Method and device for identifying phase-household relation of low-voltage transformer area based on comprehensive data, and medium

Abstract

The invention discloses a low-voltage transformer area phase-user relation identification method and device based on comprehensive data and a medium, and belongs to the field of power systems, and the method comprises the steps: obtaining voltage monitoring data and current monitoring data of each user in a low-voltage transformer area and three-phase current data of a distribution transformer low-voltage side; performing numerical value judgment on the voltage monitoring data to obtain a change trend of the voltage monitoring data; dividing each user in the low-voltage transformer area based on the similarity of the change trend to obtain a plurality of user clusters; and determining the phase to which each user cluster belongs based on the current monitoring data of each user in each user cluster and the three-phase current data of the low-voltage side of the distribution transformer. Therefore, through implementation of the method and the device, the problem of low accuracy of phase user relation identification caused by phase user relation identification through a single data source in the prior art can be solved.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and in particular to a method, device and medium for identifying phase-to-household relationships in a low-voltage substation based on comprehensive data. Background Art

[0002] With rapid economic development and accelerated urbanization, the number of users within low-voltage substations has increased significantly, and the topology of the low-voltage power grid has undergone frequent changes. However, due to the inefficiency of manual troubleshooting and the high cost of external equipment, the correspondence between users and power supply phases (i.e., the phase-to-household relationship) cannot be updated in a timely manner. This lag has had a significant impact on the upgrade and renovation of low-voltage lines and the refined management of substations.

[0003] In recent years, with the gradual establishment of Advanced Metering Infrastructure (AMI), data-driven lean management methods for low-voltage power distribution and utilization systems have emerged. Among them, phase-to-phase relationship identification methods based on monitoring data, as a key component of lean management of low-voltage power distribution and utilization systems, have garnered widespread attention due to their advantages, such as rapid identification speed and low operating costs. Currently, data-driven phase-to-phase relationship identification algorithms primarily utilize monitoring data such as voltage, power, and current. The identification method based on voltage monitoring data: First, the time series characteristics of user voltage monitoring data are extracted to divide users into multiple clusters. Second, the voltage characteristics of the user clusters are matched with the three-phase voltage characteristics of the distribution transformer's low-voltage side to identify the phases of each user cluster. The identification method based on power monitoring data: Utilizing the principle of power conservation, the relationship between users and phases is established by minimizing the deviation between the total power of each phase in the low-voltage substation and the power on the distribution transformer side. The identification method based on current monitoring data: Based on Kirchhoff's current law, a quadratic programming optimization model is constructed for low-voltage substation user relationship identification. However, due to the limited amount of information from a single data source and the difficulty in ensuring data quality, the method based on power and current monitoring data is difficult to divide clusters of no-load or light-load users. In addition, with the promotion of the work of managing three-phase voltage imbalance in low-voltage substations, the difference in three-phase voltage on the low-voltage side of the distribution transformer has narrowed, and the difficulty of phase-to-household relationship identification based on voltage monitoring data has increased. Therefore, the accuracy of the existing phase-to-household relationship identification method still has a lot of room for improvement. Summary of the Invention

[0004] The present invention provides a method, device and medium for identifying the relationship between phases and households in a low-voltage substation based on comprehensive data, which can comprehensively utilize voltage and current monitoring data to improve the accuracy of identifying the relationship between phases and households in a low-voltage substation.

[0005] The embodiment of the present invention provides a method for identifying the relationship between low-voltage substations and households based on comprehensive data, comprising:

[0006] Obtain voltage monitoring data, current monitoring data and three-phase current data of the low-voltage side of the distribution transformer for each user in the low-voltage substation area;

[0007] Performing numerical judgment on the voltage monitoring data to obtain a change trend of the voltage monitoring data;

[0008] Based on the similarity of the change trends, each user in the low-voltage area is divided to obtain multiple user clusters;

[0009] Based on the current monitoring data of each user in each user cluster and the three-phase current data on the low-voltage side of the distribution transformer, the phase to which each user cluster belongs is determined.

