the product to obtain the initial adjusted residual value of the node.

[0010] Optionally, the weight of each iteration is calculated as follows: determine whether the absolute value of the adjusted residual value obtained in the previous iteration is greater than or equal to the adjusted residual value threshold; if the absolute value of the adjusted residual value obtained in the previous iteration is greater than or equal to the adjusted residual value threshold, determine the weight of the current iteration as the first preset value; if the absolute value of the adjusted residual value obtained in the previous iteration is less than the adjusted residual value threshold, calculate the square of the adjusted residual value to obtain the target value, calculate the square of the difference between the second preset value and the target value, and obtain the weight of the current iteration.

[0011] Optionally, the method also includes: determining the number of iterations of the regression coefficient, and judging whether the number of iterations is greater than or equal to an iteration threshold; when the number of iterations is less than the iteration threshold, performing multiple iterative calculation steps; when the number of iterations is greater than or equal to the iteration threshold, and the difference between the regression coefficients calculated in two adjacent iterative calculations is greater than or equal to a preset threshold, stopping the iteration, and determining the regression coefficient obtained in the last iteration as the target regression coefficient between the node current of each user and the target phase line current.

[0012] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a phase-to-user relationship identification device for a low-voltage distribution network is provided. The device includes: an acquisition unit for acquiring the node current of each node on the target low-voltage distribution network, and the phase line current of each phase line of the target low-voltage distribution network, and generating a node current matrix based on the node currents of all nodes, and generating a phase line current sequence of the phase line based on the phase line current of each phase line, wherein the node is used to characterize the user using the target low-voltage distribution network; a calculation unit for determining the initial weight of the node current of each node for each phase line, calculating the initial regression coefficient and initial adjustment residual value between the node current of each node and the phase line current of the target phase line based on the initial weight and weighted least squares method, updating the initial weight by a preset weight function and the initial adjustment residual value, and The regression coefficient and the adjusted residual value are calculated based on the updated weight, and the calculation is iterated multiple times until the difference between the regression coefficients calculated in two adjacent iterations is less than a preset threshold value. The regression coefficient obtained in the last iteration is determined as the target regression coefficient between the node current of each node and the phase line current of the target phase line, wherein the adjusted residual value obtained in each iteration is used to update the weight of the next iteration; a determination unit is used to determine the target regression coefficient between the node current of the target node and the phase line current of each phase line, and the phase line corresponding to the maximum target regression coefficient is determined as the target phase line to which the target node belongs. The phase lines to which all nodes belong are combined according to the phase line type to obtain a user set for each phase line.

[0013] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a computer program product is provided, including a computer program, which, when executed by a processor, implements the steps of the phase-to-household relationship identification method of the low-voltage distribution network described in each embodiment of the present application.

[0014] Through the present application, the following steps are adopted: collecting the node current of each node on the target low-voltage distribution network and the phase line current of each phase line of the target low-voltage distribution network, and generating a node current matrix based on the node currents of all nodes, and generating a phase line current sequence of the phase line based on the phase line current of each phase line, wherein the node is used to characterize the user using the target low-voltage distribution network; for each phase line, determining the initial weight of the node current of each node, calculating the initial regression coefficient and initial adjusted residual value between the node current of each node and the phase line current of the target phase line based on the initial weight and weighted least squares method, updating the initial weight by a preset weight function and the initial adjusted residual value, and calculating the regression coefficient based on the updated weight The method uses the weighted least squares method to calculate the regression coefficients of the node current of each node and the phase line current of the target phase line, and adjusts the residual value, and iterates the calculation multiple times until the difference between the regression coefficients calculated in two adjacent iterations is less than the preset threshold value. The regression coefficient obtained in the last iteration is determined as the target regression coefficient between the node current of each node and the phase line current of the target phase line, wherein the adjusted residual value obtained in each iteration is used to update the weight of the next iteration; the target regression coefficient between the node current of the target node and the phase line current of each phase line is determined, and the phase line corresponding to the maximum target regression coefficient is determined as the target phase line to which the target node belongs. The phase lines belonging to all nodes are combined according to the phase line type to obtain the user set of each phase line, thereby solving the problem of low phase-to-household relationship recognition accuracy of the low-voltage distribution network in the related art. The regression coefficient between the node current of the user and the phase line current of the phase line is calculated by the weighted least squares method through multiple iterations, and the phase line to which each user belongs is determined according to the regression coefficient, thereby achieving the effect of improving the phase-to-household relationship recognition accuracy of the low-voltage distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0016] Figure 1 Flowchart of a method for identifying phase-to-household relationships in a low-voltage distribution network according to an embodiment of the present application;

[0017] Figure 2 1 is a flow chart of an optional method for identifying phase-to-household relationships in a low-voltage distribution network according to an embodiment of the present application;

[0018] Figure 3 Schematic diagram of the initial regression coefficient of phase A provided in an embodiment of the present application;

[0019] Figure 4 Schematic diagram of the initial regression coefficient of phase B provided in an embodiment of the present application;

[0020] Figure 5Schematic diagram of the initial regression coefficient of phase C provided in an embodiment of the present application;

[0021] Figure 6 Schematic diagram of the regression coefficient of phase A after multiple iterative calculations according to the embodiment of the present application;

[0022] Figure 7 Schematic diagram of the regression coefficient of phase B after multiple iterative calculations according to the embodiment of the present application;

[0023] Figure 8 Schematic diagram of the regression coefficient of phase C after multiple iterative calculations according to the embodiment of the present application;

[0024] Figure 9 Schematic diagram of the topological relationship between a phase line and a user node subset provided according to an embodiment of the present application;

[0025] Figure 10 2 is a schematic diagram showing the comparison results of the regression coefficient calculation using the iterative weighted least squares method and the ordinary least squares method according to the embodiment of the present application;

[0026] Figure 11 1 is a schematic diagram of a phase-to-household relationship identification device for a low-voltage distribution network provided in accordance with an embodiment of the present application;

[0027] Figure 12 is a schematic diagram of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0029] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data for analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties.

