Electric energy meter on-site calibrator error calculation method based on real-time power grid parameters

Abstract

The invention provides an electric energy meter on-site calibrator error calculation method based on real-time power grid parameters, and relates to the technical field of electric energy metering, and the method comprises the steps: obtaining the type information of a detected electric energy meter, and determining corresponding error model parameters; voltage signals and current signals of a power grid are collected in real time, and power grid parameters such as the voltage total harmonic distortion rate, the voltage fluctuation rate, the frequency offset and the three-phase unbalance degree are calculated; respectively calculating a voltage total harmonic error component, a voltage fluctuation error component, a frequency deviation error component and a three-phase imbalance error component according to the power grid parameters and the error model parameters; summing the error components to obtain a power grid additional error; and subtracting the power grid additional error from the total error calculated by the traditional method to obtain the inherent error of the detected electric energy meter. According to the method, a relation model of power grid parameters and errors is established, separation of inherent errors and power grid additional errors is achieved, the calculation precision is improved by 44 times, it is ensured that the method is suitable for various electric energy meters through a type self-adaption mechanism, and the misjudgment rate is effectively reduced.

Description

Technical Field

[0001] This invention relates to the field of electricity metering technology, and more specifically to a method for calculating the error of an on-site calibrator for electricity meters based on real-time grid parameters. Background Technology

[0002] As the core equipment for electricity metering in a power system, the accuracy of electricity meters directly affects the fairness of electricity billing and the economic efficiency of power grid operation. To ensure that the metering performance of electricity meters meets the requirements, power supply departments need to conduct on-site verification of electricity meters in operation on a regular or irregular basis using electricity meter calibrators.

[0003] Currently, the commonly used error calculation method in field verification of electricity meters is E=(W0-W) / W×100%, where E is the energy error, W0 is the standard energy value, and W is the measured value of the electricity meter under test. This method can meet the verification requirements under ideal power grid conditions, but the power grid parameters in actual field environments are often in a dynamic state, differing significantly from ideal conditions. The error calculation methods specified in JJG596-2012 "Verification Procedure for Electronic AC Energy Meters" and JJG307-2006 "Verification Procedure for Electromechanical AC Energy Meters" also do not consider the impact of power grid parameter changes on the metering characteristics of the electricity meter.

[0004] Existing error calculation methods suffer from the following technical shortcomings: Traditional methods treat electricity meters as ideal linear systems, neglecting the actual impact of grid parameters such as harmonics, voltage fluctuations, frequency offsets, and three-phase imbalances on the metering characteristics. This leads to distorted error calculation results under non-ideal grid conditions. The method cannot distinguish between inherent meter errors and additional errors caused by grid parameters, potentially misdiagnosing grid quality issues as meter malfunctions and causing unnecessary meter replacements. Under grid conditions with a total harmonic distortion (THD) exceeding 5%, the deviation of traditional error calculation methods can reach 0.5% to 1.0%, severely affecting the accuracy of verification results and resulting in a misjudgment rate as high as 25% to 30%. Furthermore, induction meters, electronic meters, and smart meters have different response characteristics to grid parameters, but traditional methods use a uniform calculation model, lacking the ability to adapt to different types of meters. Existing technologies have failed to establish a quantitative relationship model between grid parameters and meter errors, making it impossible to accurately assess the impact of specific factors such as harmonic content and voltage fluctuation amplitude on the error.

[0005] Chinese patent document CN101655545A discloses a field verification method for electricity meters. It discloses a technical solution that synchronizes the two by calculating the difference between the time constant of the calibrator and the electricity meter and adjusting the time constant of the calibrator. This method has the technical effect of reducing the amount of error value variation and making it easier for calibrators to accurately read error data. However, it still has problems such as not considering the impact of changes in power grid parameters on error calculation, not being able to distinguish between the inherent error of the electricity meter and the additional error of the power grid, and lacking a differentiated processing mechanism for different types of electricity meters.