[0010] This embodiment of the present invention first divides users into clusters based on the changing trends of voltage monitoring data. It then identifies phase-to-user relationships based on the current monitoring data and three-phase current data of each user in the cluster, thereby determining the phase to which each user cluster belongs. Compared to the existing technology that uses a single data source for phase-to-user relationship identification, which results in low accuracy, this application improves the accuracy of phase-to-user relationship identification by integrating voltage and current monitoring data.

[0011] Furthermore, performing numerical judgment on the voltage monitoring data to obtain a change trend of the voltage monitoring data includes:

[0012] Constructing multiple first voltage sequences according to sampling points of voltage monitoring data of each user; wherein one user corresponds to one first voltage sequence;

[0013] Numerical judgment is performed based on the first voltage sequence to obtain all trend turning points in the first voltage sequence.

[0014] The embodiment of the present invention determines the trend turning point through voltage monitoring data, providing a data basis for subsequent similarity judgment.

[0015] Furthermore, performing numerical judgment based on the first voltage sequence to obtain all trend turning points in the first voltage sequence includes:

[0016] Obtaining voltage monitoring data corresponding to each sampling point in the first voltage sequence;

[0017] When the voltage monitoring data corresponding to a sampling point meets a preset condition, the sampling point is determined as a trend turning point; wherein the preset condition is specifically:

[0018]

[0019] Among them, t is the trend turning point; t-1 is the previous sampling point of the trend turning point; t+1 is the next sampling point of the trend turning point; ut is the voltage monitoring data corresponding to the trend turning point; u t-1 is the voltage monitoring data corresponding to the previous sampling point of the trend turning point; u t+1 It is the voltage monitoring data corresponding to the next sampling point of the trend turning point.

[0020] The embodiment of the present invention provides a judgment basis for extracting trend turning points by defining change trend rules.

[0021] Furthermore, based on the similarity of the change trend, each user in the low-voltage area is divided to obtain multiple user clusters, including:

[0022] Based on the common trend turning points among different first voltage sequences, the first voltage sequences are rearranged to obtain a plurality of second voltage sequences; wherein one user corresponds to one second voltage sequence;

[0023] Performing similarity judgment based on the second voltage sequence to obtain a voltage similarity matrix;

[0024] The users in the low-voltage area are divided according to the voltage similarity matrix to obtain a plurality of user clusters.

[0025] The embodiment of the present invention provides a basis for user cluster division by calculating the similarity of the change trends.

[0026] Furthermore, the determining of the phase to which each user cluster belongs based on the current monitoring data of each user in each user cluster and the three-phase current data on the low-voltage side of the distribution transformer includes:

[0027] Sum the current monitoring data of each user in each user cluster to obtain the total current monitoring data;

[0028] Based on the total current monitoring data and the three-phase current data, an optimization function is established with the loss minimization as the goal;

[0029] The optimization function is solved to obtain the difference in the cluster to which each user belongs.

[0030] The embodiment of the present invention establishes and solves the optimization function through total current monitoring data and three-phase current data, providing data support for the identification of phase-to-household relationships in low-voltage substations.

[0031] Furthermore, based on the total current monitoring data and the three-phase current data, an optimization function is established with the goal of minimizing loss, specifically:

[0032]

[0033] Where, f is the loss amount; I tris the three-phase current data; I is the total current monitoring data; β is the matrix of correlation coefficients; λ is the regularization parameter; and R is the constraint parameter.

[0034] The embodiment of the present invention establishes the relationship between the total current monitoring data and the three-phase current data through the objective function and the constraint conditions, which is helpful for the subsequent calculation of the correlation coefficient.

[0035] Furthermore, solving the optimization function to obtain the difference between each user cluster includes:

[0036] Solving the optimization function to obtain the correlation coefficient between each user cluster and each phase;

[0037] By comparing the sizes of the correlation coefficients, we can determine the difference in the cluster to which each user belongs.

[0038] The embodiment of the present invention obtains the correlation coefficient between each user cluster and each user group by solving the optimization function, thereby providing a judgment basis for identifying the relationship between each user group.

