A method and related device for identifying abnormal files in a power distribution area

Abstract

The present application discloses a method for identifying abnormal files in a power distribution area and related devices. The method includes: obtaining the power consumption data of the power distribution area according to the metering period, screening and sorting the power consumption data to obtain several groups of power consumption data sets; establishing a 0-1 integer programming mathematical model for the multi-user situation in the power distribution area according to the characteristics of abnormal household-transformer relationships in the power distribution area; establishing a line loss model for the power distribution area through reasoning and analysis based on the total power consumption, current resistance, and work done in the power distribution area; combining the programming mathematical model and the line loss model of the power distribution area to perform data modeling on the power consumption situation of the power distribution area to obtain an abnormal household-transformer relationship identification model; based on the power consumption data sets, obtaining a line loss coefficient for each state matrix in the abnormal household-transformer relationship identification model, thereby obtaining a following curve of the total power consumption of the power distribution area, the power consumption of users, and the line loss sum, and determining the users with abnormal household-transformer relationships in the power distribution area by calculating the mean square error of the following curve. Thus, the problems of large workload and low efficiency in the prior art are solved.

Description

Technical Field

[0001] This application relates to the technical field of identifying the household-transformer relationship in distribution network substations, and particularly to a method for identifying abnormal files in substations and related devices. Background Art

[0002] With the expansion of the distribution network scale and the installation of intelligent charging piles in recent years, it has become more difficult to maintain the file relationships in substations. And the accurate household-transformer relationship in substations is the key to improving the operation and maintenance efficiency of substations and the fault handling efficiency, and is also the premise for enhancing the intelligence of the distribution network. The current household-transformer relationship in substations is mainly maintained manually, and the correction of incorrect files is also mainly carried out through manual on-site verification.

[0003] Currently, relying on manual on-site inspections to solve the problem of abnormal household-transformer relationships, the blindness of on-site verification leads to low efficiency, and it is difficult to achieve a comprehensive investigation of household-transformer relationships, seriously affecting the improvement of the service level and the intelligent operation and maintenance process of low-voltage substations. Therefore, there is an urgent need for a convenient and fast method for identifying abnormal household-transformer relationships in substations. Summary of the Invention

[0004] This application provides a method for identifying abnormal files in substations and related devices, which is used to solve the technical problems of large workload and low efficiency in the prior art.

[0005] In view of this, in the first aspect of this application, a method for identifying abnormal files in substations is provided, and the method includes:

[0006] Obtain the power consumption data of the substation according to the measurement period, screen and sort the power consumption data to obtain several groups of power consumption data groups, where the number of groups of the power consumption data groups is greater than the number of users in the substation;

[0007] According to the characteristics of abnormal household-transformer relationships in the substation, establish a 0-1 integer programming mathematical model for the multi-user situation in the substation; according to the total power consumption, current resistance and work done in the substation, conduct reasoning and analysis to establish a line loss model for the substation;

[0008] Combine the programming mathematical model and the line loss model of the substation to perform data modeling on the power consumption situation of the substation to obtain an abnormal household-transformer relationship identification model;

[0009] Based on the power consumption data groups, obtain a line loss coefficient for each state matrix in the abnormal household-transformer relationship identification model, so as to obtain a following curve of the total power consumption of the substation, the power consumption of users and the line loss sum, and determine the users with abnormal household-transformer relationships in the substation by calculating the mean square error of the following curve.

[0010] Optionally, the abnormal household-transformer relationship identification model is:

[0011] Φ*(1 - x)+LL = Q z ;

[0012] Wherein, Φ is the electricity consumption matrix of users in the substation area, x is the 0-1 state matrix of users, LL is the line loss of the substation area, and Q z is the total electricity consumption recorded by the main meter of the substation area.

[0013] Optionally, the line loss model of the substation area is:

[0014] LL = (coe * ε0).* Φ * (1 - x);

[0015]

[0016] Wherein, ε0 is the line loss rate in the first measurement period, and coe is the coefficient of the line loss rate in each measurement period to the line loss rate in the first measurement period.