[0006] Chinese patent document CN120870661A discloses a three-phase power metering and detection system for power distribution networks. It discloses a technical solution that designs power calculation formulas for three-phase four-wire and three-phase three-wire systems respectively and adopts a closed-loop process of first correction and second correction for error correction. It has achieved the technical effect of reducing basic metering errors under different wiring scenarios and avoiding residual accumulation. However, it still has problems such as not establishing an explicit quantitative relationship between power grid parameters and error components, using a black box machine learning method that makes the correction process opaque, and not setting differentiated error model parameters for different types of power meters. Summary of the Invention

[0007] The purpose of this invention is to provide an error calculation method for an on-site calibrator of electricity meters based on real-time grid parameters. This method can accurately separate the inherent error of the electricity meter from the additional error of the grid, significantly improve the accuracy of error calculation, and is applicable to various types of electricity meters.

[0008] To achieve the above objectives, the present invention provides the following technical solution: The method for calculating the error of a field calibrator for electricity meters based on real-time grid parameters includes the following steps: S1: Obtain the type information of the tested energy meter, and determine the corresponding error model parameters based on the type information; S2: Real-time acquisition of voltage and current signals of the power grid, and calculation of power grid parameters based on the voltage and current signals. The power grid parameters include total harmonic distortion rate, voltage fluctuation rate, frequency offset, and three-phase imbalance. S3: Calculate the error components corresponding to each power grid parameter based on the power grid parameters and the error model parameters; S4: Sum the error components to obtain the additional error of the power grid; S5: Obtain the total error between the standard energy value and the measured value of the energy meter under test, and subtract the additional error of the power grid from the total error to obtain the inherent error of the energy meter under test.

[0009] Furthermore: the error components include total harmonic error component, voltage fluctuation error component, frequency offset error component, and three-phase imbalance error component; The formula for calculating the total harmonic error component of the voltage is: E h =-k h ×THD×(1-cos²φ); Among them, E h k represents the total harmonic error component of the voltage. h Here, φ is the harmonic influence factor, φ is the power factor angle, and THD is the total harmonic distortion of voltage. The formula for the total harmonic distortion of voltage is: ,in, Let be the effective value of the h-th harmonic voltage, where h = 2, 3, ..., H, and H is the highest harmonic order.

[0010] Furthermore: the formula for calculating the voltage fluctuation error component is: E v =k v1 ×ΔU+k v2 ×(ΔU)², Among them, E v For the voltage fluctuation error component, k v1 k is the primary influence coefficient of voltage. v2 Let ΔU be the voltage second-order influence coefficient, and ΔU be the voltage fluctuation rate, which is calculated using the formula ΔU = ×100%, of which To measure the maximum voltage, To measure the minimum voltage, U n This is the rated voltage.

[0011] Furthermore: the formula for calculating the frequency offset error component is: E f =k f ×Δf, Among them, E f For the frequency offset error component, k f Here, Δf is the frequency influence coefficient, and Δf is the frequency offset, which is calculated using the formula Δf = ,in The phase deviation of the current sampling period. This represents the phase deviation from the previous sampling period. The sampling frequency.

[0012] Furthermore: the calculation formula for the three-phase unbalance error component is: E b =k b ×ε², where E b k represents the three-phase imbalance error component. b The unbalance influence coefficient is ε, and the three-phase voltage unbalance degree is given by the formula ε = U2 / U1 × 100%, where U1 is the effective value of the positive sequence voltage component and U2 is the effective value of the negative sequence voltage component.

[0013] Furthermore: the types of the energy meters include induction energy meters, electronic energy meters, and smart energy meters; When the energy meter is an induction energy meter, k h The value ranges from 0.10 to 0.14, k v1 The value ranges from 0.04 to 0.06, k v2 The value ranges from 0.002 to 0.004, k f The value ranges from 0.18 to 0.22, k b The value ranges from 0.08 to 0.12; When the energy meter is an electronic energy meter, k h The value ranges from 0.02 to 0.04, k v1 The value ranges from 0.01 to 0.03, k v2 The value ranges from 0.0005 to 0.0015, k f The value ranges from 0.04 to 0.06, k b The value ranges from 0.05 to 0.09; When the energy meter is a smart energy meter, k h The value ranges from 0.04 to 0.06, k v1 The value ranges from 0.02 to 0.04, k v2 The value ranges from 0.001 to 0.003, k f The value ranges from 0.06 to 0.10, k b The value ranges from 0.06 to 0.10.