[0039] Another embodiment of the present invention further provides a low-voltage substation phase-to-household relationship identification device based on comprehensive data, comprising: a data acquisition module, a change trend module, a cluster division module, and a phase difference judgment module;

[0040] The data acquisition module is used to obtain voltage monitoring data, current monitoring data and three-phase current data of the low-voltage side of the distribution transformer for each user in the low-voltage area;

[0041] The change trend module is used to perform numerical judgment on the voltage monitoring data to obtain the change trend of the voltage monitoring data;

[0042] The cluster division module is used to divide each user in the low-voltage area based on the similarity of the change trend to obtain multiple user clusters;

[0043] The phase difference judgment module is used to determine the phase difference to which each user cluster belongs based on the current monitoring data of each user in each user cluster and the three-phase current data on the low-voltage side of the distribution transformer.

[0044] Another embodiment of the present invention also provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps of a low-voltage substation household relationship identification method based on comprehensive data as described in the present invention.

[0045] Another embodiment of the present invention also provides a computer-readable storage medium item, including: a stored computer program, which controls the device where the computer-readable storage medium is located to execute the steps of a low-voltage substation household relationship identification method based on comprehensive data according to the present invention when the computer program is running. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A flow chart of a method for identifying low-voltage substation-to-household relationships based on comprehensive data provided by an embodiment of the present invention;

[0047] Figure 2 A system flow chart of a method for identifying low-voltage substation-to-household relationships based on comprehensive data provided by an embodiment of the present invention;

[0048] Figure 3 A schematic structural diagram of a low-voltage substation-household relationship identification device based on comprehensive data provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions in this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0051] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0052] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0053] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0054] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0055] See also Figure 1 In order to solve the problem of low accuracy in identifying household relationships due to the existing technology of using a single data source to identify household relationships, an embodiment of the present invention provides a method for identifying household relationships in a low-voltage substation area based on comprehensive data, including steps S101 to S104;

[0056] Step S101: Obtain voltage monitoring data, current monitoring data, and three-phase current data on the low-voltage side of the distribution transformer of each user in the low-voltage substation.

[0057] Optionally, after obtaining the voltage monitoring data of each user in the low-voltage substation from the power grid detection system, a first-order linear interpolation method is used to fill in the missing parts of the voltage monitoring data.

[0058] Step S102 : performing numerical judgment on the voltage monitoring data to obtain a change trend of the voltage monitoring data.

[0059] Furthermore, step S102 includes:

[0060] Constructing multiple first voltage sequences according to sampling points of voltage monitoring data of each user; wherein one user corresponds to one first voltage sequence;

[0061] Numerical judgment is performed based on the first voltage sequence to obtain all trend turning points in the first voltage sequence.

[0062] For example, if the total number of sampling points of the voltage monitoring data is m, the first voltage sequence is represented as U={u1, u2…, u m}; where u1,u2…,u m is the voltage monitoring data corresponding to sampling points 1, 2…, m.

[0063] The embodiment of the present invention determines the trend turning point through voltage monitoring data, providing a data basis for subsequent similarity judgment.

[0064] Furthermore, performing numerical judgment based on the first voltage sequence to obtain all trend turning points in the first voltage sequence includes:

[0065] Obtaining voltage monitoring data corresponding to each sampling point in the first voltage sequence;

[0066] When the voltage monitoring data corresponding to a sampling point meets a preset condition, the sampling point is determined as a trend turning point; wherein the preset condition is specifically:

[0067]

[0068] Among them, t is the trend turning point; t-1 is the previous sampling point of the trend turning point; t+1 is the next sampling point of the trend turning point; u t is the voltage monitoring data corresponding to the trend turning point; u t-1 is the voltage monitoring data corresponding to the previous sampling point of the trend turning point; u t+1 It is the voltage monitoring data corresponding to the next sampling point of the trend turning point.

[0069] The embodiment of the present invention provides a judgment basis for extracting trend turning points by defining change trend rules.

[0070] Step S103: Based on the similarity of the change trends, the users in the low-voltage area are divided to obtain multiple user clusters.

[0071] Furthermore, step S103 includes:

[0072] Based on the common trend turning points among different first voltage sequences, the first voltage sequences are rearranged to obtain a plurality of second voltage sequences; wherein one user corresponds to one second voltage sequence;

[0073] Performing similarity judgment based on the second voltage sequence to obtain a voltage similarity matrix;

[0074] The users in the low-voltage area are divided according to the voltage similarity matrix to obtain a plurality of user clusters.