[0017] Optionally, determining the users with abnormal household transformation relationships in the substation area by calculating the mean square error of the following curve specifically includes:

[0018] Calculating the value of the mean square error of the following curve based on the mean square error calculation formula;

[0019] By comparing the values of the mean square error, determining the first state matrix corresponding to the minimum value, and determining the users with a state quantity of 1 in the first state matrix as the users with abnormal household transformation relationships in the substation area;

[0020] Wherein, the mean square error calculation formula is:

[0021] mse = E(Q z - (Φ * (1 - x) + LL)) 2 ;

[0022] Wherein, Φ is the electricity consumption matrix of users in the substation area, x is the 0-1 state matrix of users, LL is the line loss of the substation area, and Q z is the total electricity consumption recorded by the main meter of the substation area.

[0023] The second aspect of this application provides a substation area abnormal file identification system, and the system includes:

[0024] An acquisition unit, configured to acquire the electricity consumption data of the substation area according to the measurement period, screen and sort the electricity consumption data, and obtain several groups of electricity consumption data groups, wherein the number of groups of the electricity consumption data groups is greater than the number of users in the substation area;

[0025] A first modeling unit, configured to establish a 0-1 integer programming mathematical model for the multi-user situation in the substation area according to the characteristics of abnormal household transformation relationships in the substation area; perform reasoning and analysis based on the total electricity consumption, current resistance, and work done in the substation area, and establish a line loss model for the substation area;

[0026] The second modeling unit is used to combine the planning mathematical model and the line loss model of the distribution area to perform data modeling on the power consumption situation of the distribution area, and obtain an abnormal household-transformer relationship identification model;

[0027] The identification unit is used to, based on the power consumption data group, obtain a line loss coefficient for each state matrix in the abnormal household-transformer relationship identification model, so as to obtain a following curve of the total power consumption of the distribution area, the power consumption of users, and the sum of line losses, and determine the users with abnormal household-transformer relationships in the distribution area by calculating the mean square error of the following curve.

[0028] Optionally, the abnormal household-transformer relationship identification model is:

[0029] Φ*(1 - x)+LL = Q z ;

[0030] In the formula, Φ is the power consumption matrix of the users in the distribution area, x is the 0-1 state matrix of the users, LL is the line loss of the distribution area, and Q z is the total power consumption recorded by the main meter of the distribution area.

[0031] Optionally, the line loss model of the distribution area is:

[0032] LL = (coe*ε0).*Φ*(1 - x);

[0033]

[0034] In the formula, ε0 is the line loss rate in the first measurement period, and coe is the coefficient of the line loss rate in each measurement period to the line loss rate in the first measurement period.

[0035] Optionally, the identification unit specifically includes:

[0036] The calculation unit is used to, based on the power consumption data group, obtain a line loss coefficient for each state matrix in the abnormal household-transformer relationship identification model, so as to obtain a following curve of the total power consumption of the distribution area, the power consumption of users, and the sum of line losses, and calculate the value of the mean square error of the following curve based on the mean square error calculation formula;

[0037] The identification unit is used to, by comparing the values of the mean square error, determine the first state matrix corresponding to the minimum value, and determine that the users with the state quantity of 1 in the first state matrix are the users with abnormal household-transformer relationships in the distribution area;

[0038] Among them, the mean square error calculation formula is:

[0039] mse = E(Q z -(Φ*(1 - x)+LL)) 2 ;

[0040] Wherein, Φ is the power consumption matrix of users in the substation area, x is the 0-1 status matrix of users, LL is the line loss of the substation area, and Q z is the total power consumption recorded by the main meter of the substation area.

[0041] The third aspect of the present application provides a device for identifying abnormal files in a substation area, and the device includes a processor and a memory:

[0042] The memory is used to store program codes and transmit the program codes to the processor;

[0043] The processor is used to execute the steps of the method for identifying abnormal files in the substation area as described in the first aspect above according to the instructions in the program codes.

