A high-precision error correction method and system for an electric energy meter

Abstract

This invention discloses a high-precision error correction method and system for electricity meters, relating to the field of power measurement and metering technology. The method includes collecting operating data from three-phase electricity meters and retrieving historical operating data; performing error verification on the three-phase electricity meters by automatically switching between calibration pulses and multi-function pulses to verify and determine whether the error is within a specified range; performing error detection while monitoring the false detection rate, the non-compliance rate of the detected data, and tracing historical errors for automatic short-circuiting, followed by error correction; performing error correction and then re-checking to bring the error back to the allowable range; recording the data from each detection and correction; and establishing a correction database for historical data tracing. This invention achieves efficient and accurate error correction for electricity meters through comprehensive monitoring, precise error detection and classification, automatic short-circuiting and error correction, and big data analysis to establish a correction database, significantly improving the measurement accuracy and reliability of electricity meters.

Description

Technical Field

[0001] This invention relates to the field of power measurement and metering technology, specifically to a high-precision energy meter error correction method and system. Background Technology

[0002] In the field of electricity metering, electricity meters, as key measuring devices, directly affect the normal operation of the power system and the fairness of electricity trading due to their accuracy and stability. With the development of power systems and the improvement of their intelligence levels, the application of high-precision electricity meters is becoming increasingly widespread. However, existing electricity meter error correction technologies still have certain limitations.

[0003] In recent years, although error correction technology for three-phase electricity meters has made some progress, existing technologies generally suffer from the following shortcomings: First, the current technology does not collect and analyze electricity meter operating data comprehensively enough, often ignoring the impact of environmental parameters and load characteristics on electricity meter errors; second, the error verification methods are relatively simple and fail to be flexibly switched according to different working states and external conditions, resulting in inaccurate error verification results; third, during the error correction process, the monitoring and diagnosis of errors are not detailed enough, and the false detection rate is not considered, resulting in poor correction effects; finally, after the current technology performs error correction on the electricity meter, it lacks an effective re-inspection and data traceability mechanism, making it difficult to guarantee the durability and reliability of the correction effect.

[0004] To address the aforementioned issues, our invention proposes a high-precision error correction method for three-phase energy meters, which has significant advantages in improving the accuracy of energy meter error correction and optimizing the correction process. Summary of the Invention

[0005] In view of the existing problems mentioned above, the present invention aims to solve the technical problems and difficulties such as accurately collecting and analyzing electricity meter operating data, automatically switching error verification methods for different operating conditions, performing effective error correction, and optimizing correction strategies using big data analysis. Through this method, high-precision electricity meter error detection and correction are achieved, effectively improving the measurement accuracy and operational stability of electricity meters, and reducing metering losses caused by errors.

[0006] To address the aforementioned technical problems, a high-precision energy meter error correction method is proposed, including:

[0007] Collect operating data from three-phase energy meters and retrieve historical operating data; perform error verification on three-phase energy meters, using automatic switching between calibration pulses and multi-function pulses to verify and determine whether the error is within the specified range; perform error detection, while monitoring the false detection rate and the unqualified rate of the detected data, and trace back historical judgments to automatically short-circuit errors, and perform error correction after short-circuiting; perform error correction and re-inspection after correction to bring the error back to the allowable range, record the data of each detection and correction, and establish a correction database for historical data traceability.

[0008] As a preferred embodiment of the high-precision energy meter error correction method of the present invention, the operating data includes electrical parameters, environmental parameters, load characteristics, power supply parameters and sampling frequency.

[0009] The electrical parameters include current, voltage, power, and power factor. Power includes active power, reactive power, and apparent power.

[0010] The environmental parameters include temperature and humidity;

[0011] The load characteristics include load variation and maximum demand, and load variation includes periodic fluctuations and sudden increases;

[0012] The power supply parameters include grid voltage fluctuations and harmonic content.

[0013] The system retrieves corresponding historical operating data based on the collected operating data and performs static tests on the energy meter under no-load and specified load conditions. It records the difference between the output and input of the operating data and sets specified ranges based on the geographical location and electricity consumption characteristics of the three-phase energy meter: the test voltage value is within ±3%, the leakage current is within ±5%, and the timing time is specified as 60 seconds and cannot exceed ±20 milliseconds.

