Method and device for evaluating measurement uncertainty of automatic verification line of electric energy meter

Abstract

The invention discloses a method and a device for evaluating the measurement uncertainty of an automatic verification line of an electric energy meter, and the method comprises the steps: analyzing each uncertainty source according to the structure of the to-be-evaluated automatic verification line of the electric energy meter and the environment condition of the to-be-evaluated automatic verification line of the electric energy meter, so as to determine each uncertainty component for evaluation; the same installation type standard electric energy meter is adopted to carry out a measurement experiment, data measured by the installation type standard electric energy meter is used as a reference value, and a relative error between the data measured by the installation type standard electric energy meter and data displayed by a working standard meter is used as a basic error of the automatic verification line of the electric energy meter. Calculating the value of each uncertainty component; taking the root mean square of the value of each uncertainty component as the synthesis standard uncertainty of the electric energy meter automatic verification line; and multiplying the synthesis standard uncertainty by a set confidence factor to obtain an evaluation result of the expansion uncertainty of the automatic verification line of the electric energy meter. According to the method, the measurement uncertainty can be evaluated more comprehensively and accurately.

Description

Technical Field

[0001] The invention relates to the technical field of electric energy metering, and in particular to a method and a device for evaluating the measurement uncertainty of an automatic verification line of an electric energy meter. Background Art

[0002] Electricity metering is related to the national economy and people's livelihood, and the accuracy and fairness of metering are related to the rational allocation of public resources. Promoting the use of automated calibration lines and intelligent storage systems to avoid the impact of human and regional factors on calibration quality can steadily improve the quality and efficiency of calibration and distribution work and the level of metering management capabilities.

[0003] The automatic calibration line of electric energy meters is the main carrier for realizing the transmission of electric energy meter values. Its accuracy and reliability are the key to ensuring the calibration quality of electric energy meters. Compared with the traditional calibration system, the automatic calibration line involves multiple functional modules, and factors such as ambient temperature and humidity will affect the calibration platform. Therefore, it is of great significance to study the overall measurement uncertainty evaluation method of the automatic calibration line of electric energy meters.

[0004] At present, the uncertainty assessment technology for the automatic calibration line of electric energy meters is usually a static uncertainty assessment method; for example: a common method is to use the GUM method to assess the measurement uncertainty of the automatic calibration line of electric energy meters, but this method only assesses Class A and Class B measurement uncertainty, and the assessment result is restricted by the sample capacity, and only pays attention to the current sample information, ignoring relevant historical data and information. In the study of the document “Analysis of the influence of temperature reference conditions on the automated calibration line of smart energy meters”, only the influence of the reference temperature of the automated calibration line of energy meters on the energy metering error of smart energy meters was considered, and the influence of the reference temperature of the automated calibration line of energy meters on the calibration device of energy meters was not considered; in the study of the document “Analysis and evaluation of measurement uncertainty of the automated calibration system of electric energy meters in Hebei Southern Power Grid”, multiple ordinary low-accuracy smart energy meters were used for experiments, and errors existed between different smart energy meters; and the energy value of the working standard meter was used as the reference value, and the relative error value output by the calibration device of the energy meter was used as the basic error for uncertainty assessment. The assessment object of this method is the smart energy meter rather than the automated calibration line of the energy meter. However, for the automatic calibration line of electric energy meters, since the internal parameters of the system are time-varying, such as the output standard voltage, standard current, frequency, etc., repeatability errors will be introduced; and its operating environment is unstable, such as temperature, humidity, electromagnetic interference signals, etc.; the existing methods for static analysis of uncertainty obviously cannot meet the accuracy requirements. Therefore, there is an urgent need for an uncertainty assessment scheme that can take dynamic effects into account. Summary of the invention

[0005] In order to solve the deficiencies in the prior art, the present invention provides a method and device for evaluating the measurement uncertainty of an automatic calibration line of an electric energy meter. By updating the uncertainty components according to the latest measurement data of different working conditions, the method and device have better adaptability to the time-varying state of the system inside the assembly line and the unstable external environment, and can more comprehensively and accurately evaluate the measurement uncertainty of the automatic calibration line of the electric energy meter.

[0006] The present invention adopts the following technical solution.