[0044] The fourth aspect of the present application provides a computer-readable storage medium, and the computer-readable storage medium is used to store program codes, and the program codes are used to execute the method for identifying abnormal files in the substation area as described in the first aspect above.

[0045] It can be seen from the above technical solutions that the present application has the following advantages:

[0046] The present invention provides a method for identifying abnormal files in a substation area, including: obtaining the power consumption data of the substation area according to the measurement period, screening and sorting the power consumption data to obtain several groups of power consumption data groups, wherein the number of groups of power consumption data groups is greater than the number of users in the substation area; establishing a 0-1 integer programming mathematical model for multi-user situations in the substation area according to the characteristics of abnormal household-transformer relationships in the substation area; establishing a substation area line loss model through reasoning and analysis based on the total power consumption, current resistance, and work done in the substation area; combining the programming mathematical model and the substation area line loss model to perform data modeling on the power consumption situation in the substation area to obtain an abnormal household-transformer relationship identification model; based on the power consumption data groups, obtaining a line loss coefficient for each status matrix in the abnormal household-transformer relationship identification model, thereby obtaining a follow-up curve of the total power consumption in the substation area, the power consumption of users, and the sum of line losses, and determining the users with abnormal household-transformer relationships in the substation area by calculating the mean square error of the follow-up curve.

[0047] Compared with the prior art, the present application:

[0048] (1) Identifying abnormal household-transformer relationships based on the power consumption data in the substation area reduces the labor and material costs and improves the service level of the power grid company in the distribution network.

[0049] (2) By data analysis, narrowing the scope of abnormal users provides a direction for on-site verification by the power grid company and improves the efficiency of maintaining the file relationship in the substation area. Description of the Drawings

[0050] Figure 1 It is a schematic flowchart of an embodiment of a method for identifying abnormal files in a substation area provided in an embodiment of the present application;

[0051] Figure 2 This is a schematic structural diagram of an embodiment of a substation area abnormal file recognition system provided in an embodiment of the present application. Specific implementation manners

[0052] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0053] Please refer to Figure 1 , a method for identifying an abnormal file in a substation area provided in an embodiment of the present application includes:

[0054] Step 101: Obtain the power consumption data of the substation area according to the metering period, screen and sort the power consumption data to obtain several groups of power consumption data groups, where the number of groups of power consumption data groups is greater than the number of users in the substation area;

[0055] It should be noted that the data processing link is the premise for identifying household-transformer anomalies. First, the recent power consumption data of the substation area needs to be obtained from the metering automation system, and the number of metering periods should be more than the number of users in the substation area. After that, the collected data needs to be screened, and the abnormal jump data and missing data groups of the electricity meters should be screened out, and it is necessary to ensure that the number of finally retained data groups is greater than the number of users in the substation area. If it is insufficient, new data needs to be obtained to carry out the operation.

[0056] Step 102: According to the characteristics of abnormal household-transformer relationships in the substation area, establish a 0-1 integer programming mathematical model for the multi-user situation in the substation area; perform reasoning and analysis based on the total power consumption, current resistance, and work done in the substation area to establish a line loss model for the substation area;

[0057] It should be noted that the model construction includes two parts, namely model presetting and model building:

[0058] I. Model presetting:

[0059] For the method for identifying an abnormal file in the substation area of this embodiment, it is necessary to model the error of the intelligent electricity meter in the substation area. The prerequisite conditions for establishing this model are:

[0060] (1) The error of the electricity meters in the substation area is small, and the influence on the line loss of the substation area is less than 2%;

[0061] (2) The abnormal household-transformer relationship in the substation area means that users outside the substation area are misrecorded in this substation area, resulting in too small statistical line loss or even negative line loss;

[0062] (3) The total power consumption recorded by the substation master meter is an accurate value, that is, the error of the substation master meter can be ignored.

[0063] II. Model construction:

[0064] 1. Integer programming mathematical model:

[0065] According to the characteristics of abnormal user-transformer relationships in the substation area, a 0-1 integer programming mathematical model is established for the "multiple users" situation in the substation area:

[0066] Let the variable where i is the user number and n is the total number of users in the substation area.