[0014] As a preferred embodiment of the high-precision energy meter error correction method described in this invention, the error verification includes using a calibration pulse and a multi-function pulse to perform error verification on the three-phase energy meter, with the system automatically switching between the two pulse types.

[0015] When the system is under load and the equipment enters a nonlinear load state, a multi-functional pulse verification is used.

[0016] When the system is not under current load and the temperature is below 20°C, a small signal test is performed using a calibration pulse.

[0017] When the device is detected to simultaneously meet the requirements of maintaining a current conversion rate greater than 2 amps per millisecond and a load conversion rate greater than 15% within 10 milliseconds, a verification pulse and a multi-function pulse are used for testing.

[0018] The system monitors the status of the electricity meter in real time and automatically switches the pulse type when any one of the following conditions is met. During the switch, the system retains a record of the response to the previous pulse. The conditions include:

[0019] Condition 1 is that the current working state switches randomly;

[0020] Condition two is that the current change exceeds ±10%.

[0021] Condition 3 is that the system detects a change in the jump rate of the pulse signal exceeding 0.1 Hz.

[0022] As a preferred embodiment of the high-precision energy meter error correction method of the present invention, the step of determining whether the error is within the specified range includes, in part, the preliminary calculation of the measurement error C. u -C a A comprehensive assessment is conducted to determine whether the error of the three-phase energy meter is within the specified or standard range.

[0023]

[0024] Among them, E t For total error assessment, K is the dynamic adjustment coefficient, and Acc is the total error. i L represents the random error value of each verification data. i As an important weight for the data, C u For the ideal value of the electricity meter, C a The calculated value is obtained from actual measurement, where n is the total amount of data verified, i is the variable index, and D is the calculated value. i E represents the error value of each verification data. th The set error threshold;

[0025] When E t <E th If the measurement is accurate, it is considered to be within the normal range, and maintenance should continue as normal.

[0026] When E th ≤E t <2E th If the error difference trend is judged, it is determined to be within the alarm range, a warning is output, and an error difference trend is evaluated. Based on the trend evaluation result, it is determined whether to convert it into an abnormal range. When the trend evaluation result shows that the overall error difference trend is increasing and there is no downward fluctuation, it is converted into an abnormal range for intervention-type error correction.

[0027] When the trend assessment results show that the error difference trend is decreasing but not increasing overall, error detection is performed, and after determining the cause of the error anomaly, intervention-type error correction is carried out.

[0028] When the trend evaluation results show that the error difference trend fluctuates upward but is decreasing overall, or is decreasing overall without any upward fluctuation, it is considered to be not due to equipment. The system predicts the time it will take for the overall error to decrease to the normal range. If the time does not exceed 5 minutes, the system automatically finds the influencing factors and makes corrections. If the time exceeds 5 minutes, the system performs error detection, analyzes environmental parameters, and performs error correction to assist the automatic correction of the system.

[0029] When E t ≥2E th If the error occurs, it is considered to be outside the normal range and requires immediate intervention for error correction.

[0030] As a preferred embodiment of the high-precision energy meter error correction method of the present invention, the error detection includes detecting error data by means of open circuit detection, and when it is necessary to analyze environmental parameters, two methods are used: open circuit detection and temperature detection.

[0031] The open-circuit detection includes setting an actual voltage V. actual The initial measurement value was recorded, the influence of historical data and the deviation of real-time measurement were traced, and the error was calculated as follows:

[0032]

[0033] Among them, E open M represents the percentage of error during the open-circuit detection phase. open H represents the measured value of the electricity meter under open-circuit conditions, α is a coefficient reflecting the relationship between the measured value and historical data, and H... open Bias correction factor based on historical data;

[0034] Define a new defect rate, when |E open |>E th，1 When |E is considered to have a high non-compliance rate, open |≤E th，1 At that time, it was considered that the current data non-compliance rate was low, of which E th，1 An error threshold is defined to determine whether the error exceeds the acceptable range;

[0035] The method employs both open-circuit detection and temperature detection, including recording the current ambient temperature of the electricity meter and calculating the temperature-related error and the overall error E. total Confirm the overall data non-compliance rate, when |E total |>E th，2 When |E total |≤E th，2 At that time, it was considered that the current data non-compliance rate was low, of which E th，2 The defined error threshold is used to determine whether the total data exceeds the acceptable range;

[0036] Simultaneously, false detection rate diagnosis is performed. For data with high non-compliance rate and low false detection rate, automatic short-circuiting and error correction are performed on the data in each table. Error correction is performed after short-circuiting.

