Zone area electricity meter metering error estimation method and system

Abstract

The invention, which relates to the technical field of the power system, discloses a transformer area ammeter metering error estimation method and system, comprising the following steps: collecting power utilization information data of transformer area users, and carrying out data preprocessing; establishing an energy conservation law of the intelligent electric meter; a data matching problem of new energy access is considered; constructing an intelligent electric meter error estimation model according to an energy conservation principle; a biased estimation method based on Neumann series is adopted to solve the constructed model; and calculating an ammeter error value by adopting a window sliding mechanism. The method provided by the invention solves the problems that the line loss volatility and randomness of the transformer area are large, and the transformer area is easily affected by the ill-conditioned condition of a data matrix under the condition that the new energy is connected into the intelligent electric meter, guarantees the accuracy of error estimation, improves the operation efficiency of the electric meter, and is practical.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power systems, and in particular to a method and system for estimating measurement errors of electric meters in an area. Background Art

[0002] Smart meters are the basic facilities of smart grids, power Internet of Things, smart cities, etc. At present, there are more than 650 million smart meters in operation in my country. The meters are responsible for the measurement of nearly 8 trillion kWh of electricity consumption. Such a huge number of meters and huge electricity consumption statistics require a standard precision measurement level. my country also requires that the measurement error of user smart meters is within ±2%. Therefore, the guarantee of meter measurement accuracy has become a key issue that needs further research. One type of existing technology is studied through the principle of generalized energy conservation, using the total meter measurement value of the substation to equal the power consumption of each user meter under its jurisdiction and the sum of the network loss under the substation and the inherent loss of the smart meter for calculation. However, due to the access of new energy, the volatility and randomness of the substation loss are large, and the accuracy of error estimation cannot be guaranteed. Another type of method mainly uses recursive algorithms, least squares methods, etc. to solve the error of low-voltage substation smart meters, but its calculation efficiency is low and it is easily affected by the pathological data matrix and cannot accurately calculate the error.

[0003] Taking the above problems into consideration, the present invention proposes an estimation of metering errors of electric meters in substations considering the impact of distributed renewable energy access. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: Generalized Energy Conservation Principle: Due to the access of new energy, the volatility and randomness of its area losses are large, and the accuracy of error estimation cannot be guaranteed, and its method has limitations;

[0006] The method of solving the error of smart meters in low-voltage areas using recursive algorithms, least squares methods, etc.: its calculation efficiency is low and it is easily affected by the pathological nature of the data matrix and cannot accurately calculate the error.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for estimating measurement errors of electric meters in a substation area, comprising:

[0008] Collect electricity consumption information data of users in the substation area and perform data preprocessing;

[0009] Establish the energy conservation law for smart meters;

[0010] Consider the data matching problem of new energy access;

[0011] Construct a smart meter error estimation model based on the energy conservation principle;

[0012] The constructed model is solved using a biased estimation method based on Neumann series;

[0013] A window sliding mechanism is used to calculate the meter error value.

[0014] As a preferred scheme of the method for estimating the metering error of the substation electric meter described in the present invention, wherein: the energy conservation law of the smart meter is established, including the energy conservation law based on that the energy flowing into the substation should be equal to the energy flowing out, the total meter power should be the sum of the power consumption of each branch, and a mathematical model is constructed taking into account the metering errors, low-voltage line losses and inherent losses of the meter itself in the actual distribution network.

[0015] As a preferred solution of the method for estimating the measurement error of electric meter in the substation area described in the present invention, the mathematical model is expressed as follows:

[0016]

[0017] Among them, z(n) is the metering value of the total meter supply in the area during metering period i, σ0(n) is the metering error of the total meter, and f m (n) is the measurement value of table m in measurement period n, δ m (n) is the estimated error of sub-meter m, δ0(n) is the inherent loss of the smart meter, and W(n) is the actual line loss in the substation during metering period n.

[0018] As a preferred solution of the method for estimating the measurement error of electric meter in the substation area described in the present invention, wherein: the data matching problem considering the access of new energy sources includes verifying whether the real line loss data of the substation area satisfies the normal distribution;

[0019] The Box-Cox transformation was used to transform the nonlinear data into linear output.

[0020] As a preferred solution of the method for estimating the measurement error of electric meter in the substation area described in the present invention, the Box-Cox transformation is expressed as:

[0021]

[0022] Among them, y(W(n)) is the new variable obtained after Box-Cox transformation, y is the original continuous dependent variable, and W(n) is the transformation parameter.

