A method and system for calibrating an electric energy metering device
By conducting multi-temperature point error tests and support vector machine anomaly screening on electricity metering equipment, a general error function was constructed, which solved the problems of high calibration cost and low efficiency of electricity metering equipment, and achieved efficient and accurate error compensation across the entire temperature range.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- WILLFAR INFORMATION TECH CO LTD
- Filing Date
- 2026-03-09
- Publication Date
- 2026-06-26
AI Technical Summary
Existing calibration methods for electricity metering equipment are costly, have low calibration efficiency and accuracy, and are particularly time-consuming and uneconomical in error modeling and compensation across the entire temperature range.
By conducting multi-temperature point error tests on the same batch of electricity metering equipment, the least squares method is used to fit the error curve, and the support vector machine algorithm is combined to identify and remove abnormal samples, a general error function is constructed, and a personalized error curve is derived based on room temperature data to achieve rapid calibration.
It significantly reduces calibration costs and time, improves model stability and measurement accuracy, has a wide range of applications, is suitable for measuring power metering equipment under different temperature environments, and has good engineering application prospects.
Smart Images
Figure CN122283576A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of power Internet of Things technology, and in particular relates to a calibration method and system for power metering equipment. Background Technology
[0002] Electricity metering equipment is crucial for measuring electrical energy consumption and is widely used in power systems. To ensure its measurement accuracy, it must be calibrated regularly. Traditional calibration methods primarily involve operating at room temperature, obtaining error data by comparing with a standard source, and adjusting or correcting the measurement results accordingly. However, with increasing demands for metering accuracy, it has become increasingly recognized that electricity metering equipment exhibits performance differences under varying temperature conditions. For instance, some electronic components drift with temperature changes, leading to variations in the measurement errors of parameters such as voltage, current, and power. Therefore, error modeling and compensation across the entire temperature range has become a key technological direction for improving the overall metering accuracy of electricity metering equipment.
[0003] Current technologies for calibrating electricity metering equipment generally rely on the least squares method to model the temperature characteristics of the metering equipment's errors. The core idea of this method is to perform error tests on each metering device at multiple temperature points, then use the least squares method to fit a curve model of the error changing with temperature, which serves as the basis for error compensation for that metering device. However, each metering device needs to undergo a complete multi-temperature testing process, which is time-consuming and uneconomical. If a meter exhibits abnormal error behavior (such as sudden changes or atypical drift), its fitted curve will interfere with the accuracy of the overall model. For electricity metering devices that have not participated in full-temperature calibration, it is impossible to quickly construct a full-temperature error model based solely on the error at room temperature. Patent CN114578232B provides a battery remaining capacity calculation model, with the following steps: First, obtain the discharge capacity Q of a single battery cell at different temperatures T and discharge rates C. Then, use the least squares method to calculate the Q of the single battery cell as a function of T, C, and the battery life (SOH). Next, convert the experimentally obtained discharge curves of the battery at different temperatures T and C into SOC curves. Use the least squares method to convert these curves into functional relationships. Finally, use the least squares method to express the parameters in the functional relationship as functions of T and C, obtaining the discharge voltage V as a function of T, C, and the current remaining capacity percentage (SOC). Based on the current distribution of T and C, first, integrate to calculate the remaining battery capacity at each C level at the current T and SOC. Then, use the C distribution to calculate the expected remaining capacity. This patent also uses the least squares method, conducting error tests at multiple temperature points, which is costly and has low accuracy, exhibiting the same drawbacks as existing technologies.
[0004] Therefore, how to provide a calibration method for electricity metering equipment that is low in cost, efficient, and accurate is a problem that urgently needs to be solved by those in this technical field. Summary of the Invention
[0005] To address the shortcomings of existing technologies, the purpose of this invention is to provide a calibration method for electricity metering equipment, thereby solving the problems of high calibration costs, low calibration efficiency, and low accuracy in existing technologies. In addition, this invention also provides a calibration system for electricity metering equipment.
[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:
[0007] In a first aspect, the present invention provides a calibration method for an electricity metering device, comprising the following steps:
[0008] S10. Select several power metering devices from the same batch and measure the error data of each power metering device at multiple preset temperature points.
[0009] S20. Use the least squares method to fit the error data of each power metering device to obtain the error curve coefficient array corresponding to each power metering device.
[0010] S30. Identify and remove abnormal individual error curve coefficients from all the error curve coefficients of the electricity metering equipment through a classification algorithm to obtain a set of effective error curve coefficients.
[0011] S40. Perform statistical analysis on all individual error curve coefficients in the set of effective error curve coefficients to generate a set of general error curve coefficients and construct a general error function.
[0012] S50. For the power metering equipment in the same batch that did not participate in the full-temperature calibration, obtain its error data at room temperature, and derive the zero-order coefficient of the power metering equipment based on the general error function, and construct a personalized error curve for calibration.