[0037] As a preferred embodiment of the high-precision energy meter error correction method of the present invention, the monitoring of false detection rate includes simultaneously performing false detection rate diagnosis and identifying random errors.

[0038] The false detection rate E is calculated by statistically analyzing the number of electricity meters with high failure rates in historical records. detect , when E detect >E detect_th When E is considered to have a high false positive rate, detect ≤E detect_th At that time, it was considered that there was a low false detection rate, which was due to random error;

[0039] When the data non-compliance rate is high and the false detection rate is high, the system automatically performs short-circuit processing, conducts error correction, and calculates the causal relationship of the increase in error correction for all short-circuited meters:

[0040] E corrected =E total -γD decision

[0041] Among them, E corrected D is the error correction parameter. decision γ is the decision variable, representing whether a false detection is detected; γ is the loss correction factor.

[0042] As a preferred embodiment of the high-precision energy meter error correction method of the present invention, the error correction includes: collecting operating data, including voltage, current, power factor and electricity consumption time information, through three-phase energy meters, and statistically analyzing historical data of each energy meter, including verification results and correction records;

[0043] By leveraging big data analytics, we can obtain the geographical location information of electricity meters and collect electricity consumption characteristic data for various regions. Through data mining techniques, we can extract the operating characteristics of similar electricity meters, identify error distribution patterns, analyze the overall trends and individual differences of similar electricity meters, and identify errors caused by commonalities and individual differences.

[0044] Through cluster analysis, the electricity meters are divided into y groups with similar characteristics. When the operating error of the same type of electricity meter exceeds ±2%, it is marked as a high error meter. Priority group error correction is performed on the high error meters before error correction is performed on the remaining electricity meters.

[0045] When the average error of any type of electricity meter exceeds ±3% of the global standard deviation of its type, cost-optimized individual correction is performed to reduce the error to within ±1% of the population average.

[0046] The behavior model of the electricity meter is evaluated. Based on the regional power grid characteristics, seasonal fluctuations and historical electricity consumption patterns, different correction strategies are set. When the location characteristics of the electricity meter do not match the electricity consumption characteristics, the corresponding correction parameters and correction strategies are called. When the geographical location of the electricity meter matches the location characteristics in the database by more than 85%, only the corresponding correction parameters are adjusted to adapt to the local power grid characteristics.

[0047] When the electricity meter has been in operation for more than 3 years and the power grid frequency fluctuation frequency in the geographical area exceeds 5Hz, the calibration algorithm will be upgraded, a location-related calibration database will be established for historical data tracing, and when the calibration parameters of the electricity meter have been adjusted more than 5 times, historical data analysis will be automatically started to trace the operating status and calibration changes of historical data and provide improvement suggestions. If the historical calibration failure rate exceeds 10%, the system will issue an alarm to the maintenance team.

[0048] Another objective of this invention is to provide a high-precision energy meter error correction system. This invention addresses the error problems that may occur in high-precision energy meters during actual operation. Through real-time data acquisition, accurate error detection and evaluation, and intelligent error correction and optimization, it ensures that the measurement accuracy of the energy meter is always kept within the specified range, thereby improving the accuracy and reliability of energy metering and meeting the power system's requirements for the performance of high-precision energy meters.

[0049] As a preferred embodiment of the high-precision energy meter error correction system of the present invention, it is characterized by comprising an error verification module, an error detection module, and an error correction module;

[0050] The error detection module includes an operating data acquisition unit and a verification unit. The operating data acquisition unit is responsible for collecting real-time operating data of the three-phase energy meter, including electrical parameters, environmental parameters, and load characteristics. The verification unit is responsible for calling historical operating data, analyzing the operating trend and abnormal conditions of the energy meter, and inputting the results into the error detection module.

[0051] The error detection module includes a detection unit and an error evaluation unit. The detection unit receives data from the error verification module and performs error detection, including open circuit detection and temperature detection. The error evaluation unit performs a comprehensive evaluation of the error of the electricity meter based on the error detection results, determines whether the error is within the specified range, and sends the error detection results and evaluation results to the error correction module.