[0023] As a preferred solution of the method for estimating the measurement error of electric meter in the substation area described in the present invention, the error estimation model of the smart electric meter is expressed as:

[0024]

[0025] As a preferred solution of the method for estimating the measurement error of electric meter in the substation area described in the present invention, the biased estimation method based on Neumann series is expressed as:

[0026]

[0027] Another object of the present invention is to provide a substation electric meter measurement error estimation system, which can solve the loss volatility and randomness, low computing efficiency and data matrix pathology problems caused by new energy access in existing smart meter measurement by constructing a substation electric meter measurement error estimation system.

[0028] In order to solve the above technical problems, the present invention provides the following technical solutions: a metering error estimation system for an area electric meter, comprising: a data acquisition module, an energy model module, a data matching module, an error model module, a model solving module and an error calculation module; the data acquisition module is used to collect the electricity consumption information data of the area users and perform data preprocessing; the energy model module is used to establish the energy conservation law of the smart meter; the data matching module is used to consider the data matching problem of new energy access; the error model module is used to construct an error estimation model of the smart meter according to the principle of energy conservation; the model solving module is used to solve the constructed model by using a biased estimation method based on the Neumann series; the error calculation module is used to calculate the meter error value by using a window sliding mechanism.

[0029] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned method for estimating the measurement error of an electric meter in an area are implemented.

[0030] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for estimating the measurement error of an electric meter in an area as described above.

[0031] Beneficial effects of the present invention: The present invention provides a method for estimating the metering error of an electric meter in an area. First, the daily electricity consumption data of the total meter in the area and its subordinate sub-meters are collected in the metering automation system, and the data is preprocessed; secondly, according to the energy conservation principle that the energy flowing into the area is equal to the electricity consumption of all the sub-meters in the area and the network loss, in order to satisfy its equation for data verification, the Box-Cox transformation is used to convert the nonlinear data into a linear output; then, based on its established equation, an intelligent meter error estimation model is constructed and the constructed model is solved by a biased estimation method based on the Neumann series to obtain a stable and accurate error estimation result; the method of the present invention solves the problems of large volatility and randomness of line loss in the area and susceptibility to the pathological influence of the data matrix when the smart meter is connected to new energy, thereby ensuring the accuracy of error estimation, improving the operating efficiency of the meter, and being practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0033] Figure 1 The present invention provides an overall flow chart of a method for estimating measurement errors of electric meters in an area according to an embodiment of the present invention.

[0034] Figure 2 A module operation diagram of a method for estimating metering errors of an area electric meter provided by an embodiment of the present invention.

[0035] Figure 3 A normal test result diagram of a method for estimating the measurement error of an electric meter in an area provided by an embodiment of the present invention.

[0036] Figure 4 A diagram of an input data window selection mechanism for a method for estimating errors of electric meter measurements in an area provided by an embodiment of the present invention.

[0037] Figure 5 The overall structure diagram of a system for estimating errors of electric meter measurements in an area provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0039] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0040] Example 1

[0041] Reference Figure 1-Figure 4 , is an embodiment of the present invention, and provides a method for estimating measurement errors of electric meters in a substation area, comprising:

[0042] S1: Collect electricity consumption information data of users in the substation area and perform data preprocessing

[0043] The smart meter layer collects and stores the user's electricity consumption information and responds to the concentrator's instructions. For a substation, it contains a high-precision master meter with negligible operating errors and several sub-meters. The data collected and organized by the smart meter are shown in Table 1.

[0044] Table 1 Example of collected data (unit: kWh)

[0045]

[0046]

[0047] The collected data needs to be preprocessed. For example, if the collected values ​​of sub-table 1 in Table 1 on December 29, 2023 are missing, all the data of the main table and sub-table in Table 1 on that date should be deleted. For example, if there are abnormal data in sub-table 100 on January 3, 2023, the data of the main table and sub-table on that day should also be deleted.

[0048] S2: Establishing the energy conservation law for smart meters

[0049] Based on the law of conservation of energy flowing into the substation, the total meter power consumption should be the sum of the power consumption of each branch. Considering the meter measurement error, low-voltage line loss and inherent loss of the meter itself in the actual distribution network, the constructed mathematical model is as follows:

[0050]

[0051] In the formula, z(n) is the total meter supply value of the area in metering period i, in kWh, σ0(n) is the metering error of the total meter, and f m (n) is the metering value of meter m in metering period n, in kWh, δ m (n) is the estimated error of sub-meter m, δ0(n) is the inherent loss of the smart meter, in kWh, W(n) is the actual line loss of the substation during metering period n, in kWh, which is related to factors such as the substation topology and parameters, and the power consumption of sub-meter users;

[0052] S3: Consider the data matching problem of new energy access;