[0032] It should be noted that the information collected is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data comply with the relevant laws, regulations and standards of the relevant regions, take necessary confidentiality measures, do not violate public order and good customs, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0033] The present invention will be described below in conjunction with preferred implementation steps. Figure 1 Flowchart of a method for identifying phase-to-household relationships in a low-voltage distribution network according to an embodiment of the present application. Figure 1 As shown, the method includes the following steps:

[0034] Step S101, collecting the node current of each node on the target low-voltage distribution network and the phase line current of each phase line of the target low-voltage distribution network, and generating a node current matrix based on the node currents of all nodes, and generating a phase line current sequence of the phase line based on the phase line current of each phase line, wherein the node is used to characterize the user using the target low-voltage distribution network.

[0035] Specifically, the target low-voltage distribution network can be a three-phase power supply mode, supplying power to users through three phase lines of phase A, phase B and phase C. Each user connects to any phase line of phase A, phase B and phase C to use electricity. The user connected to each phase line in the target low-voltage distribution network is identified, and the topological relationship of the target low-voltage distribution network is obtained. By identifying the topological relationship, the management efficiency of the low-voltage distribution network can be improved.

[0036] In order to identify the phase-to-user relationship of the target low-voltage distribution network, it is first necessary to collect the phase line current of each phase line and the node current measurement values ​​of each user's corresponding node. During the collection process, multiple sample data can be collected for each node and each phase line according to the preset time interval, and the node current matrix and phase line current sequence are generated based on the collected sample data.

[0037] Step S102: for each phase line, determine the initial weight of the node current of each node, calculate the initial regression coefficient and initial adjustment residual value between the node current of each node and the phase line current of the target phase line based on the initial weight and weighted least squares method, update the initial weight by a preset weight function and the initial adjustment residual value, and calculate the regression coefficient and the adjustment residual value based on the updated weight, iterate the calculation multiple times until the difference between the regression coefficients calculated in two adjacent iterations is less than a preset threshold, and determine the regression coefficient obtained in the last iteration as the target regression coefficient between the node current of each node and the phase line current of the target phase line, wherein the adjusted residual value obtained in each iteration is used to update the weight of the next iteration.

[0038] Specifically, in the weighted least squares method, for data with heteroscedasticity, the influence of each data point can be adjusted by introducing weights so that the model fits the data better. The embodiment of the present application calculates the regression coefficient between the node current of each user and the phase line current between each phase line by the weighted least squares method, thereby identifying the phase line to which each user belongs. After obtaining the node current matrix and the phase line current sequence of each phase line, an initial weight is set for each node current, for example, the initial weights of all node currents are set to 1, and an initial weight matrix is ​​generated. The weighted least squares method is used to calculate the initial regression coefficient and the initial adjusted residual value between the node current of each node and the phase line current of the target phase line, and the number of iterations is set to 0.

[0039] The preset weight function can be a bisquare weight function (a function used for data fitting and regression analysis). The node current weight is updated by the preset weight function and the adjusted error value of each iteration, the regression coefficient of each iteration is recalculated, and the adjusted residual value of the next iteration is calculated according to the calculation formula of the adjusted residual; the iterative calculation is repeated until the difference between the estimated value of the regression coefficient in two consecutive iterations is less than the preset threshold value, and the regression coefficient obtained in the last iteration is determined as the target regression coefficient between the node current of each node and the phase line current of the target phase line. For example, if the estimated value of any regression coefficient is , m is the target phase line, j is the jth user, and the change in two adjacent iterations is less than the preset threshold ,Right now , g is the g-th iteration, then the target regression coefficient between the node current of each node and the phase current of the target phase line m is output .

[0040] It should be noted that for the general multiple linear regression model Y=Xβ+e, when the error term e t (t=1,…,T) satisfies the following formula:

[0041] (1)

[0042] Among them, T represents the Tth sample point collected, represents the expectation of the error term, represents the covariance of the error term, represents the variance of the error term, and the standard deviation of the error term ,…, If they are not completely equal, the model is said to have heteroscedasticity. The root cause of heteroscedasticity is that the data errors of some abnormal sample points are too large, resulting in abnormal probability distribution of regression residuals. When the model has heteroscedasticity, the weighted least squares method can be considered to estimate the regression coefficient. In the embodiment of the present application, the node currents of the users belong to different phase lines, so the weighted least squares method can be used to estimate the regression coefficient.

[0043] The regression coefficient is calculated as follows:

[0044] (2)

[0045] Among them, w i is the weight value of the i-th sampling point for each given user, x i1 represents the node current of the i-th sampling point of the first user, N represents the total number of users using the target low-voltage distribution network, and the weighted least squares estimation is to find the estimated value of the regression coefficient m makes Q w To reach the minimum value, the weight matrix W is recorded as:

[0046] (3)

[0047] The calculation formula of the regression coefficient of each user's node current to the target phase line m is as follows:

[0048]

[0049] Where X represents the node current matrix, X′ represents the transpose of X, and Y represents the phase current sequence of the target phase m. Select the bisquare weight function and calculate the weight W for the g+1th iteration g+1 . Calculate the regression coefficient for the g+1th iteration .

[0050] Step S103, determine the target regression coefficient between the node current of the target node and the phase line current of each phase line, determine the phase line corresponding to the maximum target regression coefficient as the target phase line belonging to the target node, combine the phase lines belonging to all nodes according to the phase line type, and obtain the user set of each phase line.