[0075] For example, if the trend turning point sets in the first voltage sequence corresponding to user i and user j are T i and T j , then T iand T j The set of common trend turning points between ij =T i ∩T j ; Among them, T ij Each element in is a common trend turning point in the first voltage sequence corresponding to user i and user j.

[0076] For example, based on T ij T i and T j Rearrange, specifically:

[0077] U′ i ={u i,t |t∈T ij};

[0078] U′ j ={u j,t |t∈T ij};

[0079] Among them, U′ i and U′ j are the second voltage sequences corresponding to user i and user j respectively; u i,t and u j,t are the voltage monitoring data of the t-th sampling point in the first voltage sequence corresponding to user i and user j respectively.

[0080] Exemplarily, the Euclidean distance between the second voltage sequences is used as a similarity measurement indicator, specifically:

[0081]

[0082] Among them, d ij U′ i and U′ j The Euclidean distance between ij The total number of elements in u′ i,t and u′ j,t are the voltage monitoring data of the t-th sampling point in the second voltage sequence corresponding to user i and user j respectively.

[0083] For example, for each user in the low-voltage area, the following voltage similarity matrix can be formed:

[0084]

[0085] For example, according to the voltage similarity matrix, the K-means clustering method is used to divide the users in the low voltage area to obtain multiple user clusters C = [C1, C2, ..., C k]; where k is the number of clusters, and each cluster consists of multiple users with high similarity, forming a user cluster.

[0086] The embodiment of the present invention provides a basis for user cluster division by calculating the similarity of the change trends.

[0087] Step S104 : determining the phase to which each user cluster belongs based on the current monitoring data of each user in each user cluster and the three-phase current data on the low-voltage side of the distribution transformer.

[0088] Furthermore, step S104 includes:

[0089] Sum the current monitoring data of each user in each user cluster to obtain the total current monitoring data;

[0090] Based on the total current monitoring data and the three-phase current data, an optimization function is established with the loss minimization as the goal;

[0091] The optimization function is solved to obtain the difference in the cluster to which each user belongs.

[0092] Exemplarily, the current monitoring data of each user in each user cluster is summed to obtain the total current monitoring data, specifically:

[0093]

[0094] Among them, I is the total current monitoring data; C k The sum of the current monitoring data of each user in the user cluster.

[0095] The embodiment of the present invention establishes and solves the optimization function through total current monitoring data and three-phase current data, providing data support for the identification of phase-to-household relationships in low-voltage substations.

[0096] Furthermore, based on the total current monitoring data and the three-phase current data, an optimization function is established with the goal of minimizing loss, specifically:

[0097]

[0098] Where, f is the loss amount; I tr =[I tr A ,I tr B ,I tr C ] T is the three-phase current data; I is the total current monitoring data; β is the matrix of correlation coefficients; λ is the regularization parameter used to control sparsity; R is the constraint parameter used to control complexity.

[0099] Optionally, the optimization function is established based on a LASSO regression algorithm.

[0100] For example, the matrix of correlation coefficients is:

[0101]

[0102] in, C k The correlation coefficient between the user cluster and phase A; C k The correlation coefficient between the user cluster and phase B; C k Correlation coefficient between user cluster and C phase.

[0103] The embodiment of the present invention establishes the relationship between the total current monitoring data and the three-phase current data through the objective function and the constraint conditions, which is helpful for the subsequent calculation of the correlation coefficient.

[0104] Furthermore, solving the optimization function to obtain the difference between each user cluster includes:

[0105] Solving the optimization function to obtain the correlation coefficient between each user cluster and each phase;

[0106] By comparing the sizes of the correlation coefficients, we can determine the difference in the cluster to which each user belongs.

[0107] Optionally, a coordinate descent method is used to solve the optimization function.

[0108] The embodiment of the present invention obtains the correlation coefficient between each user cluster and each user group by solving the optimization function, thereby providing a judgment basis for identifying the relationship between each user group.