[0053] Based on the law of conservation of energy in S2, in order to satisfy its linear regression equation, W(n) must conform to the normal distribution, that is, the actual line loss in the substation area. However, due to the large volatility and randomness of its waveform caused by the access of new energy, W(n) does not necessarily satisfy the normal distribution. The solution to this problem is as follows:

[0054] S3.1: Verify whether the actual line loss data of the substation area satisfies the normal distribution

[0055] Use statistical test method to test the normality of W(n). Statistical test method is the most rigorous method to test normality and has the most stringent requirements on data. Statistical test method includes three test methods, namely Shapiro-Wilk test, Kolmogorov-Smirnov test, and Jarque-Bera test. Since the sample size is greater than 50, KS test (Kolmogorov-Smirnov test) is used. Use SPSSAU to perform statistical test of normal distribution. In the general method module, select normality test, drag the data to the analysis box on the right, and click "Start Analysis". The operation is as follows: Figure 2 and Figure 3 shown.

[0056] Since the collected meter data is 100, which is greater than 50, the KS test is used. Specifically, all W(n) are not significant (p>0.05), so it basically has normal characteristics and satisfies the normal distribution.

[0057] S3.2: Convert nonlinear data into linear output using Box-Cox transformation

[0058] Considering that there may be nonlinear data that does not conform to the normal distribution, the Box-Cox transformation is used to process the nonlinear data. Its general form is:

[0059]

[0060] Where y(W(n)) is the new variable obtained after Box-Cox transformation, y is the original continuous dependent variable, W(n) is the transformation parameter, that is, the real line loss data of the above-mentioned substation. The above transformation requires that the original variable y is positive. If the value is negative, a constant a can be added to all the original data to make (y+a) a positive value, and then the above transformation can be performed. Different transformations are made for different W(n). When W(n) approaches 0, the transformation is a logarithmic transformation, when W(n) approaches -1, it is a reciprocal transformation, and when W(n) approaches 1 / 2, it is a square root transformation.

[0061] According to the above transformation rules, the nonlinear data in W(n), i.e. the real line loss data of the above-mentioned substation area, can be converted into linear data output.

[0062] S4: Construct a smart meter error estimation model based on the energy conservation principle;

[0063] After processing W(n) in S3, the law of conservation of energy is basically established. To further construct the meter error model, the following assumptions are made: 1) The total meter in the substation has high accuracy and is assumed to be error-free, that is, σ0(n) = 0; 2) The inherent loss of the substation meter remains unchanged over time, that is, δ0(n) = δ0; 3) The metering error of the submeter is relatively stable and remains unchanged within a period of time, that is, δ m (n) = δ m , 4) Assume that the daily line loss rate is proportional to the total power supply of the substation area, that is, W(n) = δ z ×z(n), to replace the estimated line loss, where δ z It is expressed as the calculated line loss rate of the substation area, and is the variable to be determined. Therefore, formula (1) becomes:

[0064]

[0065] In formula (3), the data f of each user at each collection time of the metering automation system can be m (n) and the corresponding area total table data z(n), solve the unknown quantity δ m , δ0 and δ z , thus obtaining the estimation error of each smart meter in the area, that is, δ m (m=1,2,3,…,M);

[0066] After the metering automation collection system collects N days of electricity consumption data, formula (3) is transformed into the following form:

[0067]

[0068] In formula (4), This value is the metering line loss, f m (n) is the metering value of meter m in the metering period n, in kWh, z(n) is the metering value of the total meter in the metering period i, in kWh, δ0 is the inherent loss of the meter in the metering area, δ n (n=1,2,…,M) is the measurement error of the sub-meter, δ z is the daily line loss rate, which is different from the actual line loss of the network in the substation area. Furthermore, formula (3) can also be rewritten as Ax=b matrix form, where:

[0069]

[0070] The elements in the matrix A can all be represented by the total meter and sub-meter electricity consumption data collected by the metering and acquisition system, so it is defined as the electricity consumption matrix. b is calculated from the metering line loss value and the total metering electricity value, which is called the line loss coefficient vector. x is to be solved, that is, the value that the model needs to calculate, which is called the vector to be solved. The above transformation makes the elements of the matrix A and the element values ​​of the coefficient vector b in the error estimation mathematical model presented by formula (4) excluding the unknown variables relatively smaller than those of the existing method, so that a more stable solution can be obtained when solving the model.

