Total meter error online monitoring method and system based on user electric meter data, equipment and medium

Abstract

The invention discloses a user electric meter data-based general meter error online monitoring method and system, equipment and a medium, and the method comprises the steps: firstly collecting the electric quantity data of a user electric meter and a general meter in a plurality of time periods, and then constructing a general meter error metering model based on the law of conservation of energy; the relation between the total meter error and the user electric meter error, the transformer area line loss, the user electric meter electric quantity reading in the same time period and the total meter electric quantity reading is constructed, then the measurement error of each user electric meter and the transformer area line loss are obtained through optimization solving by means of a differential evolution algorithm, and therefore the total meter error in each time period can be obtained through calculation. According to the invention, online real-time monitoring of the total meter error can be realized, so that the total meter error can be calibrated in real time, the real-time accuracy of electric energy metering is improved, and the accuracy of electric energy transaction settlement is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of total meter error online monitoring, and in particular to a total meter error online monitoring method and system based on user meter data, electronic equipment, and a computer-readable storage medium. Background Art

[0002] In a typical distribution area of ​​a power system, there is usually a master meter and multiple user meters. The master meter is responsible for recording the total power consumption of the entire area, and the user meter is responsible for recording the power consumption of each user. As the main equipment for measuring power between the power grid and the user, the accuracy of the master meter directly affects the settlement of power transactions between the power grid and the user. Due to the high accuracy of the master meter, its readings are usually assumed to be accurate. Therefore, the current power system mainly calibrates the errors of user meters on a regular basis. However, in fact, the master meter may also have errors, and the master meter errors also need to be calibrated. The traditional method of calibrating the error of the energy meter usually uses manual regular calibration and offline detection, which leads to time lag and heavy workload, and it is impossible to monitor the actual error of the master meter in real time. Summary of the invention

[0003] The present invention provides a method and system for online monitoring of total meter errors based on user electric meter data, an electronic device, and a computer-readable storage medium, which can realize online real-time monitoring of total meter errors, so as to facilitate real-time calibration of total meter errors, improve the real-time accuracy of electric energy metering, and thus ensure the accuracy of electric energy transaction settlement.

[0004] According to one aspect of the present invention, a total meter error online monitoring method based on user meter data is provided, comprising the following contents:

[0005] Collect the electricity data of user meters and total meters in multiple time periods of a sampling cycle respectively;

[0006] Construct a total table error calculation model based on the law of conservation of energy;

[0007] The differential evolution algorithm is used to optimize and solve the electricity data of user meters and master meters in multiple time periods to obtain the measurement error of each user meter and the line loss in the substation area.

[0008] The measurement error of each user's electricity meter, the line loss in the substation area, and the electricity data of the user's electricity meter and the total meter in each time period are substituted into the total meter error calculation model to calculate the total meter error in each time period.

[0009] Furthermore, the total table error calculation model is:

[0010]

[0011] in, represents the measurement error of the total meter in the jth time period, y j represents the total electricity data of the meter in the jth time period, a i,j represents the electricity data of the i-th user's electricity meter in the j-th time period, δ i represents the measurement error of the ith user's electricity meter, r j represents the line loss in the area in the jth time period, and n represents the number of user electricity meters.

[0012] Furthermore, the process of optimizing and solving the electricity data of the user's electricity meter and the master meter in multiple time periods by using the differential evolution algorithm to obtain the measurement error of each user's electricity meter and the line loss of the substation area includes the following contents:

[0013] Initialize the population size and represent each individual in the population as ω i =[δ1,δ2,...,δ n ,r j ],δ1,δ2,...,δ n Represents the measurement error of all user meters, r j represents the area line loss in the jth time period, and assumes that the measurement errors of all user meters and the area line loss remain unchanged during the sampling period;

[0014] The search space of each individual is given and randomly initialized;

[0015] For each individual ω i , randomly select three different individuals from the population for mutation and crossover operations to generate experimental individuals μ i ;

[0016] For the experimental individual μ i and the current individual ω i , calculate the objective function values ​​respectively, and select the individuals with the best objective function values ​​to enter the next generation;

[0017] The mutation, crossover and selection operations are repeated to update the population until the convergence condition is reached. The measurement error of each user's meter and the line loss in the substation area are obtained based on the optimal individual.