[0052] The error correction module includes a correction unit and a strategy optimization unit. The correction unit performs error correction on the energy meter based on the error assessment results, including automatic short-circuit processing and error correction. The strategy optimization unit adjusts the correction strategy and optimizes the correction effect based on the energy meter's geographical location, electricity consumption characteristics, and operating time information, and feeds it back to the error detection module.

[0053] A computer device includes a memory and a processor, the memory storing a computer program, characterized in that the processor executes the computer program to implement the steps of the high-precision energy meter error correction method.

[0054] A computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the high-precision energy meter error correction method.

[0055] The beneficial effects of this invention are as follows: By collecting operational data from three-phase energy meters and recalling historical operational data, this invention achieves comprehensive monitoring of the energy meter's operating status, providing detailed data support for subsequent error verification and correction, and ensuring the accuracy and comprehensiveness of error analysis. Error verification of the energy meter is performed using automatic switching between calibration pulses and multi-functional pulses, achieving precise error detection and classification, improving the flexibility and accuracy of detection, reducing human intervention, and increasing detection efficiency. Error detection and monitoring of the false detection rate, through automatic short-circuit processing and error correction, effectively identify and correct random errors, improving the reliability of error detection and enhancing the system's automation level. Finally, error correction and re-verification are performed to ensure that the error returns to the allowable range. Simultaneously, data is recorded and a correction database is established. Through big data analysis and cluster analysis, error distribution patterns are identified, providing a scientific basis for the precise operation and maintenance of the energy meter, thereby improving the overall performance and reliability of the energy meter. Attached Figure Description

[0056] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein:

[0057] Figure 1 The above is a flowchart of a high-precision energy meter error correction method provided in one embodiment of the present invention.

[0058] Figure 2 This is a system block diagram of a high-precision energy meter error correction system provided in one embodiment of the present invention. Detailed Implementation

[0059] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0060] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0061] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is mutually exclusive, either alone or selectively, with other embodiments.

[0062] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0063] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0064] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0065] Example 1, referring to Figure 1This is the first embodiment of the present invention, which provides a high-precision energy meter error correction method, including:

[0066] S1: Collect the operating data of the three-phase electricity meter and retrieve historical operating data.

[0067] Furthermore, the operational data includes electrical parameters, environmental parameters, load characteristics, power supply parameters, and sampling frequency;

[0068] The electrical parameters include current, voltage, power, and power factor. Power includes active power, reactive power, and apparent power.

[0069] The environmental parameters include temperature and humidity;

[0070] The load characteristics include load variation and maximum demand, and load variation includes periodic fluctuations and sudden increases;

[0071] The power supply parameters include grid voltage fluctuations and harmonic content.

[0072] The system retrieves corresponding historical operating data based on the collected operating data and performs static tests on the energy meter under no-load and specified load conditions. It records the difference between the output and input of the operating data and sets specified ranges based on the geographical location and electricity consumption characteristics of the three-phase energy meter: the test voltage value is within ±3%, the leakage current is within ±5%, and the timing time is specified as 60 seconds and cannot exceed ±20 milliseconds.

[0073] S2: Perform error verification on the three-phase energy meter, using automatic switching between verification pulse and multi-function pulse to verify and determine whether the error is within the specified range.

[0074] Furthermore, the system uses calibration pulses and multi-function pulses to perform error verification on three-phase energy meters, automatically switching between the two pulse types:

[0075] When the system is under load and the equipment enters a nonlinear load state, a multi-functional pulse verification is used.

[0076] When the system is not under current load and the temperature is below 20°C, a small signal test is performed using a calibration pulse.

[0077] When the device is detected to simultaneously meet the requirements of maintaining a current conversion rate greater than 2 amps per millisecond and a load conversion rate greater than 15% within 10 milliseconds, a verification pulse and a multi-function pulse are used for testing.

[0078] The system monitors the status of the electricity meter in real time and automatically switches the pulse type when any one of the following conditions is met. During the switch, the system retains a record of the response to the previous pulse. The conditions include:

[0079] Condition 1 is that the current working state switches randomly;

[0080] Condition two is that the current change exceeds ±10%.

[0081] Condition 3 is that the system detects a change in the jump rate of the pulse signal exceeding 0.1 Hz.

[0082] It should be noted that the preliminary calculation of the measurement error C u -C a A comprehensive assessment is conducted to determine whether the error of the three-phase energy meter is within the specified or standard range.