[0013] Furthermore, in S10, the preset temperature points include -25℃, 0℃, 23℃, 55℃, and 70℃.
[0014] Furthermore, in S10, the error data includes voltage error, current error, and active power error.
[0015] Furthermore, in step S20, the fitting order is 2 to 4, where the quadratic polynomial is as follows:
[0016] error = a0 + a1*T + a2*T².
[0017] Furthermore, in S30, the classification algorithm is a support vector machine algorithm, which uses the error curve coefficients as features input to the support vector machine to identify the error curve coefficients of abnormal individuals.
[0018] Furthermore, in step S40, the average value of the individual error curve coefficients is calculated for each term, and the general error curve coefficients are expressed as follows:
[0019] generalizedCoefficients = [a0_avg, a1_avg, a2_avg, ...]
[0020] The general error function is expressed as follows:
[0021] E(T) = a0_avg + a1_avg*T + a2_avg*T² + ....
[0022] Furthermore, in S50, the ambient temperature is set to 23°C.
[0023] Furthermore, in S50, the personalized error curve coefficients are represented as follows:
[0024] E_new(T) = a0_new + a1_avg*T + a2_avg*T² + ....
[0025] Secondly, the present invention also provides a calibration system for an electricity metering device, comprising:
[0026] A temperature control testing platform is used to simulate different temperature environments and measure the errors of power metering equipment.
[0027] The data acquisition unit is used to collect error data of the power metering equipment at different temperatures;
[0028] Curve fitting processor, used to perform least squares fitting, transforms error data into polynomial curve expressions;
[0029] The anomaly detection module uses a support vector machine algorithm to classify the fitted curves, which is used to identify and remove abnormal samples.
[0030] The comprehensive modeling engine is used to perform statistical analysis on the curve coefficients of the retained samples, calculate the average coefficients, and generate a general error function.
[0031] The prediction and compensation module is used to receive ambient temperature error data from uncalibrated power metering equipment, combine it with a general error function to deduce its personalized error curve, and use it for subsequent error compensation.
[0032] The control center coordinates the workflow of each module and manages data flow and task scheduling.
[0033] Furthermore, the temperature control testing platform includes a constant temperature chamber, a standard source, and test fixtures.
[0034] Compared with the prior art, the calibration method and system for electricity metering equipment provided by this invention have at least the following advantages:
[0035] Existing technologies for calibrating electricity metering equipment suffer from high costs, low efficiency, and low accuracy. This invention effectively addresses the problems of low efficiency, poor model consistency, and weak scalability in existing full-temperature calibration methods for electricity metering equipment by introducing key technologies such as least-squares fitting, support vector machine anomaly screening, and the construction of a universal error model. First, by conducting multi-temperature point error tests on a subset of electricity metering equipment from the same batch and establishing a universal error curve, full-temperature calibration of each device is avoided, significantly reducing calibration costs and time. Second, support vector machines are used to classify and identify the fitted curves, eliminating erroneous samples caused by manufacturing differences or abnormal drift, thus improving the stability and representativeness of the overall model. Furthermore, this invention enables rapid error modeling for electricity metering equipment not involved in full-temperature calibration—its personalized error curve can be derived from only the error at room temperature, greatly enhancing the applicability and flexibility of the method. This mechanism not only improves calibration efficiency but also ensures the measurement accuracy of electricity metering equipment under different temperature environments, demonstrating promising engineering application prospects. In summary, this invention improves the accuracy of electricity metering equipment while taking into account the economy and intelligence of the calibration process, achieving efficient, reliable, and scalable technical results. Attached Figure Description
[0036] To more clearly illustrate the solution of the present invention, a brief introduction will be given to the drawings used in the description of the embodiments below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0037] Figure 1 This is a flowchart of a calibration method for an electrical energy metering device provided in an embodiment of the present invention. Detailed Implementation
[0038] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Preferred embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of the invention.
[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
[0040] This invention provides a calibration method for electricity metering equipment, applied in the periodic calibration process of electricity metering equipment in a power system. The calibration method includes the following steps:
[0041] S10. Select several energy metering devices from the same batch and measure the error data of each energy metering device at multiple preset temperature points; S20. Use the least squares method to fit the error data of each energy metering device to obtain the error curve coefficient array corresponding to each energy metering device; S30. Identify and remove abnormal individual error curve coefficients of all energy metering devices through a classification algorithm to obtain a set of effective error curve coefficients; S40. Perform statistical analysis on all individual error curve coefficients in the set of effective error curve coefficients to generate a set of general error curve coefficients and construct a general error function; S50. For energy metering devices in the same batch that did not participate in full-temperature calibration, obtain their error data at room temperature, and derive the zero-order coefficient of the energy metering device based on the general error function to construct a personalized error curve for calibration.