[0051] Specifically, different phase line currents are used as dependent variables, and the above process is repeated to obtain the target regression coefficient of each user relative to each phase line. For the node current of each target node, the phase line corresponding to the maximum regression coefficient among the target regression coefficients of all phase lines is the target phase line to which the target node belongs. All phase lines are traversed to determine the node subset of users corresponding to each phase line. The detailed information of all phase line-user node subsets is found. The phase line-user node subset (that is, the set of users for each phase line) represents the phase where any user in the data set is located, and it can reflect the topological relationship of the target low-voltage distribution network.

[0052] Output each phase line-user node subset, where the phase line represents a phase in a group of outgoing lines, and the node represents the user connected to the phase line. The phase line-user node subset represents the phase where any user in the dataset is located, which can reflect the topological relationship of the target low-voltage distribution network.

[0053] The embodiment of the present application provides a phase-to-user relationship identification method for a low-voltage distribution network, which collects the node current of each node on the target low-voltage distribution network and the phase line current of each phase line of the target low-voltage distribution network, and generates a node current matrix based on the node currents of all nodes, and generates a phase line current sequence of the phase line based on the phase line current of each phase line, wherein the node is used to characterize the user using the target low-voltage distribution network; for each phase line, the initial weight of the node current of each node is determined, and the initial regression coefficient and the initial adjusted residual value between the node current of each node and the phase line current of the target phase line are calculated based on the initial weight and weighted least squares method, the initial weight is updated by a preset weight function and the initial adjusted residual value, and based on the updated weight The regression coefficient and the adjusted residual value are calculated, and the calculation is iterated multiple times until the difference between the regression coefficients calculated in two adjacent iterations is less than a preset threshold. The regression coefficient obtained in the last iteration is determined as the target regression coefficient between the node current of each node and the phase line current of the target phase line, wherein the adjusted residual value obtained in each iteration is used to update the weight of the next iteration; the target regression coefficient between the node current of the target node and the phase line current of each phase line is determined, and the phase line corresponding to the maximum target regression coefficient is determined as the target phase line to which the target node belongs. The phase lines belonging to all nodes are combined according to the phase line type to obtain the user set of each phase line, thereby solving the problem of low phase-to-household relationship recognition accuracy of the low-voltage distribution network in the related art. The regression coefficient between the user's node current and the phase line current of the phase line is calculated by the weighted least squares method through multiple iterations, and the phase line to which each user belongs is determined based on the regression coefficient, thereby achieving the effect of improving the phase-to-household relationship recognition accuracy of the low-voltage distribution network.

[0054] In order to calculate the regression coefficient of each node current relative to the phase line current, it is necessary to first determine the node current matrix and the phase line current sequence. Optionally, in the phase-to-household relationship identification method of the low-voltage distribution network provided in an embodiment of the present application, the node current of each node on the target low-voltage distribution network and the phase line current of each phase line of the target low-voltage distribution network are collected, and a node current matrix is ​​generated based on the node currents of all nodes. Generating the phase line current sequence of the phase line based on the phase line current of each phase line includes: for each node, collecting T node currents of the target node at preset time intervals to generate a node current sequence of the target node, and generating a node current matrix based on the node current sequence of N nodes, wherein T and N are both positive integers; for each phase line, collecting T phase line currents of the target phase line at preset time intervals to generate a phase line current sequence of the target phase line.

[0055] For example, the preset time interval can be 15 minutes, and the collection period is two days. Each node collects a total of T = 192 sample points (i.e., node currents). Each node current sequence contains 192 sample point data of the target node. The node current sequences collected from N nodes are combined to form the node current matrix X. T sample points are also collected for each phase line, and each phase line current sequence contains 192 phase line current data of the target phase line.

[0056] The embodiment of the present application collects multiple data of node current and phase line current, calculates the regression coefficient through the multiple sampling data, and ensures the accuracy of the calculation result.

[0057] After determining the node current matrix and the phase line current sequence, the initial regression coefficient and the initial adjusted residual value are calculated by the weighted least squares method. Optionally, in the phase-to-household relationship identification method of the low-voltage distribution network provided in the embodiment of the present application, the initial regression coefficient and the initial adjusted residual value between the node current of each node and the phase line current of the target phase line are calculated based on the initial weights and the weighted least squares method, including: determining the initial weight matrix based on the initial weights of all nodes, inputting the initial weight matrix, the node current matrix and the phase line current sequence of the target phase line into the regression coefficient calculation formula to obtain the initial regression coefficient; calculating the fitting residual value of each node through the initial regression coefficient, the node current matrix and the phase line current sequence of the target phase line; and calculating the initial adjusted residual value based on the fitting residual value of each node.

[0058] Specifically, the calculation formula of the initial regression coefficient between the node current of each node and the phase current of the target phase line m is as follows:

[0059]

[0060] Where X represents the node current matrix, X′ represents the transpose of X, and Y represents the phase current sequence of the target phase line m. Therefore, by inputting the phase current sequence Y of each phase line a 、Y b 、Y c , the user's node current matrix X, the initial weight matrix W0. The initial regression coefficient of each node relative to each phase line can be calculated , the calculation formula for the adjusted residual value is as follows:

[0061] (6)

[0062] in, represents the adjusted residual value, represents the fitting residual value of the node current, K represents the adjustment constant parameter, S represents the standard deviation of the error term, Indicates preset parameters.

[0063] In the embodiment of the present application, the initial regression coefficient and the initial adjusted residual value are calculated as the data for calculating the regression coefficient in the next iteration.

[0064] Optionally, in the phase-to-household relationship identification method of the low-voltage distribution network provided in an embodiment of the present application, the fitting residual value of each node is calculated through the initial regression coefficient, the node current matrix and the phase line current sequence of the target phase line, including: calculating the product of the initial regression coefficient and the node current matrix to obtain the current prediction value sequence of each node; calculating the difference between the phase line current sequence of the target phase line and the current prediction value sequence of each node to obtain the fitting residual value of each node.

