A total meter error online monitoring method and system based on user electricity meter data, equipment and medium

Abstract

The application discloses a kind of total meter error online monitoring method and system based on user electric meter data, equipment, medium, it first acquires the electric quantity data of user electric meter and total meter in multiple time periods, then based on energy conservation law constructs total meter error measurement model, constructs the relationship between total meter error and user electric meter error, district line loss, the electric quantity reading of user electric meter and total meter in the same period, again using differential evolution algorithm optimization solution obtains the measurement error of each user electric meter and district line loss, so that the total meter error of each time period can be calculated, the online real-time monitoring of total meter error can be realized, so as to calibrate total meter error in real time, improve the real-time accuracy of electric energy measurement, so as to guarantee the accuracy of electric energy transaction settlement.

Description

Technical Field

[0001] This invention relates to the field of online monitoring technology for total meter error, and in particular, to a method and system for online monitoring of total meter error based on user meter data, an electronic device, and a computer-readable storage medium. Background Technology

[0002] In a typical power distribution area, there is usually one master meter and multiple user meters. The master meter records the total electricity consumption of the entire area, while the user meters record the electricity consumption of each individual user. As the primary device for electricity metering between the power grid and users, the accuracy of the master meter directly affects the electricity transaction settlement between the grid and users. Because the master meter has high accuracy, its readings are usually assumed to be accurate. Therefore, the power system currently mainly performs periodic calibrations to address the errors in user meters. However, the master meter may also have errors and requires calibration as well. Traditional methods for calibrating electricity meter errors typically involve manual periodic calibration and offline testing, resulting in time lags, a large workload, and an inability to monitor the actual error of the master meter in real time. Summary of the Invention

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

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

[0005] Electricity data from user meters and the main meter are collected separately for multiple time periods within a sampling period;

[0006] A summary table error calculation model was constructed based on the law of conservation of energy.

[0007] The differential evolution algorithm is used to optimize the solution based on the electricity consumption data of user meters and master meters in multiple time periods, so as to obtain the measurement error of each user meter and the line loss of the transformer area.

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

[0009] Furthermore, the summary table error calculation model is as follows:

[0010]

[0011] in, y represents the measurement error of the summary table in the j-th time period. j This represents the electricity consumption data in the summary table during the j-th time period, a. i,j δ represents the electricity consumption data of the i-th user's meter in the j-th time period. i Let r represent the measurement error of the i-th user's meter. j This represents the line loss in the j-th time period, and n represents the number of user meters.

[0012] Furthermore, the process of using the differential evolution algorithm to optimize and solve for the measurement error of each user's meter and the line loss of the transformer area based on the electricity consumption data of the user's meter and the master meter over multiple time periods includes the following:

[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 r represents the measurement error of all users' electricity meters. j Let represent the transformer area line loss in the j-th time period, and assume that the measurement error of all users' meters and the transformer area line loss remain constant during the sampling period;

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

[0015] For each individual ω i Three different individuals are randomly selected from the population and subjected to mutation and crossover operations to generate experimental individuals μ. i ;

[0016] For experimental individual μ i and the current individual ω i Calculate the objective function value for each individual, and select the individual with the optimal objective function value to enter the next generation;

[0017] Repeatedly perform mutation, crossover, and selection operations to update the population until convergence is achieved. Based on the optimal individual, obtain the measurement error of each user's meter and the line loss of the transformer area.

[0018] Furthermore, it also includes the following:

[0019] Collect electricity data from user meters and the main meter over multiple sampling periods. After repeating the above steps, calculate the main meter error for each time period in the multiple sampling periods. Then, calculate the average error of the main meter over the multiple sampling periods by performing a weighted average of the main meter errors for each time period in the multiple sampling periods.