[0018] Furthermore, the following contents are also included:

[0019] The electricity data of user meters and total meters in multiple sampling cycles are collected, and the above steps are repeated to calculate the total meter error in each time period in multiple sampling cycles, and the weighted average of the total meter error in each time period in multiple sampling cycles is calculated to obtain the average error of the total meter in multiple sampling cycles.

[0020] Furthermore, the process of performing weighted averaging on the total meter error in each time period in multiple sampling periods to calculate the average error of the total meter in multiple sampling periods includes the following contents:

[0021] First, the standard deviation of the total meter error of each sampling period is calculated based on the total meter error of multiple time periods within each sampling period, and then the correction weight coefficient of each sampling period is calculated based on the standard deviation of the total meter error of each sampling period. Then, the total meter error of multiple sampling periods is weightedly corrected based on the correction weight coefficients of multiple sampling periods, and the average error of the total meter in multiple sampling periods is calculated.

[0022] Furthermore, the average error of the total table in multiple sampling periods is calculated based on the following formula:

[0023]

[0024] in, It represents the average error of the total meter in multiple sampling periods. It represents the average error of the total meter in a sampling period. represents the total table error of the jth time period, T represents the number of sampling periods, m represents the number of time periods contained in a sampling period, and λ t Represents the correction weight coefficient of the tth sampling period.

[0025] Furthermore, the correction weight coefficient of each sampling period is calculated based on the following formula:

[0026]

[0027] Among them, σ t represents the standard deviation of the total table error in the tth sampling period, and C represents a constant.

[0028] In addition, the present invention also provides a total meter error online monitoring system based on user meter data, comprising:

[0029] A data collection module is used to collect electricity data of user meters and total meters in multiple time periods of a sampling cycle respectively;

[0030] A model building module is used to build a total table error calculation model based on the law of conservation of energy;

[0031] The optimization solution module is used to optimize and solve the electricity data of user meters and master meters in multiple time periods using a differential evolution algorithm to obtain the measurement error of each user meter and the line loss of the substation area;

[0032] The error calculation module is used to substitute the measurement error of each user's electric meter, the line loss in the substation area, and the electric quantity data of the user's electric meter and the total meter in each time period into the total meter error calculation model to calculate the total meter error in each time period.

[0033] In addition, the present invention also provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the above method by calling the computer program stored in the memory.

[0034] In addition, the present invention also provides a computer-readable storage medium for storing a computer program for online monitoring of total meter errors based on user meter data, wherein the computer program executes the steps of the method described above when running on a computer.

[0035] The present invention has the following beneficial effects:

[0036] The method for online monitoring of total meter errors based on user meter data of the present invention first collects electricity data of user meters and total meters in multiple time periods, then builds a total meter error measurement model based on the law of conservation of energy, builds the relationship between total meter errors and user meter errors, substation line losses, user meter electricity readings and total meter electricity readings in the same time period, and then uses a differential evolution algorithm to optimize and solve the measurement error of each user meter and substation line loss, so as to calculate the total meter error in each time period, and realize online real-time monitoring of total meter errors, so as to facilitate real-time calibration of total meter errors, thereby improving the real-time accuracy of electricity metering, and thus ensuring the accuracy of electricity transaction settlement.

[0037] In addition, the total meter error online monitoring system based on user meter data of the present invention also has the above advantages.

[0038] In addition to the above-described purposes, features and advantages, the present invention has other purposes, features and advantages. The present invention will be further described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings constituting a part of this application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0040] Figure 1 It is a flow chart of a method for online monitoring of total meter error based on user electric meter data according to a preferred embodiment of the present application.

[0041] Figure 2 It is another flow chart of the method for online monitoring of total meter error based on user meter data of a preferred embodiment of the present application.