[0065] Specifically, the fitting residual value is the difference between the observed value and the fitted value, where the observed value is the measured phase current sequence. The fitted value refers to the value calculated based on the product of the regression coefficient and the independent variable. Therefore, by calculating the product of the initial regression coefficient and the node current matrix, the current prediction value sequence for each node can be obtained. By calculating the difference between the phase current sequence of the target phase line and the current prediction value sequence of each node, the fitting residual value for each node is obtained. In the embodiment of the present application, the adjusted residual value is determined by calculating the fitting residual value.

[0066] Optionally, in the phase-to-household relationship identification method of the low-voltage distribution network provided in an embodiment of the present application, calculating the initial adjusted residual value based on the fitting residual value of each node includes: determining the adjustment constant parameter and the preset parameter, and calculating the standard deviation of the error term based on the fitting residual values ​​of all nodes; calculating the product of the adjustment constant parameter, the preset parameter and the standard deviation of the error term, and calculating the ratio of the fitting residual value of each node to the product to obtain the initial adjusted residual value of the node.

[0067] Specifically, according to the calculation formula of the adjusted residual value, the preset parameters h in tt is the tth diagonal element of the hat matrix H of the node current matrix; K is the adjustment constant; lowering the adjustment constant increases the downweight assigned to large residuals; increasing the adjustment constant decreases the downweight assigned to large residuals; S is the estimated standard deviation of the error term, S = MAD / 0.6745. MAD is the median absolute deviation of the fitted residual values ​​for all samples; the constant 0.6745 ensures an unbiased estimate of the normal distribution. In this embodiment, the initial adjusted residual values ​​are calculated from the fitted residual values, and the weights for the next iteration are updated based on these initial adjusted residual values.

[0068] The weight of each iteration is updated according to the adjusted residual value obtained in the previous iteration. Optionally, in the phase-to-household relationship identification method of the low-voltage distribution network provided in the embodiment of the present application, the weight of each iteration is calculated as follows: determine whether the absolute value of the adjusted residual value obtained in the previous iteration is greater than or equal to the adjusted residual value threshold; when the absolute value of the adjusted residual value obtained in the previous iteration is greater than or equal to the adjusted residual value threshold, determine the weight of the current iteration as the first preset value; when the absolute value of the adjusted residual value obtained in the previous iteration is less than the adjusted residual value threshold, calculate the square of the adjusted residual value to obtain the target value, calculate the square of the difference between the second preset value and the target value, and obtain the weight of the current iteration.

[0069] Specifically, when fitting the model to calculate the regression coefficient by weighted least squares method, different weights need to be assigned according to the degree of data variation. A larger weight is assigned to data with a smaller degree of variation and more accurate measurement, and a smaller weight is assigned to data with a larger degree of variation and more accurate measurement, so that the residual sum of squares of the weighted regression model is minimized; the fitting residual value based on the tth sample Set the preset weight function wt(·) and adjust the residual value of the sample point to affect the weight of each iteration. The weight update calculation formula is as follows:

[0070] (7)

[0071] Among them, the preset value can be 0, and the adjustment residual value threshold can be set to 1. It should be noted that the embodiment of the present application does not involve the situation where the absolute value of the adjustment residual value is greater than or equal to the adjustment residual value threshold.

[0072] This embodiment reduces the impact on the regression coefficient by eliminating abnormal points or assigning differentiated weights, thereby improving the accuracy of low-voltage distribution network topology identification, which is beneficial to the management and regulation of low-voltage distribution networks and provides important data support for applications such as low-voltage distribution network fault location, line loss management, and power theft detection.

[0073] In order to prevent the calculation of the regression coefficient from taking too long, the target regression coefficient is determined by setting an iteration number threshold. Optionally, in the phase-to-household relationship identification method of the low-voltage distribution network provided in the embodiment of the present application, the method also includes: determining the number of iterations of the regression coefficient, and judging whether the number of iterations is greater than or equal to the iteration number threshold; when the number of iterations is less than the iteration number threshold, performing multiple iterative calculation steps; when the number of iterations is greater than or equal to the iteration number threshold, and the difference between the regression coefficients calculated in two adjacent iterative calculations is greater than or equal to the preset threshold, stopping the iteration, and determining the regression coefficient obtained from the last iteration as the target regression coefficient between the node current of each user and the target phase line current.

[0074] Specifically, in order to prevent the iterative calculation of the regression coefficient from falling into an infinite loop, an iteration number threshold can be set artificially. In multiple iterative calculations of the regression coefficient, if the number of iterations is less than the iteration number threshold before reaching the iteration number threshold, the iteration is stopped and the regression coefficient obtained by the last iteration is output. If after reaching the iteration number threshold, the difference between the regression coefficients calculated by two adjacent iterations of all previous iterations is greater than or equal to the preset threshold, the iteration is stopped when the number of iterations reaches the iteration number threshold, and the regression coefficient obtained by the last iteration is output as the target regression coefficient. The embodiment of the present application avoids the iterative calculation of the regression coefficient from falling into an infinite loop by setting the iteration number threshold.

[0075] According to another embodiment of the present application, this embodiment provides an optional method for identifying phase-to-household relationships in a low-voltage distribution network. Figure 2 FIG. 1 is a flow chart of an optional method for identifying the relationship between households in a low-voltage distribution network according to an embodiment of the present application. Figure 2 As shown, the method includes:

[0076] S201: Collect sample data, input the current measurement value vector of each phase line, the user node current measurement value matrix, and the initial weight matrix.