[0007] In a first aspect, the present invention provides a method for evaluating the measurement uncertainty of an electric energy meter automatic verification line, the method comprising:

[0008] Step 1: Analyze the uncertainty sources according to the structure of the automatic verification line of the electric energy meter to be evaluated and the environment in which it is located, so as to determine the uncertainty components used for evaluation;

[0009] Step 2: Use the same installed standard electric energy meter to carry out measurement experiments, and use the data measured by the installed standard electric energy meter as the reference value. The relative error between the data measured by the installed standard electric energy meter and the data displayed by the working standard meter is used as the basic error of the electric energy meter automatic verification line to calculate the value of each uncertainty component.

[0010] Step 3: taking the root mean square of the values ​​of each uncertainty component as the synthetic standard uncertainty of the electric energy meter automatic verification line;

[0011] Step 4: multiply the synthetic standard uncertainty by the set confidence factor to obtain the evaluation result of the expanded uncertainty of the electric energy meter automatic calibration line.

[0012] Optionally, in step 1, the uncertainty components include:

[0013] The dynamic uncertainty component u(r f ), the standard uncertainty component u(r T ), the standard uncertainty component u(r ρ ), the standard uncertainty component u(r b ), the standard uncertainty component u(r d ), the standard uncertainty component u(r R ) and / or the standard uncertainty component u(r B ).

[0014] Optionally, in step 2, the basic error r of the electric energy meter automatic verification line is calculated as follows:

[0015]

[0016] In the formula, E b and E0 respectively represent the electric energy value of the working standard meter and the electric energy value of the installed standard electric energy meter collected under the same conditions.

[0017] Optionally, in step 2, the calculation of the values ​​of each uncertainty component includes: calculating the dynamic uncertainty component u(r) introduced by the repeatable experiment in combination with Bayesian theory f ), the process includes:

[0018] Step a: randomly select a calibration device, use the same installed standard electric energy meter to conduct m tests on all the table positions E on the calibration device, and repeat the basic error n times in each experiment; where E is the total number of table positions on the selected calibration device, and m and n are the set number of experiments and repeated measurements, respectively;

[0019] Step b: For any epitope j, j = 1, 2, ... E, repeat the first test n times to obtain the basic error set R f(1,j) As historical data, the mean and variance of the basic error set are used as the prior distribution mean and the prior distribution standard deviation respectively, so as to obtain the first prior normal distribution;

[0020] Step c: Combine the basic error set obtained by repeating the second test n times with the first prior distribution to obtain the second posterior distribution. Similarly, the posterior distribution obtained from the current experiment is used as the prior distribution of the next test to continue to participate in the update and iteration of the distribution parameters to match the dynamic changes of the automatic verification line of the electric energy meter. Finally, the posterior distribution of the mth test of the table position j is obtained.

[0021] Step d: Perform m tests on E electric energy meters according to the process of steps b and c, and record the updated posterior distribution variance of each meter after m tests to obtain the posterior distribution variance set D E ;

[0022] Step e: From the posterior distribution variance set D E Filter out the largest posterior distribution variance value represents the variance of the posterior distribution of the pth electric energy meter updated after m tests, thereby calculating the dynamic uncertainty component u(r f ) is

[0023] Optionally, in step c, the update iteration of the distribution parameters includes the update iteration of the posterior distribution mean and the posterior distribution variance, and the update formulas thereof are as follows:

[0024]

[0025] In the formula, μ f(y+1,j) and μ f(y,j) are the means of the posterior distributions of epitope j updated in the y+1th and yth trials, respectively; σ 2 f(y+1,j) and σ 2 f(y,j) are the variances of the posterior distribution of epitope j updated in the y+1th and yth trials, respectively; is the arithmetic mean of the basic error set measured in the y+1th experiment for epitope j; σ 2 r(y+1,j) The variance of the basic error set measured for epitope j in the y+1th experiment.

[0026] Optionally, in step 2, calculating the values ​​of each uncertainty component includes: calculating the standard uncertainty component u(r) introduced by the ambient temperature by combining the basic error weighted arithmetic T ), the process includes:

[0027] Starting from the lowest temperature T1, with the set temperature change ΔT as a level change unit, the entire electric energy meter automatic verification line is divided into A areas S1, S2...S with different temperature levels. A ;

[0028] Randomly select an electric energy meter calibration device from the same temperature level area, and randomly select a set number q from the electric energy meter calibration device T The installed standard electric energy meter is connected to each meter position by an automatic wiring mechanism and experimental tests are carried out respectively to determine the temperature basic error corresponding to each meter position, so as to obtain the temperature basic error value set corresponding to each temperature grade area;

[0029] Calculate the arithmetic mean of the basic temperature error value set of each temperature level area respectively, and then calculate the weighted average value of the temperature error of all areas;

[0030] The standard deviation of the weighted mean of the temperature error is taken as the standard uncertainty component u(r T ) value.