[0042] Figure 3 It is a schematic diagram of the module structure of a total meter error online monitoring system based on user meter data in another embodiment of the present application. DETAILED DESCRIPTION

[0043] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0044] Reference Figure 1 The preferred embodiment of the present application provides a method for online monitoring of total meter error based on user meter data, comprising the following contents:

[0045] Step S1: respectively collecting the electricity data of the user's electricity meter and the total meter in multiple time periods of a sampling cycle;

[0046] Step S2: constructing a total table error calculation model based on the law of conservation of energy;

[0047] Step S3: using a differential evolution algorithm to optimize and solve the electricity data of user meters and master meters in multiple time periods, and obtain the measurement error of each user meter and the line loss of the substation area;

[0048] Step S4: Substitute the measurement error of each user's electricity meter, the line loss in the substation area, and the electricity data of the user's electricity meter and the total meter in each time period into the total meter error calculation model to calculate the total meter error in each time period.

[0049] It can be understood that the online monitoring method of the total meter error based on the user meter data of this embodiment first collects the electricity data of the user meter and the total meter in multiple time periods, and then constructs a total meter error measurement model based on the law of conservation of energy, and constructs the relationship between the total meter error and the user meter error, the substation line loss, the user meter electricity reading and the total meter electricity reading in the same time period, and then uses the differential evolution algorithm to optimize and solve the measurement error of each user meter and the substation line loss, so that the total meter error in each time period can be calculated, and the online real-time monitoring of the total meter error can be realized, so as to facilitate the real-time calibration of the total meter error, thereby improving the real-time accuracy of electricity metering, thereby ensuring the accuracy of electricity transaction settlement.

[0050] It can be understood that in step S1, the electricity data of the user meter and the total meter in multiple time periods of a sampling cycle are collected respectively, including the readings of each user meter, the readings of the total meter and the corresponding timestamps in each time period. Among them, the time length of a sampling cycle is relatively short, generally one day, two days or three days. The electricity data of the user meter and the total meter are collected multiple times in a sampling cycle, and the electricity data in multiple time periods can be obtained. As an example, a sampling cycle is one day, and one day includes 96 time periods, that is, data is collected every 15 minutes in one day. Of course, in other embodiments of the present invention, the length of the sampling cycle and the number of time periods in each sampling cycle can be selected according to actual needs.

[0051] Optionally, after collecting the electricity data, it is also necessary to pre-process the electricity data, for example, including checking the time series electricity data of the user's electricity meter and the total meter. If the reading at a certain time point is too different from the readings before and after and does not conform to historical rules, it is determined to be abnormal data and is eliminated. It is also necessary to ensure that the electricity data of each user's electricity meter and the total meter are complete within a consistent time period. If the data of certain time periods are missing, it can be supplemented by interpolation (such as linear interpolation or Lagrange interpolation), or the data of the time period can be directly eliminated. In addition, since different electricity meters may have different electricity consumption ranges and fluctuation ranges, the electricity data needs to be standardized. Standardization can use Z-score standardization to normalize the data of each meter to the same range for subsequent analysis.

[0052] It can be understood that in step S2, according to the law of conservation of energy, the total power consumption of the area should be equal to the sum of the actual power consumption of all users plus the line loss. Therefore, the reading of the total meter of the area should satisfy the following relationship: Among them, y j represents the reading of the total meter in the area in the jth time period, a i,j represents the reading of the i-th smart meter in the j-th time period, r j It represents the line loss of the substation area in the jth time period. Assume that each user's electricity meter has a certain measurement error δ i , then the user's actual electricity consumption With the user's meter reading a i,j The relationship is: δ i represents the measurement error of the ith user's meter. The measurement error of the user's meter is usually related to environmental factors and its own measurement error. The environmental and self-measurement errors will basically not change in a short period of time. Therefore, it can be considered that the measurement error of the user's meter is fixed in a short period of time. The true reading of the total meter should be the sum of the true power consumption of all users plus the line loss of the substation area, that is, However, the total meter itself may also have errors, so the total meter reading yj and the actual total power There is an error between them, which can be expressed as: Therefore, the total table error calculation model can be constructed as follows: in, represents the measurement error of the total meter in the jth time period, y j represents the total electricity data of the meter in the jth time period, a i,j represents the electricity data of the i-th user's electricity meter in the j-th time period, δ i represents the measurement error of the ith user's electricity meter, r j represents the line loss in the area in the jth time period, and n represents the number of user electricity meters.