[0077] Specifically, a low-voltage distribution network test system with a total number of 63 users was established, where the A-phase users were numbered 1 to 21, the B-phase users were numbered 22 to 42, and the C-phase users were numbered 43 to 63. The collection time interval was 15 minutes, the time period was 2 days, and a total of T = 192 time sections were collected. The A, B, and C-phase buses on the low-voltage side of the distribution transformer were used as balancing nodes, and multi-section power flow simulation calculations were performed to obtain the real current data of each node, that is, the phase line current measurement value vector and the user node current measurement value matrix, and an initial weight matrix was set with the weight of each sample being 1.

[0078] S202: Assign initial weights to the sample points, and calculate the estimated values ​​of the regression coefficients and the adjusted residual values ​​based on the weighted least squares method, setting the number of iterations g = 0;

[0079] Specifically, the regression coefficient is calculated as follows:

[0080] (8)

[0081] Among them, w i is the weight value of the i-th sampling point for each given user, x i1 represents the node current of the i-th sampling point of the first user, N represents the total number of users using the target low-voltage distribution network, and the weighted least squares estimation is to find the estimated value m of the regression coefficient so that Q w To reach the minimum value, the weight matrix W is recorded as:

[0082] (9)

[0083] The calculation formula of the regression coefficient of each user's node current to the target phase line m is as follows:

[0084]

[0085] Where X represents the node current matrix, X′ represents the transpose of X, and Y represents the phase current sequence of the target phase line m.

[0086] For the first iteration, the estimated least squares regression coefficients of the 63 nodes for the ABC three-phase are as follows: Figure 3 、 Figure 4 and Figure 5 As shown, Figure 3 Schematic diagram of the initial regression coefficient of phase A provided according to the embodiment of the present application. Figure 4 Schematic diagram of the initial regression coefficient of phase B provided in the embodiment of the present application. Figure 5 Schematic diagram of the initial regression coefficient of phase C provided according to an embodiment of the present application.

[0087] S203: According to the adjusted residual calculation formula and the estimated value of the regression coefficient obtained after the g-th iteration, the adjusted residual value of the g+1-th iteration is obtained.

[0088] Specifically, the calculation formula for the adjusted residual value is as follows:

[0089] (11)

[0090] in, represents the adjusted residual value, represents the fitting residual value of the node current, K represents the adjustment constant parameter, S represents the standard deviation of the error term, Represents the preset parameters. The fitting residual value is the difference between the observed value and the fitted value, and the observed value is also the measured phase line current sequence. The fitting value refers to the value calculated based on the product of the regression coefficient and the independent variable. Therefore, by calculating the product of the initial regression coefficient and the node current matrix, the current prediction value sequence of each node can be obtained. By calculating the difference between the phase line current sequence of the target phase line and the current prediction value sequence of each node, the fitting residual value of each node is obtained. The embodiment of the present application determines the adjustment residual value by calculating the fitting residual value.

[0091] S204: Select a weight function and calculate the weight of the g+1th iteration.

[0092] Specifically, the weight update calculation formula is as follows:

[0093] (12)

[0094] S205: Calculate the estimated value of the regression coefficient for the g+1th iteration;

[0095] Specifically, calculate the weight W of the g+1th iteration g+1 . Calculate the regression coefficient for the g+1th iteration .

[0096] S206: Repeat S203 to S205. If the change in the estimated value of any regression coefficient between two consecutive iterations is less than a certain set threshold, the iterative weighted least squares estimated value of the regression coefficient is output;

[0097] Specifically, the regression coefficient of each iteration is recalculated, and the adjusted residual value of the next iteration is calculated according to the calculation formula of the adjusted residual; the iterative calculation is repeated until the difference between the estimated value of the regression coefficient in two consecutive iterations is less than the preset threshold, and the regression coefficient obtained in the last iteration is determined as the target regression coefficient between the node current of each node and the phase current of the target phase line. For example, if the estimated value of any regression coefficient is , m is the target phase line, j is the jth user, and the change in two adjacent iterations is less than the preset threshold ,Right now , g is the g-th iteration, then the target regression coefficient between the node current of each node and the phase current of the target phase line m is output .

[0098] S207: Using different phase line currents as dependent variables, obtain a series of estimated values ​​of regression coefficients.

[0099] Specifically, Figure 6 Schematic diagram of the regression coefficient of phase A after multiple iterative calculations according to the embodiment of the present application. Figure 7 Schematic diagram of the regression coefficient of phase B after multiple iterative calculations according to the embodiment of the present application. Figure 8 Schematic diagram of the regression coefficient of phase C after multiple iterative calculations according to the embodiment of the present application.

[0100] S208: Determine whether the user node is grouped into the phase line-user node subset based on the estimated regression coefficient relationship between the user node and the phase line.

[0101] Specifically, according to Figure 6 、 Figure 7 and Figure 8 For each node, the regression coefficient for each of the three phases ABC is taken as the maximum value. The phase sequence corresponding to the maximum value is the phase line (phase line) corresponding to the node (user node). For example, for user node 1, the final regression coefficient estimates for the three phases ABC are 1.69, 0.15, and 0.89 respectively, so it is determined that user node 1 belongs to phase A.

[0102] S209: Traverse all phase lines to obtain each phase line-user node subset.

[0103] Figure 9 Schematic diagram of the topological relationship between the phase line and the user node subset provided in an embodiment of the present application, such as Figure 9 As shown, the phase line represents a phase in a group of outgoing lines, the user node represents the user connected to the connected line, and the phase line-user node subset represents the phase where any user in the data set is located, which can reflect the topological relationship of the group of outgoing lines.

[0104] S210: Output each phase line-user node subset as the topological relationship of the outgoing line group.