[0067] If the user electricity consumption in the substation area includes that of users not in this substation area, the measurement error of this user in the actual mathematical relationship should be 100%, and the error of normal users is 0. Using this principle, the substation area model can be obtained:

[0068] Φ*(1 - x)+LL = Q z

[0069] where Φ is the electricity consumption matrix of the substation area users, x is the 0-1 status matrix of the users, LL is the line loss of this substation area, and Q z is the total power consumption recorded by the substation master meter.

[0070] In this model, the user electricity consumption and the total power consumption are known quantities. The status matrix is solved by the enumeration method and can also be regarded as a known quantity. Therefore, only the line loss is the unknown quantity in the model. By solving the line loss through linear regression, all the information of the substation area can be obtained. At this time, the mean square error of the regression can show the regression situation of the model, thus reflecting the load degree of this status matrix and the actual situation of the substation area. Finally, the root mean square error of the regression model is used to identify abnormal users.

[0071] 2. Substation area line loss model:

[0072] The line loss in the substation area is mainly caused by the heat generated by the line resistance. According to the calculation of current resistance and work done, the circuit loss should be related to the total power consumption of the circuit. The specific analysis is as follows:

[0073] Within a measurement period t, the total power consumption of the substation area: W = UIt, where U is the voltage and I is the current.

[0074] Then the ratio of the line losses in two measurement periods t1 and t2 is:

[0075]

[0076] In the above formula, LL is the line loss, I is the total current of the substation area, R is the total line resistance, and t is the measurement period. Among them, the durations of the two measurement periods are equal, that is, t1 = t2, then:

[0077]

[0078]

[0079] That is, the line loss rate is proportional to the power supply quantity. Based on this, the line loss model is modified to:

[0080] LL = (coe * ε0).* Φ * (1 - x)

[0081]

[0082] Among them, ε0 is the line loss rate in the first measurement period, and coe is the coefficient of the line loss rate in each measurement period to the line loss rate in the first measurement period.

[0083] Step 103: Combine the planning mathematical model and the line loss model of the distribution transformer area to perform data modeling on the power consumption situation of the distribution transformer area, and obtain an abnormal household-transformer relationship recognition model;

[0084] It should be noted that the model construction combines the 0-1 programming model in Step 102 and the line loss model of the distribution transformer area to perform mathematical modeling on the power consumption situation of the distribution transformer area, and combines the line loss range of the normal distribution transformer area to limit the statistical line loss range of the distribution transformer area within 10%.

[0085] Φ * (1 - x) + LL = Q z

[0086]

[0087] The above formula is the 0-1 programming model for identifying abnormal household-transformer relationships in the distribution transformer area and the corresponding x state matrix constraint conditions.

[0088] Step 104: Based on the power consumption data group, calculate a line loss coefficient according to each state matrix in the abnormal household-transformer relationship recognition model, so as to obtain the following curve of the total power consumption of the distribution transformer area following the user power consumption and the line loss sum, and determine the users with abnormal household-transformer relationships in the distribution transformer area by calculating the mean square error of the following curve.

[0089] It should be noted that based on the power consumption data group, according to the abnormal household-transformer relationship recognition model, a line loss coefficient can be calculated according to each state matrix, and the following curve of the total power consumption of the distribution transformer area following the user power consumption and the line loss sum can be obtained. By calculating the mean square error of the following curve, the model regression situation under this state matrix can be obtained:

[0090] mse = E(Q z - (Φ * (1 - x) + LL)) 2

[0091] By comparing the values of the mean squared error (MSE), find the x state matrix corresponding to the minimum value. Then, the users with a state quantity of 1 in this matrix are the users with abnormal household-transformer relationships in the identified substation area.

[0092] An abnormal substation area file identification method provided by an embodiment of the present application is used to identify abnormal household-transformer relationships based on substation area power consumption data. By combining the substation area energy conservation model and the line loss model, an abnormal household-transformer relationship identification model is established. Further, by combining the 0-1 integer programming idea, the status of the substation area user files is analyzed, and finally, the users with abnormal household-transformer relationships are determined through the regression of the model corresponding to the file status.