[0083]

[0084] Among them, E t For total error assessment, K is the dynamic adjustment coefficient, and Acc is the total error. i L represents the random error value of each verification data. i As an important weight for the data, C u For the ideal value of the electricity meter, C a The calculated value is obtained from actual measurement, where n is the total amount of data verified, i is the variable index, and D is the calculated value. i E represents the error value of each verification data. th The set error threshold;

[0085] When E t <E th If the measurement is accurate, it is considered to be within the normal range, and maintenance should continue as normal.

[0086] When E th ≤E t <2E th If the error difference trend is judged, it is determined to be within the alarm range, a warning is output, and an error difference trend is evaluated. Based on the trend evaluation result, it is determined whether to convert it into an abnormal range. When the trend evaluation result shows that the overall error difference trend is increasing and there is no downward fluctuation, it is converted into an abnormal range for intervention-type error correction.

[0087] When the trend assessment results show that the error difference trend is decreasing but not increasing overall, error detection is performed, and after determining the cause of the error anomaly, intervention-type error correction is carried out.

[0088] When the trend evaluation results show that the error difference trend fluctuates upward but is decreasing overall, or is decreasing overall without any upward fluctuation, it is considered to be not due to equipment. The system predicts the time it will take for the overall error to decrease to the normal range. If the time does not exceed 5 minutes, the system automatically finds the influencing factors and makes corrections. If the time exceeds 5 minutes, the system performs error detection, analyzes environmental parameters, and performs error correction to assist the automatic correction of the system.

[0089] When Et ≥2E th If the error occurs, it is considered to be outside the normal range and requires immediate intervention for error correction.

[0090] S3: Perform error detection, monitor the false detection rate and the non-compliance rate of the detection data, trace back the historical judgment error and automatically short-circuit, and perform error correction after short-circuiting.

[0091] Furthermore, open-loop detection is used to detect error data. When it is necessary to analyze environmental parameters, both open-loop detection and temperature detection are used.

[0092] The open-circuit detection includes setting an actual voltage V. actual The initial measurement value was recorded, the influence of historical data and the deviation of real-time measurement were traced, and the error was calculated as follows:

[0093]

[0094] Among them, E open M represents the percentage of error during the open-circuit detection phase. open H represents the measured value of the electricity meter under open-circuit conditions, α is a coefficient reflecting the relationship between the measured value and historical data, and H... open Bias correction factor based on historical data;

[0095] Define a new defect rate, when |E open |>E th，1 When |E is considered to have a high non-compliance rate, open |≤E th，1 At that time, it was considered that the current data non-compliance rate was low, of which E th，1 An error threshold is defined to determine whether the error exceeds the acceptable range;

[0096] The method employs both open-circuit detection and temperature detection, including recording the current ambient temperature T of the electricity meter. measure Calculate the temperature-related error E temp :

[0097] E temp =T measure ·k temp +E open

[0098] Where, k temp This represents the coefficient indicating the influence of temperature change on the measured value.

[0099] Calculate the overall error:

[0100]

[0101] Confirm the overall data non-compliance rate, when |E total|>E th，2 When |E total |≤E th，2 At that time, it was considered that the current data non-compliance rate was low, of which E th，2 The defined error threshold is used to determine whether the total data exceeds the acceptable range;

[0102] Simultaneously, false detection rate diagnosis is performed. For data with high non-compliance rate and low false detection rate, automatic short-circuiting and error correction are performed on the data in each table. Error correction is performed after short-circuiting.

[0103] It should be noted that false positive rate diagnosis is performed simultaneously to identify random errors;

[0104] The number of electricity meters with high failure rates was analyzed based on historical data:

[0105]

[0106] in, Let x be the overall failure rate of the i-th energy meter, m be the number of energy meters, and x be the failure rate of the i-th energy meter. i The status of the i-th energy meter is either qualified or unqualified.

[0107] Calculate the false positive rate:

[0108]

[0109] Among them, E detect N is the percentage of false positives. total C represents the total number of electricity meters in the historical data. detect To balance the deviation constant caused by false detections, determine the energy meter that will ultimately be short-circuited, when E detect >E detect_th When E is considered to have a high false positive rate, detect ≤E detect_th At that time, it was considered that there was a low false detection rate, which was due to random error;

[0110] When the data non-compliance rate is high and the false detection rate is high, the system automatically performs short-circuit processing, conducts error correction, and calculates the causal relationship of the increase in error correction for all short-circuited meters:

[0111] E corrected =E total -γD decision

[0112] Among them, E corrected To correct the parameters, D decision γ is the decision variable, representing whether an error is determined to exist; γ is the loss correction factor.