[0105] Specifically, the real data is: users 1-21 are connected to phase A, users 22-42 are connected to phase B, and users 43-63 are connected to phase C. Suppose method 1 M1: iterative weighted least squares method, which is also the optional phase-user relationship identification method provided in this embodiment; method 2 M2: ordinary least squares method. By comparing the experimental results of the two different methods, it is concluded that the iterative weighted least squares method is more superior. Repeatedly conduct 5 groups of experiments, each group of experiments sets the M1 and M2 methods for comparison, and all are carried out according to steps S201-S210. Figure 10 It is a schematic diagram of the comparison results of calculating the regression coefficient by the iterative weighted least squares method and the ordinary least squares method provided in the embodiment of the present application.

[0106] From the simulation calculation, it can be seen that compared with the ordinary least squares method, the topology recognition accuracy of the iterative weighted least squares method is likely to be improved, and the accuracy can be maintained above 95%.

[0107] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0108] The present application also provides a low-voltage distribution network phase-to-household relationship identification device. It should be noted that the low-voltage distribution network phase-to-household relationship identification device of the present application can be used to execute the phase-to-household relationship identification method for a low-voltage distribution network provided in the present application. The following describes the low-voltage distribution network phase-to-household relationship identification device provided in the present application.

[0109] Figure 11 Schematic diagram of a phase-to-household relationship identification device for a low-voltage distribution network according to an embodiment of the present application. Figure 11 As shown, the device includes:

[0110] The acquisition unit 1101 is configured to acquire the node current of each node on the target low-voltage distribution network and the phase current of each phase line of the target low-voltage distribution network, generate a node current matrix based on the node currents of all nodes, and generate a phase current sequence of the phase line based on the phase current of each phase line, wherein the node is used to represent a user using the target low-voltage distribution network;

[0111] a calculation unit 1102 for determining, for each phase line, an initial weight of the node current of each node, calculating an initial regression coefficient and an initial adjusted residual value between the node current of each node and the phase line current of the target phase line based on the initial weight and a weighted least squares method, updating the initial weight using a preset weight function and the initial adjusted residual value, and calculating the regression coefficient and the adjusted residual value based on the updated weight, performing multiple iterative calculations until the difference between the regression coefficients calculated in two adjacent iterative calculations is less than a preset threshold, and determining the regression coefficient obtained in the last iteration as the target regression coefficient between the node current of each node and the phase line current of the target phase line, wherein the adjusted residual value obtained in each iteration is used to update the weight of the next iteration;

[0112] Determination unit 1103 is used to determine the target regression coefficient between the node current of the target node and the phase line current of each phase line, determine the phase line corresponding to the maximum target regression coefficient as the target phase line belonging to the target node, and combine the phase lines belonging to all nodes according to the phase line type to obtain the user set of each phase line.

[0113] The phase-to-user relationship identification device of the low-voltage distribution network provided in the embodiment of the present application collects the node current of each node on the target low-voltage distribution network and the phase line current of each phase line of the target low-voltage distribution network through the collection unit 1101, and generates a node current matrix based on the node current of all nodes, and generates a phase line current sequence of the phase line based on the phase line current of each phase line, wherein the node is used to characterize the user using the target low-voltage distribution network; the calculation unit 1102 determines the initial weight of the node current of each node for each phase line, calculates the initial regression coefficient and the initial adjusted residual value between the node current of each node and the phase line current of the target phase line based on the initial weight and weighted least squares method, updates the initial weight through the preset weight function and the initial adjusted residual value, calculates the regression coefficient and the adjusted residual value based on the updated weight, and iterates the calculation multiple times until the difference between the regression coefficients calculated in two adjacent iterative calculations is less than 0. The difference is less than a preset threshold, and the regression coefficient obtained in the last iteration is determined as the target regression coefficient between the node current of each node and the phase line current of the target phase line, wherein the adjusted residual value obtained in each iteration is used to update the weight of the next iteration; a determination unit 1103 determines the target regression coefficient between the node current of the target node and the phase line current of each phase line, and determines the phase line corresponding to the maximum target regression coefficient as the target phase line to which the target node belongs, and combines the phase lines to which all nodes belong according to the phase line type to obtain the user set of each phase line, thereby solving the problem of low phase-to-household relationship recognition accuracy of the low-voltage distribution network in the related art, and calculating the regression coefficient between the node current of the user and the phase line current of the phase line through multiple iterations of the weighted least squares method, thereby determining the phase line to which each user belongs according to the regression coefficient, thereby achieving the effect of improving the phase-to-household relationship recognition accuracy of the low-voltage distribution network.

[0114] Optionally, in the phase-to-household relationship identification device of the low-voltage distribution network provided in an embodiment of the present application, the acquisition unit 1101 includes: a first acquisition module, used to collect T node currents of the target node at preset time intervals for each node, generate a node current sequence of the target node, and generate a node current matrix based on the node current sequence of N nodes, where T and N are both positive integers; a second acquisition module, used to collect T phase line currents of the target phase line at preset time intervals for each phase line, and generate a phase line current sequence of the target phase line.

[0115] Optionally, in the phase-to-household relationship identification device of the low-voltage distribution network provided in an embodiment of the present application, the calculation unit 1102 includes: an input module, used to determine the initial weight matrix based on the initial weights of all nodes, and input the initial weight matrix, the node current matrix and the phase line current sequence of the target phase line into the regression coefficient calculation formula to obtain the initial regression coefficient; a first calculation module, used to calculate the fitting residual value of each node through the initial regression coefficient, the node current matrix and the phase line current sequence of the target phase line; and a second calculation module, used to calculate the initial adjusted residual value based on the fitting residual value of each node.

[0116] Optionally, in the phase-to-household relationship identification device of the low-voltage distribution network provided in an embodiment of the present application, the first calculation module includes: a first calculation sub-module, used to calculate the product of the initial regression coefficient and the node current matrix to obtain a current prediction value sequence for each node; a second calculation sub-module, used to calculate the difference between the phase line current sequence of the target phase line and the current prediction value sequence of each node to obtain a fitting residual value for each node.