[0031] Optionally, the standard uncertainty component u(r T ) is expressed as follows:

[0032]

[0033] In the formula, σ T Represents the weighted average of temperature error The standard deviation, p i represents the temperature weight of the ith temperature level area, Represents the arithmetic mean of the temperature basic error value set for the i-th temperature level area.

[0034] Optionally, in step 2, the calculation of the values ​​of each uncertainty component includes: calculating the standard uncertainty component u(r) introduced by the ambient humidity by combining the basic error weighted arithmetic ρ ), the process includes:

[0035] Taking the lowest absolute humidity ρ1 as the starting point and the set humidity change Δρ% as a level change unit, the entire electric energy meter automatic verification line is divided into C areas S1, S2...S with different absolute humidity levels. C ;

[0036] Randomly select one electric energy meter calibration device from the same absolute humidity level area, and randomly select a set number q from the electric energy meter calibration device ρ The installed standard electric energy meter is connected to each meter position by an automatic wiring mechanism and experimental tests are carried out respectively to determine the basic error corresponding to each meter position, so as to obtain the humidity basic error value set corresponding to each absolute humidity level area;

[0037] Calculate the arithmetic mean of the humidity basic error value set of each absolute humidity level area respectively, and then calculate the weighted average of the humidity errors of all areas;

[0038] The standard deviation of the weighted mean value of the humidity error is taken as the standard uncertainty component u(r ρ ) value.

[0039] Optionally, the standard uncertainty component u(r ρ ) is expressed as follows:

[0040]

[0041] In the formula, σ ρ Represents the weighted average of humidity error The standard deviation, p i represents the humidity weight of the i-th absolute humidity level area, i = 1, 2…C; Represents the arithmetic mean of the humidity basic error value set for the i-th absolute humidity level area.

[0042] Optionally, in step 2, calculating the values ​​of each uncertainty component includes: calculating the standard uncertainty component u(r) introduced by the working standard table by the following formula: b ):

[0043]

[0044] Where k represents the grade of the working standard meter used in the automatic calibration line of electric energy meters.

[0045] Optionally, in step 2, calculating the values ​​of each uncertainty component includes: calculating the standard uncertainty component u(r d ), the process is as follows:

[0046] Randomly select the set number p d A set number q is randomly selected from each electric energy meter calibration device. d The installed standard electric energy meter is connected to each meter position by an automatic wiring mechanism and experimental tests are carried out respectively to determine the basic error corresponding to each meter position;

[0047] Calculate the maximum value of basic error δ between different epitopes in each device d(i)max The minimum value of basic error δ d(i)min The difference between d(i) =δ d(i)max -δ d(i)min , take p d The maximum value of the error difference in the device δ dmax , according to the rectangular distribution Calculate and obtain the standard uncertainty component u(r) introduced by the consistency of multi-channel output d ) can be:

[0048] Optionally, in step 2, calculating the values ​​of each uncertainty component includes: calculating the standard uncertainty component u(r R ), the process is as follows:

[0049] Extract a set number q from a randomly selected electric energy meter calibration device B The installed standard electric energy meter is connected to each meter position by automatic wiring mechanism and experimental detection is carried out to determine the basic error corresponding to each meter position, and the maximum value r is selected from each basic error. Lmax and the minimum value r Lmin ;

[0050] The installed standard electric energy meter is directly connected to the voltage circuit and current circuit of the selected electric energy meter calibration device by manual wiring for experimental detection, and the basic error r is obtained.O ;

[0051] Take the maximum value of the difference between the basic error measured in the automatic wiring case and the basic error measured in the manual wiring case δ Rmax =max{|r Lmax -r O |,|r O -r Lmin |}, distributed according to rectangle The standard uncertainty component u(r) introduced by the common impedance of the voltage loop and the current loop is calculated and obtained. R ) can be:

[0052] Optionally, in step 2, calculating the values ​​of each uncertainty component includes: calculating the standard uncertainty component u(r B ), the process is as follows:

[0053] A meter position is selected from a randomly selected electric energy meter calibration device. The installed standard electric energy meter is connected to the meter position by an automatic wiring mechanism under normal working conditions and when the mobile communication is turned off for experimental testing. The basic error r of the meter position under normal working conditions is obtained. Bm and the basic error r when mobile communication is turned off B0 ;