[0014] Further: The methods for obtaining the type information of the tested energy meter in S1 include: reading the model information of the tested energy meter through the communication interface, determining the energy meter type based on the characteristic characters in the model information, analyzing the response characteristics of the tested energy meter under standard conditions to determine the energy meter type, and determining the energy meter type based on user input.

[0015] Furthermore, in S2, the total harmonic distortion rate of voltage is calculated using an improved fast Fourier transform algorithm, which includes windowing and interpolation processing, and the calculation range is from the 2nd to the 50th harmonics.

[0016] Furthermore: In S2, the frequency offset is calculated using a three-point phase-locked loop algorithm, based on the formula f=f n +(θ k -θ k-1 )×f s Calculate the actual frequency using (2π). Where, θ k Let θ be the phase angle at time k. k-1 Let f be the phase angle at time k-1. s The sampling frequency.

[0017] Furthermore, S5 also includes boundary checks on the calculated inherent errors, specifically: When the inherent error is less than the preset lower limit, it is set to the preset lower limit; when the inherent error is greater than the preset upper limit, it is set to the preset upper limit.

[0018] Compared with the prior art, the present invention has the following beneficial effects: I. This invention establishes for the first time a quantitative relationship model between power grid parameters and electricity meter errors, decomposing the total electricity meter error into two parts: inherent error and power grid-added error, achieving precise separation between the two. By subtracting the added errors caused by power grid parameters such as harmonics, voltage fluctuations, frequency offsets, and three-phase imbalances from the total error, the inherent error reflecting the meter's own metering characteristics can be accurately obtained, avoiding misjudging power grid quality problems as electricity meter malfunctions. Actual measurement data shows that in a typical industrial environment with a THD of 6.5% and voltage fluctuation of 4.2%, the inherent error calculated by this method deviates from the actual value by only 0.007%, while the traditional method deviates by 0.31%, improving calculation accuracy by 44 times.

[0019] Second, this invention sets differentiated error model parameters for three types of energy meters: induction, electronic, and smart. It automatically selects the corresponding influence coefficient for error calculation through an energy meter type adaptive mechanism. Since different types of energy meters have significantly different response characteristics to grid parameters, using uniform parameters would lead to distorted calculation results. This invention solves this problem through type identification and parameter matching, ensuring that the method is applicable to the field verification of various types of energy meters.

[0020] Third, this invention effectively reduces the misjudgment rate of on-site verification of electricity meters, avoids unnecessary replacement of electricity meters due to the influence of power grid parameters, and can improve the first-pass rate of on-site verification by more than 35%, which has significant economic benefits and practical value. Attached Figure Description

[0021] Figure 1 The present invention provides a flowchart of a method for calculating the error of an on-site power meter calibrator based on real-time power grid parameters. Detailed Implementation

[0022] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] It should be noted that this invention proposes to decompose the total error of the electricity meter into inherent error and additional error caused by grid parameters, specifically as follows: E total =E inherent +E grid ; in: E total This represents the total error calculated using traditional methods. E inherent The inherent error of the electricity meter (the objective to be solved); E grid Additional errors caused by power grid parameters; Further details: E grid =E h +E v +E f +E b +E o ; E h Error components caused by harmonics; E v Error component caused by voltage fluctuations; E f Error components caused by frequency offset; E b Error components caused by three-phase imbalance; E o Error components caused by other factors.

[0024] It should be noted that the total error calculated by the traditional method mentioned in this invention is specifically the total error between the standard energy value and the measured value of the tested energy meter, which can be expressed by formula E. total =(W0-W) / W×100% is calculated, where W0 is the standard energy value and W is the measured value of the energy meter under test.

[0025] Example 1 like Figure 1 As shown, this invention provides a method for calculating the error of an on-site energy meter calibrator based on real-time grid parameters, comprising the following steps: S1: Obtain the type information of the tested energy meter and determine the corresponding error model parameters based on the type information; S2: Real-time acquisition of voltage and current signals from the power grid, and calculation of power grid parameters based on the voltage and current signals. Power grid parameters include total harmonic distortion rate, voltage fluctuation rate, frequency offset, and three-phase imbalance. S3: Calculate the error components corresponding to each power grid parameter based on the power grid parameters and error model parameters; S4: Sum the error components to obtain the additional error of the power grid; S5: Obtain the total error between the standard energy value and the measured value of the energy meter under test, and subtract the additional error of the power grid from the total error to obtain the inherent error of the energy meter under test.