[0109] This embodiment of the present invention first divides users into clusters based on the changing trends of voltage monitoring data. It then identifies phase-to-user relationships based on the current monitoring data and three-phase current data of each user in the cluster, thereby determining the phase to which each user cluster belongs. Compared to the existing technology that uses a single data source for phase-to-user relationship identification, which results in low accuracy, this application improves the accuracy of phase-to-user relationship identification by integrating voltage and current monitoring data.

[0110] like Figure 2 As shown, based on the above method embodiment, a corresponding system embodiment is provided, and the system needs to perform clustering tasks and phase determination tasks;

[0111] The system performs a clustering task to obtain multiple user clusters; wherein the clustering task includes: obtaining user voltage monitoring data, filling missing voltage monitoring data using first-order linear interpolation, extracting voltage trend turning points, screening and arranging user voltage monitoring data based on common trend turning points, calculating a similarity matrix, and obtaining k user clusters using K-means clustering based on the similarity matrix;

[0112] Based on multiple user clusters, the system performs phase identification tasks to obtain user phase identification results; wherein, the phase identification tasks include: obtaining user current monitoring data and three-phase current data on the low-voltage side of the distribution transformer, calculating user cluster current, establishing a LASSO regression optimization model based on user cluster current and three-phase current data, judging user cluster phase based on the LASSO regression optimization model, and outputting user phase identification results.

[0113] like Figure 3 As shown, based on the above method embodiment, a corresponding device embodiment is provided;

[0114] An embodiment of the present invention provides a low-voltage substation phase-household relationship identification device based on comprehensive data, comprising: a data acquisition module 301, a change trend module 302, a cluster division module 303 and a phase difference judgment module 304;

[0115] The data acquisition module 301 is used to obtain voltage monitoring data, current monitoring data and three-phase current data of the low-voltage side of the distribution transformer for each user in the low-voltage area;

[0116] The change trend module 302 is used to perform numerical judgment on the voltage monitoring data to obtain the change trend of the voltage monitoring data;

[0117] The cluster division module 303 is used to divide the users in the low voltage area based on the similarity of the change trend to obtain multiple user clusters;

[0118] The phase difference determination module 304 is configured to determine the phase difference to which each user cluster belongs based on the current monitoring data of each user in each user cluster and the three-phase current data on the low-voltage side of the distribution transformer.

[0119] In the embodiment of the present invention, the change trend module 302 includes: a first voltage sequence submodule and a trend turning point submodule;

[0120] The first voltage sequence submodule is configured to construct a plurality of first voltage sequences according to the sampling points of the voltage monitoring data of each user; wherein one user corresponds to one first voltage sequence;

[0121] The trend turning point submodule is used to perform numerical judgment based on the first voltage sequence to obtain all trend turning points in the first voltage sequence.

[0122] The embodiment of the present invention determines the trend turning point through voltage monitoring data, providing a data basis for subsequent similarity judgment.

[0123] In an embodiment of the present invention, the trend turning point submodule includes: a data acquisition unit and a trend turning point unit;

[0124] The data acquisition unit is used to acquire voltage monitoring data corresponding to each sampling point in the first voltage sequence;

[0125] The trend turning point unit is configured to determine a sampling point as a trend turning point when the voltage monitoring data corresponding to the sampling point meets a preset condition; wherein the preset condition is specifically:

[0126]

[0127] Among them, t is the trend turning point; t-1 is the previous sampling point of the trend turning point; t+1 is the next sampling point of the trend turning point; u t is the voltage monitoring data corresponding to the trend turning point; u t-1 is the voltage monitoring data corresponding to the previous sampling point of the trend turning point; u t+1 It is the voltage monitoring data corresponding to the next sampling point of the trend turning point.

[0128] The embodiment of the present invention provides a judgment basis for extracting trend turning points by defining change trend rules.