[0054] Calculate the variation δ B =r Bm -r B0 , and distributed in a rectangular manner Calculate and obtain the standard uncertainty component u(r) introduced by the electromagnetic interference signal B ) is

[0055] Optionally, in step 3, the expression of the combined standard uncertainty is as follows:

[0056]

[0057] In the formula, u c is the combined standard uncertainty, u(r f ) is the dynamic uncertainty component introduced by the repeatability experiment, u(r d ) is the uncertainty component introduced into the standard by the consistency of multi-channel output, u(r R ) is the standard uncertainty component introduced by the common impedance of the voltage loop and the current loop, u(r T ) is the standard uncertainty component introduced by the ambient temperature, u(r b ) is the standard uncertainty component introduced by the working standard table, u(rρ ) is the uncertainty component introduced by the ambient humidity, u(r B ) is the standard uncertainty component introduced by the electromagnetic interference signal.

[0058] Optionally, in step 4, the expression of the expanded uncertainty is as follows:

[0059] U=Ku c

[0060] Where U is the expanded uncertainty and K is the set confidence factor.

[0061] In a second aspect, the present invention provides a device for evaluating the measurement uncertainty of an electric energy meter automatic verification line, and executes the steps of any method described in the first aspect of the present invention, the device comprising:

[0062] The uncertainty component determination module is used to analyze various uncertainty sources according to the structure of the automatic verification line of the electric energy meter to be evaluated and the environmental conditions in which it is located, so as to determine various uncertainty components for evaluation;

[0063] A basic error calculation module is used to use the same installed standard electric energy meter to carry out measurement experiments, and use the data measured by the installed standard electric energy meter as a reference value, and use the relative error between the data measured by the installed standard electric energy meter and the data displayed by the working standard meter as the basic error of the electric energy meter automatic verification line to calculate the value of each uncertainty component;

[0064] A synthetic standard uncertainty calculation module, used for taking the root mean square of the values ​​of each uncertainty component as the synthetic standard uncertainty of the electric energy meter automatic verification line;

[0065] The expanded uncertainty calculation module is used to multiply the synthetic standard uncertainty by a set confidence factor to obtain an evaluation result of the expanded uncertainty of the electric energy meter automatic verification line.

[0066] In a third aspect, the present invention provides a terminal, including a processor and a storage medium;

[0067] The storage medium is used to store instructions;

[0068] The processor is used to operate according to the instructions to execute the steps of any one of the methods described in the first aspect of the present invention.

[0069] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods described in the first aspect of the present invention.

[0070] The beneficial effects of the present invention are as follows:

[0071] 1. The present invention considers the sources of uncertainty of the automatic calibration line of the electric energy meter more comprehensively and accurately. It is not limited to the classification standards of Class A uncertainty and Class B uncertainty in the traditional GUM method, but classifies the environmental impact quantities more finely, regards different environmental impact quantities as separate influencing individuals, and performs weighted processing on the basic errors measured within a certain range of environmental impact quantities, so as to more specifically study the influence of different environmental quantities on the automatic calibration line of the electric energy meter within a certain range of variation; in addition, the present invention is not limited to the static data of a single measurement, and does not simply divide the uncertainty range according to the measurement limit value, but combines historical data with newly measured samples, takes into account the changes in time and working conditions, and thus evaluates a more real-time and accurate measurement uncertainty.

[0072] 2. The present invention adopts the relative error between the same installed standard electric energy meter and the working standard meter as the basic error, eliminating the error introduced by the different electric energy meters used in the test, and further assessing the uncertainty of the electric energy meter automatic calibration line itself; the daily operation of the electric energy meter automatic calibration line can be monitored and evaluated based on the assessment results of the present invention, and abnormal operation can be discovered in time.

[0073] 3. The present invention takes into account the dynamic changes of system internal parameters and external environmental influences, uses the Bayesian rule to dynamically process the data obtained from repeatable measurements, combines the real-time measured sample information with the prior distribution to obtain the posterior distribution of the system state, and makes full use of the real-time dynamic information of the measurement system to obtain the repeatability uncertainty component caused by the time-varying internal parameters; adopts the control variable method to establish a mathematical relationship between uncertainty and temperature and humidity, and can calculate the corresponding uncertainty value according to the dynamic changes of the actual environment; finally, the total dynamic uncertainty is synthesized by combining its own structure with the remaining static uncertainty components to realize the analysis of the dynamic measurement uncertainty of the automatic calibration line of the electric energy meter; the present invention can more finely analyze the uncertainty source of the automatic calibration line of the electric energy meter, and can use real-time data to evaluate the uncertainty more accurately and in real time, effectively improving the accuracy of the measurement uncertainty evaluation, and can monitor the daily operation of the automatic calibration line of the electric energy meter in a more real-time and targeted manner;