[0020] Furthermore, the process of calculating the average error of the total table over multiple sampling periods by weighted averaging of the total table error for each time period in multiple sampling periods includes the following:

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

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

[0023]

[0024] in, This represents the average error of the summary table over multiple sampling periods. This represents the average error of the summary table over one sampling period. Let λ represent the total table error for the j-th time period, T represent the number of sampling periods, m represent the number of time periods contained within a sampling period, and λ represent the total table error for the j-th time period. t This represents the corrected weighting coefficient for the t-th sampling period.

[0025] Furthermore, the corrected weighting coefficient for each sampling period is calculated based on the following formula:

[0026]

[0027] Where, σ t Let C represent the standard deviation of the total table error in the t-th sampling period, and let C be a constant.

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

[0029] The data acquisition module is used to collect electricity data from user meters and the main meter for multiple time periods within a sampling period.

[0030] The model building module is used to build a summary table error calculation model based on the law of conservation of energy.

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

[0032] The error calculation module is used to input the measurement error of each user's meter, the line loss of the transformer area, and the electricity data of the user's meter and the main meter for each time period into the main meter error calculation model to calculate the main meter error for 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 method described above 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 error 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 present invention discloses an online monitoring method for total meter error based on user meter data. First, it collects electricity data from user meters and the total meter over multiple time periods. Then, based on the law of conservation of energy, it constructs a total meter error measurement model, establishing the relationship between total meter error and user meter errors, transformer line losses, and the electricity readings of user meters and the total meter during the same time period. Next, it uses a differential evolution algorithm to optimize and solve for the measurement error of each user meter and the transformer line loss, thereby calculating the total meter error for each time period. This enables real-time online monitoring of the total meter error, facilitating real-time calibration and improving the real-time accuracy of electricity metering, thus ensuring the accuracy of electricity trading settlement.

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

[0038] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the figures. Attached Figure Description

[0039] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0040] Figure 1 This is a flowchart illustrating a preferred embodiment of the online monitoring method for total meter error based on user meter data.

[0041] Figure 2 This is another schematic diagram of the online monitoring method for total meter error based on user meter data, which is a preferred embodiment of this application.

[0042] Figure 3 This is a schematic diagram of the module structure of an online monitoring system for total meter error based on user meter data, according to another embodiment of this application. Detailed Implementation

[0043] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0044] Reference Figure 1 A preferred embodiment of this application provides a method for online monitoring of total meter error based on user meter data, including the following:

[0045] Step S1: Collect electricity data from user meters and main meters for multiple time periods within a sampling period;

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

[0047] Step S3: Use the differential evolution algorithm to optimize the solution based on 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 transformer area;

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

[0049] It is understood that the online monitoring method for total meter error based on user meter data in this embodiment first collects electricity data from user meters and the total meter over multiple time periods. Then, based on the law of conservation of energy, a total meter error measurement model is constructed to establish the relationship between the total meter error and user meter errors, transformer line losses, and the electricity readings of user meters and the total meter in the same time period. The differential evolution algorithm is then used to optimize and solve for the measurement error of each user meter and the transformer line loss, thereby calculating the total meter error for each time period. This enables online real-time monitoring of the total meter error, facilitating real-time calibration of the total meter error and improving the real-time accuracy of electricity metering, thus ensuring the accuracy of electricity trading settlement.

[0050] It is understood that in step S1, electricity data from user meters and the main meter are collected over multiple time periods within a sampling period, including the readings of each user meter, the main meter reading, and the corresponding timestamps for each time period. A sampling period is typically short, usually one, two, or three days. Multiple data collections from user meters and the main meter within a sampling period yield electricity data for multiple time periods. For example, a sampling period of one day includes 96 time periods, meaning data is collected every 15 minutes. Of course, in other embodiments of the invention, the length of the sampling period and the number of time periods within each sampling period can be selected according to actual needs.