[0042] This invention effectively reduces calibration costs while ensuring the calibration efficiency and accuracy of power metering equipment.
[0043] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.
[0044] This invention provides a calibration method for electricity metering equipment, applied to the periodic calibration process of electricity metering equipment in power systems. It involves selecting a portion of electricity metering equipment from the same batch for multi-temperature point error testing, establishing a polynomial model of error variation with temperature, using SVM to identify and eliminate abnormal curve samples, and averaging the fitting coefficients of the valid samples to form a universal error curve applicable to the entire batch of electricity metering equipment. For electricity metering equipment not involved in full-temperature calibration, only the error at room temperature point is needed to infer its full-temperature error curve. Figure 1 As shown, in this embodiment, the calibration method for the electricity metering equipment includes the following steps:
[0045] S10. Select several power metering devices from the same batch and measure the error data of each power metering device at multiple preset temperature points.
[0046] Specifically, in this embodiment, in S10, the platform device (constant temperature chamber + standard source) is used to measure the error at multiple preset points, including -25℃, 0℃, 23℃, 55℃, and 70℃.
[0047] Specifically, in this embodiment, in S10, the error data includes voltage error, current error, and active power error, etc.
[0048] S20. Use the least squares method to fit the error data of each power metering device to obtain the error curve coefficient array errorCurveCoefficients[i] corresponding to each power metering device.
[0049] Specifically, in this embodiment, in S20, the fitting order is 2 to 4, where the quadratic polynomial is as follows:
[0050] error = a0 + a1*T + a2*T².
[0051] S30. Identify and remove abnormal individual error curve coefficients from all the error curve coefficients of the electricity metering equipment through a classification algorithm to obtain a set of effective error curve coefficients.
[0052] Specifically, in this embodiment, in S30, the classification algorithm is the Support Vector Machine (SVM Classifier) algorithm, which uses the error curve coefficients as features input to the support vector machine to identify the error curve coefficients of abnormal individuals.
[0053] S40. Perform statistical analysis on all individual error curve coefficients in the effective error curve coefficient set to generate a set of general error curve coefficients and construct a general error function.
[0054] Specifically, in this embodiment, in S40, the average value of the individual error curve coefficients is calculated for each item, and the general error curve coefficients are represented as follows:
[0055] generalizedCoefficients = [a0_avg, a1_avg, a2_avg, ...]
[0056] The general error function is expressed as follows:
[0057] E(T) = a0_avg + a1_avg*T + a2_avg*T² + ....
[0058] S50. For the power metering equipment in the same batch that did not participate in the full-temperature calibration, obtain its error data at room temperature (23℃), and derive the zero-order term coefficient a0_new of the power metering equipment based on the general error function, keep the higher-order terms unchanged, and construct a personalized error curve for calibration.
[0059] Specifically, in this embodiment, in S50, the personalized error curve coefficients are represented as follows:
[0060] E_new(T) = a0_new + a1_avg*T + a2_avg*T² + ....
[0061] In the actual operation of this embodiment, the real-time error estimate is obtained by substituting the current ambient temperature T into the error curve, and the measurement results are dynamically corrected to improve the metering accuracy of the power metering equipment under different temperature conditions.
[0062] This invention also provides a calibration system for electricity metering equipment, comprising:
[0063] The temperature control test platform, including a constant temperature chamber, standard source, test fixtures and other equipment, is used to simulate different temperature environments and measure the error of power metering equipment.
[0064] The data acquisition unit is used to collect error data of the power metering equipment at different temperatures, including parameters such as voltage, current, and power.
[0065] Curve fitting processor, used to perform least squares fitting, transforms error data into polynomial curve expressions;
[0066] The anomaly detection module uses a support vector machine algorithm to classify the fitted curves, which is used to identify and remove abnormal samples.
[0067] The comprehensive modeling engine is used to perform statistical analysis on the curve coefficients of the retained samples, calculate the average coefficients, and generate a general error function.
[0068] The prediction and compensation module is used to receive ambient temperature error data from uncalibrated power metering equipment, combine it with a general error function to deduce its personalized error curve, and use it for subsequent error compensation.
[0069] The control center coordinates the workflow of each module and manages data flow and task scheduling.
[0070] In this embodiment, the data flow relationship between the system modules is as follows: the temperature control test platform provides standard power data to the control center; the data acquisition power supply provides the current measured power value to the control center; the control center provides the error data between the measured value and the standard value to the curve fitting processor; the anomaly detection module provides the filtered error data to the integrated modeling engine; the integrated modeling engine provides the error compensation model to the prediction and compensation module; and the temperature control test platform provides the power data at a specific temperature to the prediction and compensation module.