[0071] S5: Use the biased estimation method based on Neumann series to solve the constructed model

[0072] Rewrite the formula (3) of the form Ax=b into the form of formula (4):

[0073] x=B -1 z (5)

[0074] In the formula, B -1 is the inverse matrix of matrix B, matrix B is the product of the transpose of the power matrix and the power matrix, z is the product of the transpose of the power matrix and the line loss coefficient vector, that is, B = A T A, z = A T b;

[0075] Then transform equation (4) into the following form:

[0076] x=B -1 z=(B+EE) -1 z=((B+E)-E) -1 z (6)

[0077] In the formula, matrix E is the perturbation matrix, which requires that the condition number of matrix B+E is less than the condition number of matrix B, that is:

[0078] cond(B+E)<cond(B) (7)

[0079] Where cond(·) is the condition number;

[0080] By performing Neumann series expansion on equation (6), we can obtain:

[0081] x=(B+E) -1 [I+E(B+E) -1 +E(B+E) -1 E(B+E) -1 +...]z (8)

[0082] Where, matrix I is the identity matrix of the same dimension as matrix B;

[0083] Taking the first three terms of equation (8), we obtain a biased estimate based on the Neumann series:

[0084]

[0085] In the formula, is a biased estimate of the vector to be found.

[0086] S6: Calculate the meter error value using a window sliding mechanism

[0087] like Figure 3 As shown in the figure, the number of user sub-meters in the substation area is M+2+γ, the number of unknown quantities is (P+2), and the amount of daily power consumption data collected in the substation area is N>>M. Therefore, selecting (M+2+γ) pieces of data can form the estimation model of formula (4), and the primary estimation value can be solved by formula (9), γ≥0. Figure 4 The mode selects and calculates data in multiple windows to obtain multiple calculation results, where the step length of sliding down is l step ;

[0088] Then, when multiple windows are slid and different data is input, multiple calculation results X can be obtained for each meter. jm (the error value calculated by the mth smart meter for the jth time), write the error calculation values ​​calculated by all smart meters in the area each time into a collection form:

[0089]

[0090] Where X is the error value set calculated for each meter under different data input conditions, X jm is the error value calculated for the jth time by the mth smart meter;

[0091] And take the average of each calculation as the error estimate for the table:

[0092]

[0093] In the formula, δ m is the average error estimate of the mth smart meter, and the J value is given by Figure 1 The sliding window calculates and outputs the estimated error value.

[0094] Currently, smart meters need to be replaced after about 8 years of operation. When calibrating the replaced electricity meters, it was found that most of the smart meters are still normal and can continue to be used. Therefore, replacing smart meters according to regulations will lead to a large waste of resources and low efficiency. It is not possible to rely solely on regular replacement, and a status replacement method must be carried out simultaneously. However, what data should be used to reasonably and accurately evaluate the status of smart meters has become a key issue.

[0095] At present, the research method of metering error uses the generalized law of conservation of energy to solve the error of smart meters. However, the solution method of decomposing the reading matrix into upper and lower triangular matrices is cumbersome and prone to pathological problems. On this basis, a smart meter error estimation method based on a parameter degradation model is proposed. Although the comprehensive influence of various error factors such as temperature, humidity and load under actual working conditions is taken into account, this method is only for areas without photovoltaic power generation. However, when new energy is connected, the fluctuation and randomness caused by the solution of metering errors in the areas are large, that is, the metering error of the meter cannot be accurately calculated.

[0096] Based on the above problems, the present invention provides a method for estimating the metering error of substation electric meters considering the impact of distributed new energy access. The method mainly uses Box-Cox transformation to convert nonlinear data into linear output, which improves the problems of large volatility and randomness of substation line losses when smart meters are connected to new energy, so that the proposed method can be closed-loop and reliable; the constructed model is solved by a biased estimation method based on the Neumann series to obtain stable and accurate error estimation results, thereby ensuring the accuracy of the error estimation, providing an evaluation reference for related studies such as electric energy meter status evaluation and electric energy meter intelligent rotation strategy, further improving the operating efficiency of electric energy meters, and having practicality.

[0097] Example 2

[0098] Reference Figure 5 , is an embodiment of the present invention, and provides a system for estimating metering errors of a substation, including:

[0099] Data acquisition module 100, energy model module 200, data matching module 300, error model module 400, model solving module 500 and error calculation module 600;

[0100] The data collection module 100 is used to collect the electricity consumption information data of users in the substation area and perform data preprocessing;

[0101] The energy model module 200 is used to establish the energy conservation law of the smart meter;

[0102] The data matching module 300 is used to consider the data matching problem of new energy access;

[0103] The error model module 400 is used to construct a smart meter error estimation model according to the energy conservation principle;

[0104] The model solving module 500 is used to solve the constructed model using a biased estimation method based on Neumann series;

[0105] The error calculation module 600 is used to calculate the error value of the electric meter by using a window sliding mechanism.