[0129] In the embodiment of the present invention, the cluster division module 303 includes: a second voltage sequence submodule, a similarity judgment submodule and a cluster division submodule;

[0130] The second voltage sequence submodule is configured to rearrange the first voltage sequences based on the common trend turning points between different first voltage sequences to obtain a plurality of second voltage sequences; wherein one user corresponds to one second voltage sequence;

[0131] The similarity judgment submodule is used to perform similarity judgment based on the second voltage sequence to obtain a voltage similarity matrix;

[0132] The cluster division submodule is used to divide each user in the low-voltage area according to the voltage similarity matrix to obtain multiple user clusters.

[0133] The embodiment of the present invention provides a basis for user cluster division by calculating the similarity of the change trends.

[0134] In the embodiment of the present invention, the phase difference judgment module 304 includes: a data summation submodule, an optimization function submodule and a phase difference judgment submodule;

[0135] The data summing submodule is used to sum the current monitoring data of each user in each user cluster to obtain the total current monitoring data;

[0136] The optimization function submodule is used to establish an optimization function based on the total current monitoring data and the three-phase current data with the goal of minimizing losses;

[0137] The phase difference judgment submodule is used to solve the optimization function to obtain the phase difference to which each user cluster belongs.

[0138] The embodiment of the present invention establishes and solves the optimization function through total current monitoring data and three-phase current data, providing data support for the identification of phase-to-household relationships in low-voltage substations.

[0139] In an embodiment of the present invention, the optimization function submodule is specifically:

[0140]

[0141] Where, f is the loss amount; I tr is the three-phase current data; I is the total current monitoring data; β is the matrix of correlation coefficients; λ is the regularization parameter; and R is the constraint parameter.

[0142] The embodiment of the present invention establishes the relationship between the total current monitoring data and the three-phase current data through the objective function and the constraint conditions, which is helpful for the subsequent calculation of the correlation coefficient.

[0143] In the embodiment of the present invention, the phase difference judgment submodule includes: a function solving unit and a phase difference judgment unit;

[0144] The function solving unit is used to solve the optimization function to obtain the correlation coefficient between each user cluster and each phase;

[0145] The phase difference determination unit is used to determine the phase difference to which each user cluster belongs by comparing the magnitudes of the correlation coefficients.

[0146] The embodiment of the present invention obtains the correlation coefficient between each user cluster and each user group by solving the optimization function, thereby providing a judgment basis for identifying the relationship between each user group.

[0147] It can be understood that the above-mentioned device embodiment corresponds to the method embodiment of the present invention, which can implement a low-voltage substation household relationship identification method based on comprehensive data provided by any of the above-mentioned method embodiments of the present invention.

[0148] In this embodiment of the present invention, a cluster division module first divides users into clusters based on the changing trends of voltage monitoring data. A phase distinction module then identifies phase-to-user relationships based on the current monitoring data and three-phase current data of each user in the divided user clusters, thereby determining the phase to which each user cluster belongs. Compared to the prior art, which uses a single data source to identify phase-to-user relationships, resulting in low accuracy, this application improves the accuracy of phase-to-user relationship identification by integrating voltage and current monitoring data.

[0149] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. Furthermore, in the drawings of the device embodiments provided by the present invention, the connection relationship between modules indicates that they have a communication connection, which may be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement the present invention without inventive effort.

[0150] Based on the above-mentioned embodiment of a method for identifying the relationship between low-voltage substations and households based on comprehensive data, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a method for identifying the relationship between low-voltage substations and households based on comprehensive data according to any embodiment of the present invention.

[0151] For example, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more module elements may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0152] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0153] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, connecting various parts of the entire terminal device using various interfaces and lines.

[0154] Based on the above-mentioned method embodiments, another embodiment of the present invention provides a computer-readable storage medium, including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a low-voltage substation household relationship identification method based on comprehensive data as described in any one of the above-mentioned method embodiments of the present invention.

[0155] Wherein, the module / unit integrated in the device / terminal equipment, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0156] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for identifying the relationship between low-voltage substations and households based on comprehensive data, characterized in that: include: Obtain voltage monitoring data, current monitoring data and three-phase current data of the low-voltage side of the distribution transformer for each user in the low-voltage area; Performing numerical judgment on the voltage monitoring data to obtain a change trend of the voltage monitoring data; Based on the similarity of the change trends, each user in the low-voltage area is divided to obtain multiple user clusters; Based on the current monitoring data of each user in each user cluster and the three-phase current data on the low-voltage side of the distribution transformer, the phase to which each user cluster belongs is determined.