[0071] Compared with existing technologies, the calibration method and system for electricity metering equipment described in the above embodiments suffer from problems such as high cost, low calibration efficiency, and low accuracy. This invention effectively solves the technical problems of low calibration efficiency, poor model consistency, and weak scalability in existing full-temperature calibration methods for electricity metering equipment by introducing key technologies such as least squares fitting, support vector machine anomaly screening, and the construction of a general error model. First, by conducting multi-temperature point error tests on a subset of energy metering devices from the same batch and establishing a universal error curve, the need for full-temperature calibration of each device is avoided, significantly reducing calibration costs and time. Second, by using a support vector machine to classify and identify the fitted curves, erroneous samples caused by manufacturing differences or abnormal drift are eliminated, improving the stability and representativeness of the overall model. Furthermore, this invention enables rapid error modeling for energy metering devices not calibrated at full temperature—their personalized error curves can be derived from room-temperature errors alone, greatly enhancing the applicability and flexibility of the method. This mechanism not only improves calibration efficiency but also ensures the measurement accuracy of energy metering devices under different temperature environments, demonstrating promising engineering application prospects. In summary, this invention improves the measurement accuracy of energy metering devices while also considering the economy and intelligence of the calibration process, achieving efficient, reliable, and scalable technical results.
[0072] Obviously, the embodiments described above are merely preferred embodiments of the present invention, and not all embodiments. The accompanying drawings illustrate preferred embodiments of the present invention, but do not limit the scope of the patent. The present invention can be implemented in many different forms; rather, these embodiments are provided to provide a more thorough and complete understanding of the disclosure of the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this invention.
Claims
1. A calibration method for an electricity metering device, characterized in that, Includes the following steps: S10. Select several power metering devices from the same batch and measure the error data of each power metering device at multiple preset temperature points. S20. Use the least squares method to fit the error data of each power metering device to obtain the error curve coefficient array corresponding to each power metering device. S30. Identify and remove abnormal individual error curve coefficients from all the error curve coefficients of the electricity metering equipment through a classification algorithm to obtain a set of effective error curve coefficients. S40. Perform statistical analysis on all individual error curve coefficients in the set of effective error curve coefficients to generate a set of general error curve coefficients and construct a general error function. S50. For the power metering equipment in the same batch that did not participate in the full-temperature calibration, obtain its error data at room temperature, and derive the zero-order coefficient of the power metering equipment based on the general error function, and construct a personalized error curve for calibration.
2. The calibration method for an electricity metering device according to claim 1, characterized in that, In S10, the preset temperature points include -25℃, 0℃, 23℃, 55℃, and 70℃.
3. The calibration method for an electricity metering device according to claim 1, characterized in that, In S10, the error data includes voltage error, current error, and active power error.
4. The calibration method for an electricity metering device according to claim 1, characterized in that, In S20, the fitting order is 2 to 4, and the quadratic polynomial is as follows: error = a0 + a1*T + a2*T².
5. The calibration method for an electricity metering device according to claim 1, characterized in that, In step S30, the classification algorithm is a support vector machine algorithm, which uses the error curve coefficients as features input to the support vector machine to identify the error curve coefficients of abnormal individuals.
6. The calibration method for an electricity metering device according to claim 1, characterized in that, In step S40, the average value of the individual error curve coefficients is calculated for each term. The general error curve coefficients are represented as follows: generalizedCoefficients = [a0_avg, a1_avg, a2_avg, ...] The general error function is expressed as follows: E(T) = a0_avg + a1_avg*T + a2_avg*T² + ....
7. The calibration method for an electricity metering device according to claim 1, characterized in that, In S50, the ambient temperature is set to 23°C.
8. The calibration method for an electricity metering device according to claim 1, characterized in that, In S50, the personalized error curve coefficients are represented as follows: E_new(T) = a0_new + a1_avg*T + a2_avg*T² + ....
9. A system employing the method as described in any one of claims 1 to 8, characterized in that, include: A temperature control testing platform is used to simulate different temperature environments and measure the errors of power metering equipment. The data acquisition unit is used to collect error data of the power metering equipment at different temperatures; Curve fitting processor, used to perform least squares fitting, transforms error data into polynomial curve expressions; The anomaly detection module uses a support vector machine algorithm to classify the fitted curves, which is used to identify and remove abnormal samples. The comprehensive modeling engine is used to perform statistical analysis on the curve coefficients of the retained samples, calculate the average coefficients, and generate a general error function. The prediction and compensation module is used to receive ambient temperature error data from uncalibrated power metering equipment, combine it with a general error function to deduce its personalized error curve, and use it for subsequent error compensation. The control center coordinates the workflow of each module and manages data flow and task scheduling.
10. A system according to claim 9, characterized in that, The temperature control testing platform includes a constant temperature chamber, a standard source, and test fixtures.