[0106] Example 3

[0107] An embodiment of the present invention is different from the first two embodiments in that:

[0108] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0109] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0110] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0111] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0112] Example 4

[0113] Reference Figure 2-Figure 5 , which is an embodiment of the present invention, provides a method for estimating the measurement error of electric meters in an area. In order to verify the beneficial effects of the present invention, actual distribution network area data is used to verify the proposed strategy.

[0114] In the selected area 1, this example evaluates the calculation results and algorithm effects of different algorithms and different regularization parameters when the same data is input. Area 1 has 247 user meters. It is known that the meters with inaccurate field calibration in this area are numbered *****392 and *****330. The maximum eigenvalue and minimum eigenvalue of the coefficient matrix of this area are greatly different. The condition number of the matrix k(A)=215878, which is much larger than 1000. The system model of area 1 is seriously ill-conditioned. All the collected data are used as input, and then ordinary least squares (OLS), ordinary least squares (OLS) and Box-Cox transformation, Neumann biased estimation method, Neumann biased estimation method and Box-Cox transformation are used respectively. Different regularization parameters are manually set for solution, and the four smart meters with the largest absolute value of calculation error are selected. The results are shown in Table 2.

[0115] Table 2 Estimation results for area 1

[0116]

[0117] Table Notes: ***** in the table is desensitized information.

[0118] It can be seen from Table 2 that the screening results of the ordinary least squares method do not include inaccurate meters. After the Box-Cox transformation, one inaccurate meter is detected. When the Neumann biased estimation method is used, both inaccurate meters are detected. After adding the Box-Cox transformation, the estimation error is minimized.

[0119] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for estimating measurement error of electric meters in an area, characterized in that: include: Collect electricity consumption information data of users in the substation area and perform data preprocessing; Establish the energy conservation law for smart meters; Consider the data matching problem of new energy access; Construct a smart meter error estimation model based on the energy conservation principle; The constructed model is solved using a biased estimation method based on Neumann series; A window sliding mechanism is used to calculate the meter error value.

2. The method for estimating the measurement error of an electric meter in an area according to claim 1, characterized in that: The energy conservation law for establishing the smart meter includes the energy conservation law based on that the energy flowing into the substation should be equal to the energy flowing out, the total meter power should be the sum of the power consumption of each branch, and a mathematical model is constructed taking into account the meter measurement errors, low-voltage line losses and inherent losses of the meter itself in the actual distribution network.

3. The method for estimating the measurement error of an electric meter in an area according to claim 2, characterized in that: The mathematical model is expressed as, Among them, z(n) is the metering value of the total meter supply in the area during metering period i, σ0(n) is the metering error of the total meter, and f m (n) is the measurement value of table m in measurement period n, δ m (n) is the estimated error of sub-meter m, δ0(n) is the inherent loss of the smart meter, and W(n) is the actual line loss in the substation during metering period n.

4. The method for estimating the measurement error of an electric meter in an area according to claim 3, characterized in that: The data matching problem considering the access of new energy sources includes verifying whether the real line loss data of the substation area satisfies the normal distribution; The Box-Cox transformation was used to transform the nonlinear data into linear output.

5. The method for estimating the measurement error of an electric meter in an area according to claim 4, characterized in that: The Box-Cox transformation is expressed as, Among them, y(W(n)) is the new variable obtained after Box-Cox transformation, y is the original continuous dependent variable, and W(n) is the transformation parameter.

6. The method for estimating the measurement error of an electric meter in an area according to claim 5, characterized in that: The smart meter error estimation model is expressed as:

7. The method for estimating the measurement error of an electric meter in an area according to claim 6, characterized in that: The biased estimation method based on Neumann series is expressed as:

8. A system using the method for estimating the measurement error of an electric meter in an area according to any one of claims 1 to 7, characterized in that: include: A data acquisition module (100), an energy model module (200), a data matching module (300), an error model module (400), a model solving module (500) and an error calculation module (600); The data collection module (100) is used to collect electricity consumption information data of users in the substation area and perform data preprocessing; The energy model module (200) is used to establish the energy conservation law of the smart meter; The data matching module (300) is used to consider the data matching problem of new energy access; The error model module (400) is used to construct a smart meter error estimation model according to the energy conservation principle; The model solving module (500) is used to solve the constructed model using a biased estimation method based on Neumann series; The error calculation module (600) is used to calculate the error value of the electric meter by adopting a window sliding mechanism.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for estimating the measurement error of an electric meter in an area according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for estimating the measurement error of an electric meter in an area according to any one of claims 1 to 7 are implemented.

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