[0051] Optionally, after collecting electricity consumption data, preprocessing is required. This includes checking the time-series electricity consumption data of user meters and the master meter. If the reading at a certain point in time differs significantly from the readings before and after it and does not conform to historical patterns, it is considered abnormal data and removed. It is also necessary to ensure that the electricity consumption data of each user meter and the master meter are complete within a consistent time period. If data is missing for certain time periods, it can be supplemented using interpolation methods (such as linear interpolation or Lagrange interpolation), or the data for that time period can be directly removed. Furthermore, since different meters may have different electricity consumption ranges and fluctuations, the electricity consumption data needs to be standardized. Standardization can use Z-score standardization to normalize the data from each meter to the same range for subsequent analysis.

[0052] It is understood that in step S2, according to the law of conservation of energy, the total electricity consumption of the transformer area should be equal to the sum of the actual electricity consumption of all users plus line losses. Therefore, the reading of the transformer area's main meter should satisfy the following relationship: Among them, y j This represents the reading of the station's master meter in the j-th time period, a i,j Let r represent the reading of the i-th smart meter in the j-th time period. j Let represent the line loss in the j-th time period. Assume that each user's meter has a certain measurement error δ. i Then the user's actual electricity consumption With user's meter reading a i,j The relationship is: δ i This represents the measurement error of the i-th user's meter. The measurement error of a user's meter is usually related to environmental factors and its own metering error. Since these environmental and metering errors generally do not change significantly over a short period, the measurement error of a user's meter can be considered constant over a short time. The true reading of the main meter should be the sum of the actual electricity consumption of all users plus the line loss in the distribution area, i.e. However, the main meter itself may also have errors, therefore the main meter reading yj and the actual total electricity consumption will differ. There is an error between them, which can be expressed as: Therefore, the summary table error calculation model can be constructed as follows: in, y represents the measurement error of the summary table in the j-th time period. j This represents the electricity consumption data in the summary table during the j-th time period, a. i,j δ represents the electricity consumption data of the i-th user's meter in the j-th time period. i Let r represent the measurement error of the i-th user's meter. j This represents the line loss in the j-th time period, and n represents the number of user meters.

[0053] It is understood that in step S3, the process of using the differential evolution algorithm to optimize and solve based on the electricity consumption data of user meters and the main meter over multiple time periods to obtain the measurement error of each user meter and the line loss of the transformer area includes the following:

[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 r represents the measurement error of all users' electricity meters. j Let represent the transformer area line loss in the j-th time period, and assume that the measurement error of all users' meters and the transformer area line loss remain constant during the sampling period;

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

[0056] For each individual ω i Three different individuals are randomly selected from the population and subjected to mutation and crossover operations to generate experimental individuals μ. i ;

[0057] For experimental individual μ i and the current individual ω i Calculate the objective function value for each individual, and select the individual with the optimal objective function value to enter the next generation;

[0058] Repeatedly perform mutation, crossover, and selection operations to update the population until convergence is achieved. Based on the optimal individual, obtain the measurement error of each user's meter and the line loss of the transformer area.

[0059] Specifically, the population size N is first initialized, typically selecting tens to hundreds of individuals. Each individual represents a possible solution, and each solution includes δ. i and r jThat is, each individual in the population is represented by ω. i =[δ1,δ2,...,δ n ,r j ],δ1,δ2,...,δ n r represents the measurement error of all users' electricity meters. j Let represent the line loss of the transformer substation during the j-th time period. Since the duration of a sampling period is short, it is assumed that the measurement error of all user meters and the line loss of the transformer substation remain constant within the sampling period.

[0060] Then, historical data can be used to determine the error range of the user's electricity meter. 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%. Since user meters are generally single-phase meters, δ... i The search space is set to -2% ≤ δ i ≤2%. Furthermore, the maximum line loss in the distribution area can be statistically determined using historical data; therefore, r... j The search space is set to 0 ≤ r j ≤r max r max This represents the maximum line loss value for the transformer area obtained based on historical data statistics. Random initialization can be performed after the search space for each individual element has been defined.