[0113] S4: Perform error correction and re-inspection after correction to bring the error back to the allowable range. Record the data of each test and correction and establish a correction database for historical data traceability.

[0114] Furthermore, operational data, including voltage, current, power factor, and electricity usage time information, are collected through three-phase energy meters, and historical data for each energy meter, including verification results and correction records, are statistically analyzed.

[0115] By leveraging big data analytics, we can obtain the geographical location information of electricity meters and collect electricity consumption characteristic data for various regions. Through data mining techniques, we can extract the operating characteristics of similar electricity meters, identify error distribution patterns, analyze the overall trends and individual differences of similar electricity meters, and identify errors caused by commonalities and individual differences.

[0116] Through cluster analysis, the electricity meters are divided into y groups with similar characteristics. When the operating error of the same type of electricity meter exceeds ±2%, it is marked as a high error meter. Priority group error correction is performed on the high error meters before error correction is performed on the remaining electricity meters.

[0117] When the average error of any type of electricity meter exceeds ±3% of the global standard deviation of its type, cost-optimized individual correction is performed to reduce the error to within ±1% of the population average.

[0118] The behavior model of the electricity meter is evaluated. Based on the regional power grid characteristics, seasonal fluctuations and historical electricity consumption patterns, different correction strategies are set. When the location characteristics of the electricity meter do not match the electricity consumption characteristics, the corresponding correction parameters and correction strategies are called. When the geographical location of the electricity meter matches the location characteristics in the database by more than 85%, only the corresponding correction parameters are adjusted to adapt to the local power grid characteristics.

[0119] When the electricity meter has been in operation for more than 3 years and the power grid frequency fluctuation frequency in the geographical area exceeds 5Hz, the calibration algorithm will be upgraded, a location-related calibration database will be established for historical data tracing, and when the calibration parameters of the electricity meter have been adjusted more than 5 times, historical data analysis will be automatically started to trace the operating status and calibration changes of historical data and provide improvement suggestions. If the historical calibration failure rate exceeds 10%, the system will issue an alarm to the maintenance team.

[0120] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0121] Example 2, the second embodiment of the present invention, differs from the previous two embodiments in that:

[0122] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0123] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0124] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0125] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0126] Example 3, referring to Figure 2 This is the third embodiment of the present invention, which provides a high-precision energy meter error correction system, including an error verification module 10, an error detection module 20, and an error correction module 30;

[0127] The error detection module 10 includes an operation data acquisition unit 101 and a verification unit 102. The operation data acquisition unit 101 is responsible for collecting real-time operation data of the three-phase energy meter, including electrical parameters, environmental parameters, and load characteristics. The verification unit 102 is responsible for calling historical operation data, analyzing the operation trend and abnormal conditions of the energy meter, and inputting the results into the error detection module 20.

[0128] The error detection module 20 includes a detection unit 201 and an error evaluation unit 202. The detection unit 201 receives data from the error verification module 10 and performs error detection, including loop open circuit detection and temperature detection. The error evaluation unit 202 performs a comprehensive evaluation of the error of the energy meter based on the error detection results, determines whether the error is within the specified range, and sends the error detection results and evaluation results to the error correction module 30.

[0129] The error correction module 30 includes a correction unit 301 and a strategy optimization unit 302. The correction unit 301 performs error correction on the energy meter based on the error assessment results, including automatic short-circuit processing and error correction. The strategy optimization unit 302 adjusts the correction strategy and optimizes the correction effect based on the energy meter's geographical location, electricity consumption characteristics and operating time information, and feeds it back to the error detection module 20.