[0026] The core idea of ​​this invention is to decompose the total error of an electricity meter into inherent error and additional error caused by grid parameters, i.e., E total =E inherent +E grid By establishing a quantitative relationship between power grid parameters and error components, the additional error from the power grid is subtracted from the total error, thus obtaining the inherent error that reflects the metering characteristics of the electricity meter itself, and avoiding interference from changes in power grid parameters on the verification results.

[0027] In one specific embodiment of this example, the error components include the total harmonic distortion (THD) error component, the voltage fluctuation error component, the frequency offset error component, and the three-phase imbalance error component. The formula for calculating the THD error component is E. h =-k h ×THD×(1-cos²φ), Among them, E h k represents the total harmonic error component of the voltage. h Here, THD is the total harmonic distortion factor, and φ is the power factor angle. The negative sign in the formula indicates that induction energy meters typically under-meter. (1-cos²φ), or sin²φ, represents the proportion of reactive power. Harmonic effects are more significant when the power factor is low. The formula for the total harmonic distortion factor is as follows: ,in, Let be the effective value of the h-th harmonic voltage, where h = 2, 3, ..., H, and H is the highest harmonic order.

[0028] In one specific embodiment of this example, the formula for calculating the voltage fluctuation error component is E. v =k v1 ×ΔU+k v2 ×(ΔU)², Where k v1 k is the primary influence coefficient of voltage. v2 Here, ΔU is the voltage second-order influence coefficient, and ΔU is the voltage fluctuation rate, which is calculated using the formula ΔU = ×100%, of which To measure the maximum voltage, To measure the minimum voltage, U n This is the rated voltage. The linear term reflects the basic operating characteristics of the energy meter in response to voltage changes, while the quadratic term reflects nonlinear effects.

[0029] In one specific embodiment of this example, the formula for calculating the frequency offset error component is E. f =k f ×Δf, where k f Here, Δf is the frequency influence coefficient, and Δf is the frequency offset. The frequency offset Δf = ,in The phase deviation of the current sampling period. This represents the phase deviation from the previous sampling period. The sampling frequency.

[0030] In one specific embodiment of this example, the formula for calculating the three-phase unbalance error component is E. b =k b ×ε², where k b Let ε be the unbalance influence coefficient, and ε be the three-phase voltage unbalance degree. The formula for the three-phase voltage unbalance degree is ε = U2 / U1 × 100%, where U1 is the effective value of the positive-sequence voltage component and U2 is the effective value of the negative-sequence voltage component. A quadratic model is used to reflect the nonlinear effect of the unbalance degree on the error.

[0031] In one specific embodiment of this example, the types of electricity meters include induction electricity meters, electronic electricity meters, and smart electricity meters.

[0032] When the electricity meter is an induction-type electricity meter, k h The value ranges from 0.10 to 0.14, k v1 The value ranges from 0.04 to 0.06, k v2 The value ranges from 0.002 to 0.004, k f The value ranges from 0.18 to 0.22, k b The value ranges from 0.08 to 0.12.

[0033] When the electricity meter is an electronic electricity meter, k h The value ranges from 0.02 to 0.04, k v1 The value ranges from 0.01 to 0.03, k v2 The value ranges from 0.0005 to 0.0015, k f The value ranges from 0.04 to 0.06, k b The value ranges from 0.05 to 0.09.

[0034] When the electricity meter is a smart electricity meter, k h The value ranges from 0.04 to 0.06, k v1 The value ranges from 0.02 to 0.04, k v2 The value ranges from 0.001 to 0.003, k f The value ranges from 0.06 to 0.10, k bThe value ranges from 0.06 to 0.10. Different parameters are used for different types of electricity meters to ensure that the method is applicable to all types of electricity meters.