[0053] It can be understood that in step S3, the process of optimizing and solving the power data of the user meter and the master meter in multiple time periods using the differential evolution algorithm to obtain the measurement error of each user meter and the line loss of the station area includes the following contents:

[0054] Initialize the population size and represent each individual in the population as ω i =[δ1,δ2,...,δ n ,r j ],δ1,δ2,...,δ n Represents the measurement error of all user meters, r j represents the area line loss in the jth time period, and assumes that the measurement errors of all user meters and the area line loss remain unchanged during the sampling period;

[0055] The search space of each individual is given and randomly initialized;

[0056] For each individual ω i , randomly select three different individuals from the population for mutation and crossover operations to generate experimental individuals μ i ;

[0057] For the experimental individual μ i and the current individual ω i , calculate the objective function values ​​respectively, and select the individuals with the best objective function values ​​to enter the next generation;

[0058] The mutation, crossover and selection operations are repeated to update the population until the convergence condition is reached. The measurement error of each user's meter and the line loss in the substation area are obtained based on the optimal individual.

[0059] Specifically, we first initialize the population size N, usually selecting dozens to hundreds of individuals, each of which represents a possible solution. Each solution includes δ i and r j, that is, each individual in the population is represented by ω i =[δ1,δ2,...,δ n ,r j ],δ1,δ2,...,δ n Represents the measurement error of all user meters, r j represents the area line loss in the jth time period. Since a sampling period is short, it is assumed that the measurement errors of all user meters and the area line loss remain unchanged during the sampling period.

[0060] Then, the error range of the user's meter can be determined through historical data. For example, the error range of a normal single-phase meter is between -2% and +2%, and the error range of a three-phase meter is between -1% and 1%. The user's meter is generally a single-phase meter, so δ i The search space is set to -2% ≤ δ i ≤2%. In addition, the maximum value of the line loss in the substation area can be obtained through historical data statistics, so r j The search space is set to 0≤r j ≤r max , r max It represents the maximum value of the line loss in the substation area obtained based on historical data statistics. After setting the search space of each individual, random initialization can be performed.

[0061] Next, for each individual ω in the population i , randomly select three different individuals ω from the population r1 ,ω r2 ,ω r3 Perform mutation operation to generate a new mutant individual ν i : ν i =ω r1 +F·(ω r2 -ω r3 ), where F is the scaling factor, usually between 0.4 and 0.95. Then, for the variant individual ν i and the current individual ω i Perform crossover operation to generate experimental individuals μ i , the crossover operation can be expressed as: CR represents the crossover probability, which is usually between 0.3 and 0.9, and determines that at least one parameter has mutated.

[0062] Then, for the experimental individual μ i and the current individual ω i Calculate the objective function values ​​respectively, the objective function is: Select individuals with better objective function values ​​to enter the next generation. The selection operation can be expressed as: Through the selection operation, better solutions can be retained and poor solutions can be eliminated.

[0063] Finally, the mutation, crossover and selection operations are repeated to update the population until the convergence condition is reached, such as the objective function value is less than the preset threshold, or the maximum number of iterations is reached, and the measurement error δ of each user's meter can be obtained based on the optimal individual. i and area line loss r j .

[0064] It can be understood that in step S4, the measurement error δ of each user's electric meter is obtained by optimizing the solution. i and area line loss r j After that, it can be substituted into the total table error calculation model: In the above example, the power data of the user meter and the total meter in each time period are combined. i,j and j , the total meter error for each time period can be calculated. In addition, the average value of the total meter error for multiple time periods within a sampling period can be calculated to obtain the average error of the total meter within a sampling period. The calculation formula is: m represents the number of time periods included in a sampling cycle, which is output as the online monitoring result of the error of the total table.