[0130] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for correcting errors in a high-precision energy meter, characterized in that: include, Collect operating data from three-phase electricity meters and retrieve historical operating data; The error of the three-phase energy meter is verified by automatically switching between the calibration pulse and the multi-function pulse to verify and determine whether the error is within the specified range. Error detection is performed, and the false detection rate and the non-compliance rate of the detection data are monitored. Historical judgments are traced and errors are automatically short-circuited, and error correction is performed after short-circuiting. Perform error correction and re-inspection after correction to bring the error back to the allowable range. Record the data of each test and correction and establish a correction database for historical data traceability. The error verification includes using a calibration pulse and a multi-function pulse to verify the error of the three-phase energy meter, with the system automatically switching between the two pulse types: When the system is under load and the equipment enters a nonlinear load state, a multi-functional pulse verification is used. When the system is not under current load and the temperature is below 20°C, a small signal test is performed using a calibration pulse. When the device is detected to simultaneously meet the requirements of maintaining a current conversion rate greater than 2 amps per millisecond and a load conversion rate greater than 15% within 10 milliseconds, a verification pulse and a multi-function pulse are used for testing. The system monitors the status of the electricity meter in real time and automatically switches the pulse type when any one of the following conditions is met. During the switch, the system retains a record of the response to the previous pulse. The conditions include: Condition 1 is that the current working state switches randomly; Condition two is that the current change exceeds ±10%. Condition 3 is that the system detects a change in the rate of change of the pulse signal exceeding 0.1 Hz; The monitoring of false detection rate includes simultaneously performing false detection rate diagnosis and identifying random errors; The false alarm rate is calculated by statistically analyzing the number of electricity meters with high failure rates in historical records. ,when At that time, it was considered that there was a high false positive rate. At that time, it was considered that there was a low false detection rate, which was due to random error; When the data non-compliance rate is high and the false detection rate is high, the system automatically performs short-circuit processing, conducts error correction, and calculates the causal relationship of the increase in error correction for all short-circuited meters: wherein, is an error correction parameter, is a decision variable indicating whether a false positive is determined to exist or not; is a loss correction factor.

2. The high-precision electric energy meter error correction method of claim 1, wherein: The operational data includes electrical parameters, environmental parameters, load characteristics, power supply parameters, and sampling frequency; The electrical parameters include current, voltage, power, and power factor. Power includes active power, reactive power, and apparent power. The environmental parameters include temperature and humidity; The load characteristics include load variation and maximum demand, and load variation includes periodic fluctuations and sudden increases; The power supply parameters include grid voltage fluctuations and harmonic content. The system retrieves corresponding historical operating data based on the collected operating data and performs static tests on the energy meter under no-load and specified load conditions. It records the difference between the output and input of the operating data and sets specified ranges based on the geographical location and electricity consumption characteristics of the three-phase energy meter: the test voltage value is within ±3%, the leakage current is within ±5%, and the timing time is specified as 60 seconds and cannot exceed ±20 milliseconds.

3. The method for error correction of a high-precision electric energy meter according to claim 2, characterized in that: The determination of whether the error is within the specified range includes preliminary calculation of the measurement error. A comprehensive assessment is conducted to determine whether the error of the three-phase energy meter is within the specified or standard range. in, For the total error assessment, For dynamic adjustment coefficients, It is the random error value of each verification data. As an important weight for data, This is the ideal value for the electricity meter. The calculated value is obtained from actual measurement, where n is the total amount of data verified, and i is the variable index. The error value for each verification data, The set error threshold; when If the measurement is accurate, it is considered to be within the normal range, and maintenance should continue as normal. when If the error difference trend is judged, it is determined to be within the alarm range, a warning is output, and an error difference trend is evaluated. Based on the trend evaluation result, it is determined whether to convert it into an abnormal range. When the trend evaluation result shows that the overall error difference trend is increasing and there is no downward fluctuation, it is converted into an abnormal range for intervention-type error correction. When the trend assessment results show that the error difference trend is decreasing but not increasing overall, error detection is performed, and after determining the cause of the error anomaly, intervention-type error correction is carried out. When the trend evaluation results show that the error difference trend fluctuates upward but is decreasing overall, or is decreasing overall without any upward fluctuation, it is considered to be not due to equipment. The system predicts the time it will take for the overall error to decrease to the normal range. If the time does not exceed 5 minutes, the system automatically finds the influencing factors and makes corrections. If the time exceeds 5 minutes, the system performs error detection, analyzes environmental parameters, and performs error correction to assist the automatic correction of the system. When then it is judged as an abnormal range, and intervention type error correction needs to be performed immediately.