[0035] In one specific embodiment of this example, the method for obtaining the type information of the tested energy meter in S1 includes: reading the model information of the tested energy meter through a communication interface, determining the energy meter type based on the characteristic characters in the model information; analyzing the response characteristics of the tested energy meter under standard conditions to determine the energy meter type; and determining the energy meter type based on user input. Specifically, if the model contains DD862 or DSZ, it is determined to be an inductive type; if it contains DDSY or DSSD, it is determined to be an electronic type; and if it contains DJ or Smart, it is determined to be a smart type.

[0036] In one specific implementation of this embodiment, the total harmonic distortion rate of voltage in S2 is calculated using an improved fast Fourier transform algorithm. The improved fast Fourier transform algorithm includes windowing processing and interpolation processing, and the calculation range is from the 2nd to the 50th harmonics.

[0037] In one specific implementation of this embodiment, the frequency offset in S2 is calculated using a three-point phase-locked loop algorithm, according to the formula f=f n +(θ k -θ k-1 )×f s Calculate the actual frequency using (2π), where θ k Let θ be the phase angle at time k. k- 1 represents the phase angle at time k-1, f s The sampling frequency.

[0038] In one specific embodiment of this example, S5 further includes performing a boundary check on the calculated inherent error. When the inherent error is less than a preset lower limit, it is set as the preset lower limit; when the inherent error is greater than a preset upper limit, it is set as the preset upper limit. The preset lower limit can be set to -10.0%, and the preset upper limit can be set to 10.0%, ensuring that the output result is within a reasonable range.

[0039] Example 2 The following provides a specific implementation example of the software algorithm.

[0040] The formulas used for calculating power grid parameters are as follows: Harmonic analysis: Improved FFT algorithm (windowing + interpolation) to calculate 2nd to 50th harmonics; Voltage fluctuation rate: ΔU=(UU) n ) / U n ×100%; Frequency offset: A three-point phase-locked loop (3PLL) algorithm is used, f=f n +(θ k θk-1 )×f s / (2π); Three-phase unbalance: ε = U2 / U1 × 100%; The process of electricity meter type identification is as follows: First, the electricity meter type is initialized to an unknown state. Then, it is determined whether a usable communication interface exists. If the communication interface is available, the system attempts to read the electricity meter's model information string. After successful reading, feature matching is performed on the model string: if the model string contains "DD862" or "DSZ", the electricity meter type is determined to be induction; if the model string contains "DDSY" or "DSSD", the electricity meter type is determined to be electronic; if the model string contains "DJ" or "Smart", the electricity meter type is determined to be smart. When the electricity meter type cannot be identified through the communication interface, the system defaults to setting the electricity meter type to electronic and finally returns the identified electricity meter type.

[0041] The calculation process for the total harmonic distortion (THD) error component of voltage is as follows: Calculations are performed based on the input energy meter type, THD distortion rate, and power factor. First, the harmonic influence coefficient k is determined according to the energy meter type. h Value of k: k corresponds to induction energy meter h The value is 0.12, corresponding to k for electronic energy meters. h The value is 0.03, corresponding to k for smart energy meters. h The default value is 0.05; other types are assumed to be 0.05 by default. Then, the total harmonic error component of the voltage is calculated according to the formula, i.e., the negative k... h Multiply by the total harmonic distortion of the voltage and then by (1 minus the square of the power factor) to get the calculation result.

[0042] The calculation process for voltage fluctuation error components is as follows: Calculations are performed based on the input energy meter type and voltage fluctuation rate. First, the values ​​of two voltage influence coefficients are determined according to the energy meter type: for induction energy meters, k corresponds to... v1 0.05, k v2 The value is 0.003, corresponding to k for electronic energy meters. v1 For 0.02, k v2 The value is 0.001, corresponding to k in smart energy meters. v1 0.03, k v2 The default value is 0.002; for other types, the default value is k. v1 0.03, k v2 The value is 0.002. Then, the voltage fluctuation error component, k, is calculated according to the formula. v1 Multiply by voltage fluctuation rate plus k v2 Multiply by the square of the voltage fluctuation rate and return the calculation result.

[0043] The calculation process for the frequency offset error component is as follows: Calculation is performed based on the input energy meter type and frequency offset. First, the frequency influence coefficient k is determined according to the energy meter type. f Value of k: k corresponds to induction energy meter f The value is 0.20, corresponding to k for electronic energy meters. f The value is 0.05, corresponding to k for smart energy meters. f The default value is 0.08; other types default to 0.08. Then, it returns k. f The result of multiplying by the frequency offset.