[0093] The above is an abnormal substation area file identification method provided by an embodiment of the present application. The following is an abnormal substation area file identification system provided by an embodiment of the present application.

[0094] Please refer to Figure 2 , an abnormal substation area file identification system provided by an embodiment of the present application includes:

[0095] An acquisition unit 201, configured to acquire the power consumption data of the substation area according to the metering period, screen and sort the power consumption data to obtain several groups of power consumption data groups, where the number of power consumption data groups is greater than the number of users in the substation area;

[0096] A first modeling unit 202, configured to establish a 0-1 integer programming mathematical model for the multi-user situation in the substation area according to the characteristics of abnormal household-transformer relationships in the substation area; perform reasoning and analysis based on the total power consumption, current resistance, and work done in the substation area to establish a substation area line loss model;

[0097] A second modeling unit 203, configured to perform data modeling on the power consumption situation of the substation area by combining the programming mathematical model and the substation area line loss model to obtain an abnormal household-transformer relationship identification model;

[0098] An identification unit 204, configured to, based on the power consumption data groups, obtain a line loss coefficient for each state matrix in the abnormal household-transformer relationship identification model, thereby obtaining a follow-up curve of the total power consumption of the substation area, the power consumption of users, and the sum of line losses, and determining the users with abnormal household-transformer relationships in the substation area by calculating the mean squared error of the follow-up curve.

[0099] Furthermore, an abnormal substation area file identification device is provided in an embodiment of the present application. The device includes a processor and a memory:

[0100] The memory is used to store program code and transmit the program code to the processor;

[0101] The processor is configured to execute the steps of the abnormal substation area file identification method described in the above method embodiment according to the instructions in the program code.

[0102] Furthermore, in the embodiments of the present application, a computer-readable storage medium is also provided. The computer-readable storage medium is used to store program codes, and the program codes are used to execute the method described in the embodiment of the above-mentioned method for identifying abnormal files in the substation area.

[0103] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems and units can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0104] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0105] It should be understood that in the present application, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships can exist. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist at the same time. Among them, A and B can be singular or plural. The character " / " generally means that the associated objects before and after are in an "or" relationship. "At least one (one) of the following" or a similar expression means any combination of these items, including any combination of single item (one) or plural items (ones). For example, at least one (one) of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0106] In several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms.

[0107] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0108] In addition, in each embodiment of the present application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0109] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (English full name: Read-Only Memory, English abbreviation: ROM), random access memories (English full name: Random Access Memory, English abbreviation: RAM), magnetic disks, or optical discs that can store program codes.

[0110] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A method for identifying abnormal files in a substation area, characterized in that, Including: Obtain the power consumption data of the transformer substation area according to the measurement period, screen and sort the power consumption data to obtain several groups of power consumption data groups, where the number of groups of the power consumption data groups is greater than the number of users in the transformer substation area; According to the characteristics of abnormal household-transformer relationship in the transformer substation area, establish a 0-1 integer programming mathematical model for the multi-user situation in the transformer substation area; conduct reasoning and analysis based on the total power consumption, current resistance and work done in the transformer substation area, and establish a line loss model for the transformer substation area; Combine the programming mathematical model and the line loss model of the transformer substation area to perform data modeling on the power consumption situation of the transformer substation area to obtain an abnormal household-transformer relationship identification model; Based on the power consumption data groups, obtain a line loss coefficient according to each state matrix in the abnormal household-transformer relationship identification model, so as to obtain a follow-up curve of the total power consumption of the transformer substation area, the power consumption of users and the sum of line losses. By calculating the value of the mean square error mse of the follow-up curve, determine the state matrix corresponding to the minimum value of the mean square error mse, and determine the users with a state quantity of 1 in the state matrix as the users with abnormal household-transformer relationships in the transformer substation area; Wherein, the abnormal household-transformer relationship identification model is: ； Wherein, is the power consumption matrix of the users in the substation area, is the 0-1 state matrix of the users, is the line loss of the substation area, is the total power consumption recorded by the main meter of the substation area; The line loss model of the transformer substation area is: ； ； In the formula, is the line loss rate in the first measurement period, and coe is the coefficient of the line loss rate in each measurement period to the line loss rate in the first measurement period.