4. The method for error correction of a high-precision electric energy meter according to claim 3, characterized in that: The error detection includes detecting error data by using open-loop detection. When it is necessary to analyze environmental parameters, two methods are used: open-loop detection and temperature detection. The loop open circuit detection includes setting an actual voltage And record the initial measurement, trace history data impact and real-time measurement deviation, the error is calculated as: in, This represents the percentage of error during the open-circuit detection phase. The measured value of the electricity meter under open circuit conditions. A coefficient that reflects the relationship between measured values ​​and historical data. Bias correction factor based on historical data; Define a new non-conforming rate when At that time, it was considered that the current data non-compliance rate was high. At that time, it was considered that the current data non-compliance rate was low, among which, An error threshold is defined to determine whether the error exceeds the acceptable range. The method employs both open-circuit detection and temperature detection, including recording the current ambient temperature of the electricity meter and calculating temperature-related errors and overall errors. Confirm the overall data non-compliance rate, when At that time, it was considered that the current data showed a high failure rate. At that time, it was considered that the current data non-compliance rate was low, among which, The defined error threshold is used to determine whether the total data exceeds the acceptable range; Simultaneously, false detection rate diagnosis is performed. For data with high non-compliance rate and low false detection rate, automatic short-circuiting and error correction are performed on the data in each table. Error correction is performed after short-circuiting.

5. The high-precision energy meter error correction method as described in claim 4, characterized in that: The error correction includes collecting operating data, including voltage, current, power factor and electricity consumption time information, through three-phase energy meters, and statistically analyzing historical data of each energy meter, including verification results and correction records; By leveraging big data analytics, we can obtain the geographical location information of electricity meters and collect electricity consumption characteristic data for various regions. Through data mining techniques, we can extract the operating characteristics of similar electricity meters, identify error distribution patterns, analyze the overall trends and individual differences of similar electricity meters, and identify errors caused by commonalities and individual differences. Through cluster analysis, the electricity meters are divided into y groups with similar characteristics. When the operating error of the same type of electricity meter exceeds ±2%, it is marked as a high error meter. Priority group error correction is performed on the high error meters before error correction is performed on the remaining electricity meters. When the average error of any type of electricity meter exceeds ±3% of the global standard deviation of its type, cost-optimized individual correction is performed to reduce the error to within ±1% of the population average. The behavior model of the electricity meter is evaluated. Based on the regional power grid characteristics, seasonal fluctuations and historical electricity consumption patterns, different correction strategies are set. When the location characteristics of the electricity meter do not match the electricity consumption characteristics, the corresponding correction parameters and correction strategies are called. When the geographical location of the electricity meter matches the location characteristics in the database by more than 85%, only the corresponding correction parameters are adjusted to adapt to the local power grid characteristics. When the electricity meter has been in operation for more than 3 years and the power grid frequency fluctuation frequency in the geographical area exceeds 5Hz, the calibration algorithm will be upgraded, a location-related calibration database will be established for historical data tracing, and when the calibration parameters of the electricity meter have been adjusted more than 5 times, historical data analysis will be automatically started to trace the operating status and calibration changes of historical data and provide improvement suggestions. If the historical calibration failure rate exceeds 10%, the system will issue an alarm to the maintenance team.

6. A system employing a high-precision energy meter error correction method as described in any one of claims 1 to 5, characterized in that: It includes an error verification module, an error detection module, and an error correction module; The error detection module includes an operating data acquisition unit and a verification unit. The operating data acquisition unit is responsible for collecting real-time operating data of the three-phase energy meter, including electrical parameters, environmental parameters, and load characteristics. The verification unit is responsible for calling historical operating data, analyzing the operating trend and abnormal conditions of the energy meter, and inputting the results into the error detection module. The error detection module includes a detection unit and an error evaluation unit. The detection unit receives data from the error verification module and performs error detection, including open circuit detection and temperature detection. The error evaluation unit performs a comprehensive evaluation of the error of the electricity meter based on the error detection results, determines whether the error is within the specified range, and sends the error detection results and evaluation results to the error correction module. The error correction module includes a correction unit and a strategy optimization unit. The correction unit performs error correction on the energy meter based on the error assessment results, including automatic short-circuit processing and error correction. The strategy optimization unit adjusts the correction strategy and optimizes the correction effect based on the energy meter's geographical location, electricity consumption characteristics, and operating time information, and feeds it back to the error detection module.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the high-precision energy meter error correction method according to any one of claims 1 to 5.

8. A computer readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the high-precision energy meter error correction method according to any one of claims 1 to 5.

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