[0044] The calculation process for the three-phase unbalance error components is as follows: The calculation is performed based on the input energy meter type and the three-phase unbalance degree. First, the unbalance influence coefficient k is determined according to the energy meter type. b Value of k: k corresponds to induction energy meter b The value is 0.10, corresponding to k for electronic energy meters. b The value is 0.07, corresponding to k for smart energy meters. b The default value is 0.08; other types default to 0.08. Then, it returns k. b The result is calculated by multiplying by the square of the imbalance.

[0045] The process of calculating inherent error is as follows: The system receives the electricity meter type, grid parameters, and total error as input. First, it calls the calculation functions for the four error components mentioned above. It calculates the total harmonic distortion (THD) error component based on the THD and power factor in the grid parameters; the voltage fluctuation error component based on the voltage fluctuation rate; the frequency offset error component based on the frequency offset; and the three-phase imbalance error component based on the three-phase imbalance. Then, it adds the four error components to obtain the total additional error. Next, it subtracts the total additional error from the total error to obtain the inherent error of the electricity meter. Finally, it performs a boundary check: if the inherent error is less than -10.0%, it is set to -10.0%; if it is greater than 10.0%, it is set to 10.0%. After ensuring the result is within a reasonable range, it returns the inherent error value.

[0046] Example 3 The following is a complete verification example. Test conditions are: electronic energy meter (0.5S class), THD = 6.5%, voltage fluctuation = +4.2%, frequency deviation = +0.15Hz, three-phase imbalance = 1.8%, power factor = 0.95, and the total error E measured by the traditional method is... total =+0.45%.

[0047] The calculation process for the error components is as follows: Total harmonic error component E of voltage h =-0.03×6.5×(1-0.95²)=-0.0189%; Voltage fluctuation error component E v=0.02×4.2+0.001×(4.2)²=0.1016%; Frequency offset error component E f =0.05×0.15=0.0075%; Three-phase unbalance error component E b =0.07×(1.8)²=0.2268%. Total additional error E grid =E h +E v +E f +E b =0.3170%.

[0048] The result of the inherent error calculation is: E inherent =0.45%-0.3170%=+0.133%.

[0049] Verification results: Recalibrating the meter under ideal grid conditions using a high-precision standard source (0.01 level), the actual error was measured to be +0.14%, which deviates from the inherent error (+0.133%) calculated by this method by only 0.007%. Traditional methods directly use the total error of 0.45% as the meter error, resulting in a deviation of 0.31% from the actual value. This method improves the accuracy of error calculation by 44 times, effectively avoiding meter misjudgment.

[0050] The above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They should not be construed as limiting the scope of protection of the present invention. All equivalent transformations or modifications made in accordance with the spirit and essence of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for calculating the error of an on-site energy meter calibrator based on real-time power grid parameters, characterized in that, Includes the following steps: S1: Obtain the type information of the tested energy meter, and determine the corresponding error model parameters based on the type information; S2: Real-time acquisition of voltage and current signals of the power grid, and calculation of power grid parameters based on the voltage and current signals. The power grid parameters include total harmonic distortion rate, voltage fluctuation rate, frequency offset, and three-phase imbalance. S3: Calculate the error components corresponding to each power grid parameter based on the power grid parameters and the error model parameters; S4: Sum the error components to obtain the additional error of the power grid; S5: Obtain the total error between the standard energy value and the measured value of the energy meter under test, and subtract the additional error of the power grid from the total error to obtain the inherent error of the energy meter under test.

2. The method for calculating the error of a field calibrator for a power meter based on real-time grid parameters according to claim 1, characterized in that: The error components include total harmonic error component, voltage fluctuation error component, frequency offset error component and three-phase imbalance error component; The formula for calculating the total harmonic error component of the voltage is: E h =-k h ×THD×(1-cos²φ); Among them, E h k represents the total harmonic error component of the voltage. h Here, φ is the harmonic influence factor, φ is the power factor angle, and THD is the total harmonic distortion of voltage. The formula for the total harmonic distortion of voltage is: ,in, Let be the effective value of the h-th harmonic voltage, where h = 2, 3, ..., H, and H is the highest harmonic order.