[0117] Optionally, in the phase-to-household relationship identification device for a low-voltage distribution network provided in an embodiment of the present application, the second calculation module includes: a first determination submodule, used to determine the adjustment constant parameters and preset parameters, and calculate the standard deviation of the error term based on the fitting residual values ​​of all nodes; a third calculation submodule, used to calculate the product of the adjustment constant parameters, the preset parameters and the standard deviation of the error term, and calculate the ratio of the fitting residual value of each node to the product to obtain the initial adjustment residual value of the node.

[0118] Optionally, in the phase-to-household relationship identification device for a low-voltage distribution network provided in an embodiment of the present application, the device also includes: a first judgment unit, used to judge whether the absolute value of the adjusted residual value obtained in the previous iteration is greater than or equal to the adjusted residual value threshold; a weight determination unit, used to determine the weight of the current iteration as a first preset value when the absolute value of the adjusted residual value obtained in the previous iteration is greater than or equal to the adjusted residual value threshold; a weight calculation unit, used to calculate the square of the adjusted residual value to obtain a target value when the absolute value of the adjusted residual value obtained in the previous iteration is less than the adjusted residual value threshold, and calculate the square of the difference between the second preset value and the target value to obtain the weight of the current iteration.

[0119] Optionally, in the phase-to-household relationship identification device for a low-voltage distribution network provided in an embodiment of the present application, the device further includes: a second judgment unit, used to determine the number of iterations of the regression coefficient, and to judge whether the number of iterations is greater than or equal to an iteration number threshold; an execution unit, used to perform multiple iterative calculation steps when the number of iterations is less than the iteration number threshold; a regression coefficient determination unit, used to stop the iteration when the number of iterations is greater than or equal to the iteration number threshold, and the difference between the regression coefficients calculated in two adjacent iterative calculations is greater than or equal to a preset threshold, and to determine the regression coefficient obtained in the last iteration as the target regression coefficient between the node current of each user and the target phase line current.

[0120] The phase-to-household relationship identification device of the low-voltage distribution network includes a processor and a memory. The above-mentioned acquisition unit 1101, calculation unit 1102 and determination unit 1103 are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize the corresponding functions.

[0121] The processor contains a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and the accuracy of the phase-to-household relationship recognition of the low-voltage distribution network can be improved by adjusting the kernel parameters.

[0122] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0123] An embodiment of the present invention provides a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, a method for identifying a phase-to-household relationship in a low-voltage distribution network is implemented.

[0124] An embodiment of the present invention provides a processor, which is used to run a program, wherein the program executes a method for identifying phase-to-household relationships in a low-voltage distribution network when running.

[0125] Figure 12 Schematic diagram of an electronic device according to an embodiment of the present application. Figure 12As shown, the electronic device 1201 includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the following steps are implemented: collecting the node current of each node on the target low-voltage distribution network and the phase line current of each phase line of the target low-voltage distribution network, generating a node current matrix based on the node currents of all nodes, and generating a phase line current sequence of the phase line based on the phase line current of each phase line, wherein the node is used to characterize the user using the target low-voltage distribution network; for each phase line, determining the initial weight of the node current of each node, calculating the initial regression coefficient and the initial adjusted residual value between the node current of each node and the phase line current of the target phase line based on the initial weight and the weighted least squares method, and Assume that the weight function and the initial adjusted residual value are used to update the initial weights. The regression coefficient and adjusted residual value are calculated based on the updated weights. The calculation is repeated multiple times until the difference between the regression coefficients calculated in two consecutive iterations is less than a preset threshold. The regression coefficient obtained in the last iteration is determined as the target regression coefficient between the node current of each node and the phase current of the target phase line. The adjusted residual value obtained in each iteration is used to update the weight of the next iteration. The target regression coefficient between the node current of the target node and the phase current of each phase line is determined. The phase line corresponding to the maximum target regression coefficient is determined as the target phase line belonging to the target node. The phase lines belonging to all nodes are combined according to the phase line type to obtain the user set of each phase line. The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0126] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program initialized with the following method steps: collecting the node current of each node on the target low-voltage distribution network and the phase line current of each phase line of the target low-voltage distribution network, and generating a node current matrix based on the node currents of all nodes, and generating a phase line current sequence of the phase line based on the phase line current of each phase line, wherein the node is used to characterize the user using the target low-voltage distribution network; for each phase line, determining the initial weight of the node current of each node, calculating the initial regression coefficient and the initial adjusted residual value between the node current of each node and the phase line current of the target phase line based on the initial weight and the weighted least squares method, and The initial weights are updated based on the number and the initial adjusted residual value, and the regression coefficient and the adjusted residual value are calculated based on the updated weights. The calculation is iterated multiple times until the difference between the regression coefficients calculated in two adjacent iterations is less than a preset threshold value, and the regression coefficient obtained in the last iteration is determined as the target regression coefficient between the node current of each node and the phase line current of the target phase line, wherein the adjusted residual value obtained in each iteration is used to update the weight of the next iteration; the target regression coefficient between the node current of the target node and the phase line current of each phase line is determined, and the phase line corresponding to the maximum target regression coefficient is determined as the target phase line to which the target node belongs. The phase lines of all nodes are combined according to the phase line type to obtain the user set of each phase line.

[0127] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0128] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0129] 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.

[0130] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating 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.