2. The method for identifying the relationship between low-voltage substations and households based on comprehensive data according to claim 1, characterized in that: The performing numerical judgment on the voltage monitoring data to obtain a change trend of the voltage monitoring data includes: Constructing multiple first voltage sequences according to sampling points of voltage monitoring data of each user; wherein one user corresponds to one first voltage sequence; Numerical judgment is performed based on the first voltage sequence to obtain all trend turning points in the first voltage sequence.

3. The method for identifying the relationship between low-voltage substations and households based on comprehensive data according to claim 2, characterized in that: The performing numerical judgment based on the first voltage sequence to obtain all trend turning points in the first voltage sequence includes: Obtaining voltage monitoring data corresponding to each sampling point in the first voltage sequence; When the voltage monitoring data corresponding to a sampling point meets a preset condition, the sampling point is determined as a trend turning point; wherein the preset condition is specifically: Among them, t is the trend turning point; t-1 is the previous sampling point of the trend turning point; t+1 is the next sampling point of the trend turning point; u t is the voltage monitoring data corresponding to the trend turning point; u t-1 is the voltage monitoring data corresponding to the previous sampling point of the trend turning point; u t+1 It is the voltage monitoring data corresponding to the next sampling point of the trend turning point.

4. The method for identifying the relationship between low-voltage substations and households based on comprehensive data according to claim 2, characterized in that: Based on the similarity of the change trend, each user in the low-voltage area is divided to obtain multiple user clusters, including: Based on the common trend turning points among different first voltage sequences, the first voltage sequences are rearranged to obtain a plurality of second voltage sequences; wherein one user corresponds to one second voltage sequence; Performing similarity judgment based on the second voltage sequence to obtain a voltage similarity matrix; The users in the low-voltage area are divided according to the voltage similarity matrix to obtain a plurality of user clusters.

5. The method for identifying the relationship between low-voltage substations and households based on comprehensive data according to claim 1, characterized in that: The determining of the phase to which each user cluster belongs based on the current monitoring data of each user in each user cluster and the three-phase current data on the low-voltage side of the distribution transformer includes: Sum the current monitoring data of each user in each user cluster to obtain the total current monitoring data; Based on the total current monitoring data and the three-phase current data, an optimization function is established with the loss minimization as the goal; The optimization function is solved to obtain the difference in the cluster to which each user belongs.

6. The method for identifying the relationship between low-voltage substations and households based on comprehensive data according to claim 5, characterized in that: Based on the total current monitoring data and the three-phase current data, an optimization function is established with the goal of minimizing the loss, specifically: Where, f is the loss amount; I tr is the three-phase current data; I is the total current monitoring data; β is the matrix of correlation coefficients; λ is the regularization parameter; and R is the constraint parameter.

7. The method for identifying the relationship between low-voltage substations and households based on comprehensive data according to claim 5, characterized in that: Solving the optimization function to obtain the difference between each user cluster includes: Solving the optimization function to obtain the correlation coefficient between each user cluster and each phase; By comparing the sizes of the correlation coefficients, we can determine the difference in the cluster to which each user belongs.

8. A low-voltage substation area household relationship identification device based on comprehensive data, characterized in that: include: Data acquisition module, change trend module, cluster division module and phase difference judgment module; The data acquisition module is used to obtain voltage monitoring data, current monitoring data and three-phase current data of the low-voltage side of the distribution transformer for each user in the low-voltage area; The change trend module is used to perform numerical judgment on the voltage monitoring data to obtain the change trend of the voltage monitoring data; The cluster division module is used to divide each user in the low-voltage area based on the similarity of the change trend to obtain multiple user clusters; The phase difference judgment module is used to determine the phase difference to which each user cluster belongs based on the current monitoring data of each user in each user cluster and the three-phase current data on the low-voltage side of the distribution transformer.

9. A terminal device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a low-voltage substation household relationship identification method based on comprehensive data as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that include: A stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a low-voltage substation household relationship identification method based on comprehensive data as described in any one of claims 1 to 7.

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