[0065] It can be understood that in the above optimization solution process, it is assumed that the line loss of the substation area remains unchanged in each sampling period. However, in the actual substation area, there is a certain correlation between the line loss of the substation area and the total power consumption of the substation area. Generally, the greater the total power consumption of the substation area, the greater the line loss of the substation area. Therefore, optionally, the present invention uses historical data and a linear regression model to estimate the line loss of the substation area, wherein the linear regression model is expressed as: r j =α·y j +β, using the historical area line loss values ​​and the area total meter readings for regression fitting, calculate the regression coefficients α and β, and then use the area total meter readings y in each time period j Substituting into the linear regression model, the line loss r of the current time period can be calculated accordingly. j , when calculating the total table error for each time period, the r obtained based on the regression calculation j Replace the r obtained based on the optimization solution j , which can improve the accuracy of the line loss in the substation area, thereby improving the calculation accuracy of the total meter error.

[0066] Alternatively, if Figure 2 As shown, the total meter error online monitoring method based on user meter data also includes the following contents:

[0067] Step S5: Collect electricity data from user meters and total meters in multiple sampling cycles, and repeat the above steps to calculate the total meter error for each time period in multiple sampling cycles, and perform weighted averaging based on the total meter error for each time period in multiple sampling cycles to calculate the average error of the total meter in multiple sampling cycles.

[0068] Specifically, the above steps S1 to S4 calculate the total meter error based on the electricity data of only one sampling period (generally one day). In order to avoid the interference of occasional factors and further improve the accuracy of online monitoring of the total meter error, the present invention also collects the electricity data of the user meter and the total meter in multiple sampling periods, and repeats the above steps S1 to S4 for the electricity data of the user meter and the total meter in each sampling period. The total meter error of each time period in multiple sampling periods can be calculated, and the total meter error of each time period in multiple sampling periods (for example, three days, five days or one week) is weighted averaged, different weights are assigned to different sampling periods, and the average error of the total meter in multiple sampling periods is calculated.

[0069] The process of performing weighted averaging on the total meter error in each time period in multiple sampling periods to calculate the average error of the total meter in multiple sampling periods includes the following:

[0070] First, the standard deviation of the total meter error of each sampling period is calculated based on the total meter error of multiple time periods within each sampling period, and then the correction weight coefficient of each sampling period is calculated based on the standard deviation of the total meter error of each sampling period. Then, the total meter error of multiple sampling periods is weightedly corrected based on the correction weight coefficients of multiple sampling periods, and the average error of the total meter in multiple sampling periods is calculated.

[0071] Specifically, the correction weight coefficient of each sampling period is first calculated based on the following formula:

[0072]

[0073] Among them, λ t represents the correction weight coefficient of the tth sampling period, σ t It represents the standard deviation of the total table error in the tth sampling period, and C represents a constant. It can be seen from the above formula that when the error standard deviation within a certain sampling period is larger, it means that the volatility of the total table error is higher, and the stability of the total table error data is worse, thus giving a smaller correction weight coefficient, and when the error standard deviation within a certain sampling period is smaller, it means that the volatility of the total table error is lower, and the stability of the total table error data is better, thus giving a higher correction weight coefficient, thereby improving the robustness and accuracy of the algorithm.

[0074] Then, the average error of the total meter in multiple sampling periods is calculated based on the following formula:

[0075]

[0076] in, It represents the average error of the total meter in multiple sampling periods. It represents the average error of the total meter in a sampling period. represents the total table error of the jth time period, T represents the number of sampling periods, m represents the number of time periods contained in a sampling period, and λ t Represents the correction weight coefficient of the tth sampling period.

[0077] It can be understood that the present invention calculates the total meter error of the electricity data of multiple sampling periods, performs stability analysis on the total meter error data of multiple sampling periods, and assigns higher weights to sampling periods with good stability, thereby improving the robustness and accuracy of the total meter error online monitoring algorithm.

[0078] Optionally, in step S4, after calculating the total table error value of all time periods within a sampling cycle, the offset value between the total table error value of each time period and the average error value of the total table within a sampling cycle can be calculated first, and then the weight coefficient of each time period is calculated based on the offset value, and then the total table error values ​​of all time periods are weighted and summed based on the weight coefficients of all time periods to calculate the average error of the total table within a sampling cycle.