[0061] Next, for each individual ω in the population i Three different individuals ω are randomly selected from the population. r1 ω r2 ω r3 Perform a mutation operation to generate a new mutated individual ν i :ν i =ω r1 +F·(ω r2 -ω r3 ), where F represents the scaling factor, typically between 0.4 and 0.95. Then, for the variant individuals ν... i and the current individual ω i Perform crossover operations to generate experimental individuals μ i The crossover operation can be represented as: CR stands for crossover probability, which is typically 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 value separately. The objective function is: The selection operation, which chooses individuals with better objective function values ​​to proceed to the next generation, can be represented as: By using selection operations, better solutions can be retained while poorer solutions can be discarded.

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

[0064] It is understood that in step S4, the measurement error δ of each user's electricity meter is obtained through optimization. i And the line loss r in the transformer area j Then, it can be substituted into the summary table error calculation model: In addition, the electricity consumption data from user meters and the master meter for each time period is combined. i,j and y j This allows you to calculate the total table error for each time period. Alternatively, you can calculate the average total table error over a sampling period by averaging the errors from multiple time periods within the same sampling period. The formula is: m represents the number of time periods included in a sampling period, which is used as the output of the online error monitoring results in the summary table.

[0065] It is understood that the above optimization process assumes that the line loss of the transformer substation remains constant in each sampling period. However, in actual transformer substations, the line loss is correlated with the total electricity consumption of the substation; generally, the higher the total electricity consumption, the higher the line loss. Therefore, optionally, this invention uses historical data and a linear regression model to estimate the line loss of the transformer substation, wherein the linear regression model is expressed as: r j =α·y j +β, using historical transformer substation line loss values ​​and transformer substation meter readings for regression fitting, calculate the regression coefficients α and β, and then use the current transformer substation meter readings y for each time period. j Substituting the values ​​into the linear regression model, the line loss r of the transformer area for the current time period can be calculated. j When calculating the total table error for each time period, the r calculated based on regression will be used. j Replace r obtained based on optimization solution j This can improve the accuracy of line loss in the transformer area, thereby improving the accuracy of the total meter error calculation.

[0066] Optionally, such as Figure 2 As shown, the online monitoring method for total meter error based on user meter data also includes the following:

[0067] Step S5: Collect electricity data of user meters and main meter in multiple sampling periods, and repeat the above steps to calculate the main meter error for each time period in multiple sampling periods. Then, calculate the average error of the main meter in multiple sampling periods by weighted averaging the main meter error for each time period in multiple sampling periods.

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

[0069] The process of calculating the average error of the total table over multiple sampling periods by weighted averaging the total table error for each time period in multiple sampling periods includes the following:

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

[0071] Specifically, the corrected weighting coefficient for each sampling period is first calculated based on the following formula:

[0072]

[0073] Where, λ t σ represents the correction weighting coefficient for the t-th sampling period. t Let represent the standard deviation of the total table error in the t-th sampling period, and C represent a constant. From the above formula, it can be seen that a larger standard deviation within a sampling period means higher volatility of the total table error, resulting in poorer stability of the total table error data, thus assigning a smaller correction weight coefficient. Conversely, a smaller standard deviation within a sampling period means lower volatility of the total table error, resulting in better stability of the total table error data, thus assigning a higher correction weight coefficient, thereby improving the robustness and accuracy of the algorithm.

[0074] Then, the average error of the summary table over multiple sampling periods is calculated based on the following formula:

[0075]

[0076] in, This represents the average error of the summary table over multiple sampling periods. This represents the average error of the summary table over one sampling period. Let λ represent the total table error for the j-th time period, T represent the number of sampling periods, m represent the number of time periods contained within a sampling period, and λ represent the total table error for the j-th time period. t This represents the corrected weighting coefficient for the t-th sampling period.

[0077] It is understood that this invention improves the robustness and accuracy of the online monitoring algorithm for total meter error by calculating the total meter error data from multiple sampling periods and then performing stability analysis on the total meter error data from multiple sampling periods, assigning higher weights to sampling periods with good stability.