2. The method for identifying the abnormal file of the substation area according to claim 1, wherein Determine the users with abnormal household-transformer relationships in the transformer substation area by calculating the mean square error of the follow-up curve, specifically including: Based on the mean square error calculation formula, calculate the value of the mean square error of the follow-up curve; By comparing the values of the mean square error, determine the first state matrix corresponding to the minimum value, and determine the users with a state quantity of 1 in the first state matrix as the users with abnormal household-transformer relationships in the transformer substation area; Wherein, the mean square error calculation formula is: ； Wherein, is the electricity consumption matrix of the users in the substation area, is the 0-1 state matrix of the users, is the line loss of the substation area, is the total electricity consumption recorded by the main meter of the substation area.

3. A substation area abnormal file identification system, characterized in that, Including: An acquisition unit for obtaining the power consumption data of the transformer substation area according to the measurement period, screening and sorting the power consumption data to obtain several groups of power consumption data groups, where the number of groups of the power consumption data groups is greater than the number of users in the transformer substation area; A first modeling unit for establishing a 0-1 integer programming mathematical model for the multi-user situation in the transformer substation area according to the characteristics of abnormal household-transformer relationship in the transformer substation area; conducting reasoning and analysis based on the total power consumption, current resistance and work done in the transformer substation area, and establishing a line loss model for the transformer substation area; A second modeling unit for combining the programming mathematical model and the line loss model of the transformer substation area to perform data modeling on the power consumption situation of the transformer substation area to obtain an abnormal household-transformer relationship identification model; An identification unit for obtaining a line loss coefficient according to each state matrix in the abnormal household-transformer relationship identification model based on the power consumption data groups, so as to obtain a follow-up curve of the total power consumption of the transformer substation area, the power consumption of users and the sum of line losses. By calculating the value of the mean square error mse of the follow-up curve, determine the state matrix corresponding to the minimum value of the mean square error mse, and determine the users with a state quantity of 1 in the state matrix as the users with abnormal household-transformer relationships in the transformer substation area; Wherein, the abnormal household-transformer relationship identification model is: ； Wherein, is the power consumption matrix of the users in the substation area, is the 0-1 status matrix of the users, is the line loss of the substation area, is the total power consumption recorded by the main meter of the substation area; The line loss model of the transformer substation area is: ； ； In the formula, is the line loss rate in the first measurement period, and coe is the coefficient of the line loss rate in each measurement period to the line loss rate in the first measurement period.

4. The abnormal file recognition system for the power distribution area according to claim 3, wherein The identification unit specifically includes: A calculation unit for obtaining a line loss coefficient according to each state matrix in the abnormal household-transformer relationship identification model based on the power consumption data groups, so as to obtain a follow-up curve of the total power consumption of the transformer substation area, the power consumption of users and the sum of line losses, and calculating the value of the mean square error of the follow-up curve based on the mean square error calculation formula; An identification unit, configured to determine a first state matrix corresponding to the minimum value by comparing the values of the mean square error, and determine that a user with a state quantity of 1 in the first state matrix is an abnormal household-transformer relationship user in the substation area; Wherein, the calculation formula of the mean square error is: ； wherein, is the electricity consumption matrix of the users in the substation area, is the 0-1 state matrix of the users, is the line loss of the substation area, is the total electricity consumption recorded by the main meter of the substation area.

5. A device for identifying abnormal files in a substation area, characterized in that, The device includes a processor and a memory: The memory is used to store program codes and transmit the program codes to the processor; The processor is configured to execute the substation area abnormal file identification method according to any one of claims 1-2 based on the instructions in the program code.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program codes, and the program codes are used to execute the substation area abnormal file identification method according to any one of claims 1-2.

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