[0079] Specifically, the offset value between the total meter error value in each time period and the total meter average error value in a sampling period is calculated based on the following formula:

[0080]

[0081] in, It represents the offset between the total meter error value in the jth time period and the average error value of the total meter in one sampling period.

[0082] Then, the weight coefficient for each time period is calculated based on the following formula:

[0083]

[0084] Among them, η j represents the weight coefficient of the jth time period, and D represents a constant.

[0085] Finally, the average error of the total table within a sampling period is calculated based on the following formula:

[0086]

[0087] in, It represents the average error of the total table in a sampling period, and m represents the number of time periods included in a sampling period.

[0088] It can be understood that by analyzing the total meter error data of multiple time periods within a sampling cycle, the time periods with larger deviations from the total meter error mean are assigned smaller weight coefficients, while the time periods with smaller deviations from the total meter error mean are assigned larger weight coefficients. This reduces the impact of occasional large total meter errors due to accidental factors within a sampling cycle, and improves the robustness and accuracy of the online monitoring algorithm for the total meter error.

[0089] In addition, if Figure 3 As shown, another embodiment of the present invention further provides a total meter error online monitoring system based on user meter data, preferably using the total meter error online monitoring method as described above, the system comprising:

[0090] A data collection module is used to collect electricity data of user meters and total meters in multiple time periods of a sampling cycle respectively;

[0091] A model building module is used to build a total table error calculation model based on the law of conservation of energy;

[0092] The optimization solution module is used to optimize and solve the electricity data of user meters and master meters in multiple time periods using a differential evolution algorithm to obtain the measurement error of each user meter and the line loss of the substation area;

[0093] The error calculation module is used to substitute the measurement error of each user's electric meter, the line loss in the substation area, and the electric quantity data of the user's electric meter and the total meter in each time period into the total meter error calculation model to calculate the total meter error in each time period.

[0094] It can be understood that the online monitoring system of the total meter error based on user meter data in this embodiment first collects electricity data of user meters and total meters in multiple time periods, and then constructs a total meter error measurement model based on the law of conservation of energy, and constructs the relationship between the total meter error and the user meter error, the substation line loss, the user meter electricity readings and the total meter electricity readings in the same time period, and then uses the differential evolution algorithm to optimize and solve the measurement error of each user meter and the substation line loss, so as to calculate the total meter error in each time period, and realize online real-time monitoring of the total meter error, so as to facilitate real-time calibration of the total meter error, thereby improving the real-time accuracy of electricity metering, thereby ensuring the accuracy of electricity transaction settlement.

[0095] In addition, another embodiment of the present invention further provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the above method by calling the computer program stored in the memory.

[0096] In addition, another embodiment of the present invention further provides a computer-readable storage medium for storing a computer program for online monitoring of total meter errors based on user electricity meter data, wherein the computer program executes the steps of the method described above when running on a computer.

[0097] The general form of computer readable storage media includes: floppy disk, flexible disk, hard disk, magnetic tape, any other magnetic medium, CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with a pattern of holes, random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), flash erasable programmable read-only memory (FLASH-EPROM), any other memory chip or cartridge, or any other medium that can be read by a computer. The instructions can further be transmitted or received by a transmission medium. The term transmission medium can include any tangible or intangible medium that can be used to store, encode or carry instructions for execution by a machine, and includes digital or analog communication signals or other intangible media that facilitate the communication of the above instructions. Transmission media include coaxial cables, copper wires and optical fibers, which include the wires of a bus used to transmit a computer data signal.

[0098] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0099] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0100] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0101] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0102] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0103] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

[0104] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A total meter error online monitoring method based on user meter data, characterized in that: Includes the following: Collect the electricity data of user meters and total meters in multiple time periods of a sampling cycle respectively; Construct a total table error calculation model based on the law of conservation of energy; The differential evolution algorithm is used to optimize and solve the electricity data of user meters and master meters in multiple time periods to obtain the measurement error of each user meter and the line loss in the substation area. The measurement error of each user's electricity meter, the line loss in the substation area, and the electricity data of the user's electricity meter and the total meter in each time period are substituted into the total meter error calculation model to calculate the total meter error in each time period.