[0078] Optionally, in step S4, after calculating the total table error value for all time periods within a sampling period, the offset value between the total table error value for each time period and the average error value of the total table within a sampling period can be calculated first. Then, the weight coefficient for each time period is calculated based on the offset value. Finally, the total table error values ​​for 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 period.

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

[0080]

[0081] in, This represents the offset between the total table error value in the j-th time period and the average total table error value within a sampling period.

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

[0083]

[0084] Where, η j Let represent the weighting coefficient for the j-th time period, and D represent a constant.

[0085] Finally, the average error of the summary table over one sampling period is calculated based on the following formula:

[0086]

[0087] in, This represents the average error of the total table within one sampling period, where m represents the number of time periods contained within one sampling period.

[0088] It is understandable that by analyzing the total table error data of multiple time periods within a sampling period, time periods with larger deviations from the mean of the total table error are assigned smaller weight coefficients, while time periods with smaller deviations from the mean of the total table error are assigned larger weight coefficients. This reduces the impact of occasional large deviations in the total table error caused by random factors within a sampling period, and improves the robustness and accuracy of the online monitoring algorithm for the total table error.

[0089] In addition, such as Figure 3 As shown, another embodiment of the present invention also provides an online monitoring system for total meter error based on user meter data, preferably employing the online monitoring method for total meter error as described above. The system includes:

[0090] The data acquisition module is used to collect electricity data from user meters and the main meter for multiple time periods within a sampling period.

[0091] The model building module is used to build a summary table error calculation model based on the law of conservation of energy.

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

[0093] The error calculation module is used to input the measurement error of each user's meter, the line loss of the transformer area, and the electricity data of the user's meter and the main meter for each time period into the main meter error calculation model to calculate the main meter error for each time period.

[0094] As can be understood, the online monitoring system for total meter error based on user meter data in this embodiment first collects electricity data from user meters and the total meter over multiple time periods. Then, based on the law of conservation of energy, it constructs a total meter error measurement model, establishing the relationship between the total meter error and user meter errors, transformer line losses, and the electricity readings of user meters and the total meter during the same time period. The differential evolution algorithm is then used to optimize and solve for the measurement error of each user meter and the transformer line loss, thereby calculating the total meter error for each time period. This enables real-time online monitoring of the total meter error, facilitating real-time calibration and improving the real-time accuracy of electricity metering, thus ensuring the accuracy of electricity trading settlement.

[0095] In addition, another embodiment of the present invention 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 method described above by calling the computer program stored in the memory.

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

[0097] Common computer-readable storage media include: floppy disks, flexible disks, hard disks, magnetic tapes, any other magnetic media, CD-ROMs, any other optical media, punch cards, paper tape, any other physical media with perforated patterns, 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 chips or cartridges, or any other media readable by a computer. Instructions may further be transmitted or received by a transmission medium. The term transmission medium can include any tangible or intangible medium used to store, encode, or carry instructions for machine execution, and includes digital or analog communication signals or intangible media that facilitate communication of such instructions. Transmission media include coaxial cables, copper wires, and optical fibers, which contain conductors for transmitting a bus of computer data signals.

[0098] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take 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.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0099] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0100] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0101] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0102] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0103] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