[0131] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0132] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0133] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can be implemented using any method or technology for information storage. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change RAM (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0134] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0135] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0136] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for identifying phase-to-household relationships in a low-voltage distribution network, characterized in that: include: Collecting the node current of each node on the target low-voltage distribution network and the phase line current of each phase line of the target low-voltage distribution network, generating a node current matrix based on the node currents of all nodes, and generating a phase line current sequence of the phase line based on the phase line current of each phase line, wherein the nodes are used to characterize users using the target low-voltage distribution network; For each phase line, determine the initial weight of the node current of each node, calculate the initial regression coefficient and initial adjustment residual value between the node current of each node and the phase line current of the target phase line based on the initial weight and weighted least squares method, update the initial weight by a preset weight function and the initial adjustment residual value, calculate the regression coefficient and the adjustment residual value based on the updated weight, perform multiple iterations until the difference between the regression coefficients calculated in two adjacent iterations is less than a preset threshold, determine the regression coefficient obtained in the last iteration as the target regression coefficient between the node current of each node and the phase line current of the target phase line, wherein the adjusted residual value obtained in each iteration is used to update the weight of the next iteration; Determine the target regression coefficient between the node current of the target node and the phase line current of each phase line, determine the phase line corresponding to the maximum target regression coefficient as the target phase line to which the target node belongs, combine the phase lines to which all nodes belong according to the phase line type, and obtain a user set for each phase line.

2. The method according to claim 1, characterized in that Collecting the node current of each node on the target low-voltage distribution network and the phase line current of each phase line of the target low-voltage distribution network, and generating a node current matrix based on the node currents of all nodes, and generating a phase line current sequence of the phase line based on the phase line current of each phase line includes: For each node, T node currents of the target node are collected at preset time intervals to generate a node current sequence of the target node, and the node current matrix is ​​generated based on the node current sequences of N nodes, where T and N are both positive integers; For each phase line, T phase line currents of the target phase line are collected at preset time intervals to generate a phase line current sequence of the target phase line.

3. The method according to claim 1, characterized in that Calculating the initial regression coefficient and the initial adjustment residual value between the node current of each node and the phase line current of the target phase line based on the initial weight and the weighted least square method includes: An initial weight matrix is ​​determined based on the initial weights of all nodes, and the initial weight matrix, the node current matrix and the phase line current sequence of the target phase line are input into a regression coefficient calculation formula to obtain the initial regression coefficient; Calculating the fitting residual value of each node through the initial regression coefficient, the node current matrix and the phase line current sequence of the target phase line; The initial adjusted residual value is calculated based on the fitting residual value of each node.

4. The method according to claim 3, characterized in that Calculating the fitting residual value of each node by using the initial regression coefficient, the node current matrix and the phase line current sequence of the target phase line includes: Calculating the product of the initial regression coefficient and the node current matrix to obtain a current prediction value sequence for each node; The difference between the phase line current sequence of the target phase line and the current prediction value sequence of each node is calculated to obtain the fitting residual value of each node.

5. The method according to claim 3, characterized in that: Calculating the initial adjusted residual value based on the fitting residual value of each node includes: Determine the adjustment constant parameters and preset parameters, and calculate the standard deviation of the error term based on the fitting residual values ​​of all nodes; The product of the adjustment constant parameter, the preset parameter and the standard deviation of the error term is calculated, and the ratio of the fitting residual value of each node to the product is calculated to obtain the initial adjustment residual value of the node.

6. The method according to claim 1, characterized in that The weights for each iteration are calculated as follows: Determine whether the absolute value of the adjusted residual value obtained in the previous iteration is greater than or equal to the adjusted residual value threshold; When the absolute value of the adjusted residual value obtained in the previous iteration is greater than or equal to the adjusted residual value threshold, the weight of the current iteration is determined as a first preset value; When the absolute value of the adjusted residual value obtained in the previous iteration is less than the adjusted residual value threshold, the square of the adjusted residual value is calculated to obtain the target value, and the square of the difference between the second preset value and the target value is calculated to obtain the weight of the current iteration.

7. The method according to claim 1, characterized in that The method further comprises: Determine the iteration number of the regression coefficient, and judge whether the iteration number is greater than or equal to the iteration number threshold; When the number of iterations is less than the iteration number threshold, the steps of multiple iterative calculations are performed; when the number of iterations is greater than or equal to the iteration number threshold, and the difference between the regression coefficients calculated in two adjacent iterative calculations is greater than or equal to a preset threshold, the iteration is stopped, and the regression coefficient obtained from the last iteration is determined as the target regression coefficient between the node current of each user and the target phase line current.

8. A phase-to-household relationship identification device for a low-voltage distribution network, characterized in that: include: A collection unit, used to collect the node current of each node on the target low-voltage distribution network and the phase line current of each phase line of the target low-voltage distribution network, and generate a node current matrix based on the node currents of all nodes, and generate a phase line current sequence of the phase line based on the phase line current of each phase line, wherein the node is used to characterize a user using the target low-voltage distribution network; A calculation unit, for determining, for each phase line, an initial weight of a node current of each node, calculating an initial regression coefficient and an initial adjusted residual value between the node current of each node and the phase line current of a target phase line based on the initial weight and a weighted least square method, updating the initial weight by a preset weight function and the initial adjusted residual value, and calculating the regression coefficient and the adjusted residual value based on the updated weight, performing multiple iterations of calculations until the difference between the regression coefficients calculated in two adjacent iterations is less than a preset threshold value, and determining the regression coefficient obtained in the last iteration as a target regression coefficient between the node current of each node and the phase line current of the target phase line, wherein the adjusted residual value obtained in each iteration is used to update the weight of the next iteration; A determination unit is used to determine a target regression coefficient between a node current of a target node and a phase line current of each phase line, determine a phase line corresponding to a maximum target regression coefficient as a target phase line to which the target node belongs, and combine the phase lines to which all nodes belong according to the phase line type to obtain a user set for each phase line.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for identifying the relationship between households in a low-voltage distribution network as described in any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: It includes one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method for identifying the relationship between households in a low-voltage distribution network as described in any one of claims 1 to 7.

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