2. The method for online monitoring of total meter error based on user meter data according to claim 1, characterized in that: The total table error calculation model is: in, represents the measurement error of the total meter in the jth time period, y j represents the total electricity data of the meter in the jth time period, a i,j represents the electricity data of the i-th user's electricity meter in the j-th time period, δ i represents the measurement error of the ith user's electricity meter, r j represents the line loss in the area in the jth time period, and n represents the number of user electricity meters.

3. The method for online monitoring of total meter error based on user meter data according to claim 2, characterized in that: The process of using the differential evolution algorithm to optimize and solve the electricity data of the user's electricity meter and the total meter in multiple time periods to obtain the measurement error of each user's electricity meter and the line loss in the substation area includes the following contents: Initialize the population size and represent each individual in the population as ω i =[δ1,δ2,...,δ n ,r j ],δ1,δ2,...,δ n represents the measurement error of all user meters, rj represents the line loss of the area in the jth time period, and it is assumed that the measurement error of all user meters and the line loss of the area remain unchanged during the sampling period; The search space of each individual is given and randomly initialized; For each individual ω i , randomly select three different individuals from the population for mutation and crossover operations to generate experimental individuals μ i ; For the experimental individual μ i and the current individual ω i , calculate the objective function values ​​respectively, and select the individuals with the best objective function values ​​to enter the next generation; The mutation, crossover and selection operations are repeated to update the population until the convergence condition is reached. The measurement error of each user's meter and the line loss in the substation area are obtained based on the optimal individual.

4. The method for online monitoring of total meter error based on user meter data according to claim 1, characterized in that: Also included are the following: The electricity data of user meters and total meters in multiple sampling cycles are collected, and the above steps are repeated to calculate the total meter error in each time period in multiple sampling cycles, and the weighted average of the total meter error in each time period in multiple sampling cycles is calculated to obtain the average error of the total meter in multiple sampling cycles.

5. The method for online monitoring of total meter error based on user meter data according to claim 4, characterized in that: The process of performing weighted averaging on the total meter error in each time period in multiple sampling periods to calculate the average error of the total meter in multiple sampling periods includes the following: First, the standard deviation of the total meter error of each sampling period is calculated based on the total meter error of multiple time periods within each sampling period, and then the correction weight coefficient of each sampling period is calculated based on the standard deviation of the total meter error of each sampling period. Then, the total meter error of multiple sampling periods is weightedly corrected based on the correction weight coefficients of multiple sampling periods, and the average error of the total meter in multiple sampling periods is calculated.

6. The method for online monitoring of total meter error based on user meter data according to claim 5, characterized in that: The average error of the total meter in multiple sampling periods is calculated based on the following formula: in, It represents the average error of the total meter in multiple sampling periods. It represents the average error of the total meter in a sampling period. represents the total table error of the jth time period, T represents the number of sampling periods, m represents the number of time periods contained in a sampling period, and λ t Represents the correction weight coefficient of the tth sampling period.

7. The method for online monitoring of total meter error based on user meter data according to claim 6, characterized in that: The correction weight coefficient for each sampling period is calculated based on the following formula: Among them, σ t represents the standard deviation of the total table error in the tth sampling period, and C represents a constant.

8. An online monitoring system for total meter error based on user meter data, characterized in that: include: A data collection module is used to collect electricity data of user meters and total meters in multiple time periods of a sampling cycle respectively; A model building module is used to build a total table error calculation model based on the law of conservation of energy; The optimization solution module is used to optimize and solve the electricity data of user meters and master meters in multiple time periods using a differential evolution algorithm to obtain the measurement error of each user meter and the line loss of the substation area; The error calculation module is used to substitute the measurement error of each user's electric meter, the line loss in the substation area, and the electric quantity data of the user's electric meter and the total meter in each time period into the total meter error calculation model to calculate the total meter error in each time period.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the method according to any one of claims 1 to 7 by calling the computer program stored in the memory.

10. A computer-readable storage medium for storing a computer program for online monitoring of total meter errors based on user meter data, characterized in that: When the computer program is run on a computer, the steps of the method according to any one of claims 1 to 7 are executed.

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