[0104] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for on-line monitoring of total error of a user's meter based on user's meter data, characterized in that, The method comprises the following steps: Collecting power data of user meters and a total meter in multiple time periods in a sampling period respectively; Constructing a total meter error calculation model based on the law of conservation of energy, wherein the total meter error calculation model is: wherein, represents the measurement error of the total meter at the jth time period, y j represents the power data of the total meter at the jth time period, a i,j represents the power data of the ith user meter at the jth time period, δ i represents the measurement error of the ith user meter, r j represents the feeder line loss at the jth time period, n represents the number of user meters; Optimizing and solving based on the power data of the user meters and the total meter in the multiple time periods in the sampling period by using a differential evolution algorithm to obtain measurement errors of each user meter and line losses of a transformer area in the sampling period; Substituting the measurement errors of each user meter, the line losses of the transformer area, and the power data of the user meters and the total meter in each time period into the total meter error calculation model to calculate total meter errors in each time period in the sampling period; Collecting power data of the user meters and the total meter in multiple sampling periods, and calculating total meter errors in each time period in the multiple sampling periods after repeating the above steps, and performing weighted averaging based on the total meter errors in each time period in the multiple sampling periods to calculate average errors of the total meter in the multiple sampling periods; The process of performing weighted averaging based on the total meter errors in each time period in the multiple sampling periods to calculate the average errors of the total meter in the multiple sampling periods comprises the following steps: First, calculating a standard deviation of the total meter errors in each time period in each sampling period to obtain a total meter error of the sampling period, then calculating a correction weight coefficient of each sampling period according to the standard deviation of the total meter error of each sampling period, and then performing weighted correction on the total meter errors of the multiple sampling periods based on the correction weight coefficients of the multiple sampling periods to obtain the average errors of the total meter in the multiple sampling periods.

2. The method for on-line monitoring of total error of a consumer's meter based on data from the meter according to claim 1, characterized in that, The process of optimizing and solving based on the power data of the user meters and the total meter in the multiple time periods by using the differential evolution algorithm to obtain the measurement errors of each user meter and the line losses of the transformer area comprises the following steps: Initialize the population size, represent each individual in the population as ω i = [δ1, δ2,..., δ n , r j ], δ1, δ2,..., δ n represent the measurement error of all user meters, r j represents the feeder line loss of the jth time period, and it is assumed that the measurement error of all user meters and the feeder line loss remain unchanged within the sampling period; Given a search space of each individual and performing random initialization; For each individual ω i Three different individuals are randomly selected from the population for mutation operation and crossover operation to generate experimental individual μ i ; For the experimental individual μ i and the current individual ω i , the objective function values thereof are calculated respectively, and the individual with the optimal objective function value is selected to enter the next generation; Repeating the mutation, crossover and selection operations to update the population until a convergence condition is reached, and obtaining the measurement errors of each user meter and the line losses of the transformer area based on the optimal individual.

3. The method for on-line monitoring of total error of a consumer's meter based on data from the meter as claimed in claim 1, wherein, The average errors of the total meter in the multiple sampling periods are calculated based on the following formula: wherein, represents the average error of the total table over a plurality of sampling periods, represents the average error of the total table over one 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 one sampling period, λ t represents the correction weight coefficient of the tth sampling period.

4. The method for on-line monitoring of total error of a consumer's meter based on data from the meter of claim 3, wherein, The correction weight coefficient of each sampling period is calculated based on the following formula: where σ t denotes the total table error standard deviation for the tth sampling period, and C denotes a constant.

5. A total error on-line monitoring system based on user meter data, which adopts the total error on-line monitoring method based on user meter data according to any one of claims 1 to 4, characterized in that, The method comprises the following steps: A data collection module is configured to collect power data of user meters and a total meter in multiple time periods in a sampling period respectively; A model construction module is configured to construct a total meter error calculation model based on the law of conservation of energy; An optimization solving module is configured to optimize and solve based on the power data of the user meters and the total meter in the multiple time periods by using a differential evolution algorithm to obtain measurement errors of each user meter and line losses of a transformer area in the sampling period; An error calculation module is configured to substitute the measurement errors of each user meter, the line losses of the transformer area, and the power data of the user meters and the total meter in each time period into the total meter error calculation model to calculate total meter errors in each time period in the sampling period.

6. An electronic device, comprising: The method comprises the following steps: A processor and a memory are provided, and the memory stores a computer program, and the processor is configured to execute the steps of the method according to any one of claims 1-4 by calling the computer program stored in the memory.

7. A computer readable storage medium for storing a computer program for total error on-line monitoring based on user meter data, characterized in that, The computer program, when run on a computer, performs the steps of the method of any of claims 1-4.

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