3. The method for calculating the error of a field calibrator for a power meter based on real-time grid parameters according to claim 2, characterized in that: The formula for calculating the voltage fluctuation error component is: E v =k v1 ×ΔU+k v2 ×(ΔU)², Among them, E v For the voltage fluctuation error component, k v1 k is the primary influence coefficient of voltage. v2 Let ΔU be the voltage second-order influence coefficient, and ΔU be the voltage fluctuation rate. The formula for the voltage fluctuation rate is: ΔU= ×100%, of which To measure the maximum voltage, To measure the minimum voltage, U n This is the rated voltage.

4. The method for calculating the error of a field calibrator for a power meter based on real-time grid parameters according to claim 2, characterized in that: The formula for calculating the frequency offset error component is: E f =k f ×Δf, Among them, E f For the frequency offset error component, k f Here, Δf is the frequency influence coefficient, and Δf is the frequency offset, which is calculated using the formula Δf = ,in The phase deviation of the current sampling period. This represents the phase deviation from the previous sampling period. The sampling frequency.

5. The method for calculating the error of a field calibrator for a power meter based on real-time grid parameters according to claim 2, characterized in that: The formula for calculating the three-phase unbalance error component is: E b =k b ×ε², where E b k represents the three-phase imbalance error component. b The unbalance influence coefficient is ε, and the three-phase voltage unbalance degree is given by the formula ε = U2 / U1 × 100%, where U1 is the effective value of the positive sequence voltage component and U2 is the effective value of the negative sequence voltage component.

6. The method for calculating the error of a field calibrator for energy meters based on real-time grid parameters according to any one of claims 2-5, characterized in that: The types of electricity meters include induction electricity meters, electronic electricity meters, and smart electricity meters; When the energy meter is an induction energy meter, k h The value ranges from 0.10 to 0.14, k v1 The value ranges from 0.04 to 0.06, k v2 The value ranges from 0.002 to 0.004, k f The value ranges from 0.18 to 0.22, k b The value ranges from 0.08 to 0.12; When the energy meter is an electronic energy meter, k h The value ranges from 0.02 to 0.04, k v1 The value ranges from 0.01 to 0.03, k v2 The value ranges from 0.0005 to 0.0015, k f The value ranges from 0.04 to 0.06, k b The value ranges from 0.05 to 0.09; When the energy meter is a smart energy meter, k h The value ranges from 0.04 to 0.06, k v1 The value ranges from 0.02 to 0.04, k v2 The value ranges from 0.001 to 0.003, k f The value ranges from 0.06 to 0.10, k b The value ranges from 0.06 to 0.

10.

7. The method for calculating the error of a field calibrator for a power meter based on real-time grid parameters according to claim 1, characterized in that: The methods for obtaining the type information of the tested energy meter in S1 include: reading the model information of the tested energy meter through the communication interface, determining the energy meter type based on the characteristic characters in the model information, analyzing the response characteristics of the tested energy meter under standard conditions to determine the energy meter type, and determining the energy meter type based on user input.

8. The method for calculating the error of a field calibrator for a power meter based on real-time grid parameters according to claim 1, characterized in that: In S2, the total harmonic distortion rate of voltage is calculated using an improved fast Fourier transform algorithm, which includes windowing and interpolation processing, and the calculation range is from the 2nd to the 50th harmonics.

9. The method for calculating the error of a field calibrator for a power meter based on real-time grid parameters according to claim 1, characterized in that: In S2, the frequency offset is calculated using a three-point phase-locked loop algorithm, based on the formula f=f n +(θ k -θ k-1 )×f s Calculate the actual frequency using (2π). Where, θ k Let θ be the phase angle at time k. k-1 Let f be the phase angle at time k-1. s The sampling frequency.

10. The method for calculating the error of a field calibrator for a power meter based on real-time grid parameters according to claim 1, characterized in that: S5 also includes boundary checks on the calculated inherent errors, specifically: When the inherent error is less than the preset lower limit, it is set to the preset lower limit; when the inherent error is greater than the preset upper limit, it is set to the preset upper limit.

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