[0074] 4. In summary, the present invention uses Bayesian theory combined with the latest test data to update the dynamic uncertainty introduced by repeatability in real time; for environmental quantity changes, the standard uncertainty introduced is weighted by comprehensively calculating the basic errors under all environmental impact values; compared with traditional static uncertainty assessment methods, the present invention is based on dynamic data and is more suitable for the time-varying state inside the assembly line system and the unstable external environment. It can more comprehensively and accurately assess the uncertainty of the measurement of the automatic calibration line of the electric energy meter, and provides a scientific basis for the dynamic uncertainty assessment of the automatic calibration line system of the electric energy meter in the current electric energy metering field. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 The present invention is a flowchart of a method for evaluating the measurement uncertainty of an electric energy meter automated verification line. DETAILED DESCRIPTION

[0076] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in the present invention are only part of the embodiments of the present invention, not all of the embodiments. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0077] Embodiment 1:

[0078] Reference Figure 1 The embodiment of the present invention provides a method for evaluating the measurement uncertainty of an electric energy meter automatic calibration line, which specifically includes the following steps:

[0079] Step 1: Analyze the uncertainty sources according to the structure of the automatic verification line of the electric energy meter to be evaluated and the environment in which it is located, so as to determine the uncertainty components used for evaluation;

[0080] In this embodiment, the automatic calibration line of the electric energy meter to be evaluated has time-varying internal parameters such as the system output standard voltage, standard current, frequency, etc., so these parameters will cause repeatability errors and introduce repeatability test uncertainty; because the external environmental parameters of the system, such as temperature, humidity, etc., will change over time, these environmental parameters will cause corresponding errors and introduce corresponding uncertainties.

[0081] Furthermore, combined with the structure of the electric energy meter automatic verification line itself, its uncertainty sources include the dynamic uncertainty component u(r f ), the standard uncertainty component u(r T ), the standard uncertainty component u(r ρ ), the standard uncertainty component u(r b ), the standard uncertainty component u(r d ), the standard uncertainty component u(r R ) and the standard uncertainty component u(r B ).

[0082] Step 2: Use the same installed standard electric energy meter to carry out measurement experiments, and use the data measured by the installed standard electric energy meter as the reference value. The relative error between the data measured by the installed standard electric energy meter and the data displayed by the working standard meter is used as the basic error of the electric energy meter automatic verification line to calculate the value of each uncertainty component.

[0083] For the automatic verification line of electric energy meters, the basic error r is the relative error between the device under test and the installed standard electric energy meter. The electric energy value of the working standard meter collected under the same conditions is E b , the electric energy value of the installed standard electric energy meter is E0, and the relative error value of electric energy measurement is calculated, that is, the basic error r of the electric energy meter automatic verification line, as shown in formula (1):

[0084]

[0085] In the formula, E b and E0 respectively represent the electric energy value of the working standard meter and the electric energy value of the installed standard electric energy meter collected under the same conditions.

[0086] As an embodiment of the present invention, in step 2, calculating the values ​​of each uncertainty component includes:

[0087] (I) Dynamic uncertainty component u(r) introduced by repeatability experiment f ):

[0088] Since the internal parameters of the electric energy meter automatic calibration line system are time-varying, when the external environmental conditions do not change, repeatable dynamic errors will be introduced, affecting the accuracy of the measurement results. Therefore, this embodiment obtains the distribution law of repeatable experimental errors based on Bayesian theory and experimental data, and obtains updated distribution parameters in combination with real-time data to participate in the synthesis of the total dynamic uncertainty of the system. The process includes:

[0089] Step a: randomly select a calibration device, use the same installed standard electric energy meter to conduct m tests on all the table positions E on the calibration device, and repeat the basic error n times in each experiment; where E is the total number of table positions on the selected calibration device, and m and n are the set number of experiments and repeated measurements, respectively;

[0090] Step b: For any epitope j, j = 1, 2, ... E, repeat the first test n times to obtain the basic error set R f(1,j) As historical data, the mean and variance of the basic error set are used as the prior distribution mean and the prior distribution standard deviation respectively, so as to obtain the first prior normal distribution;

[0091] Step c: Combine the basic error set obtained by repeating the second test n times with the first prior distribution to obtain the second posterior distribution. Similarly, the posterior distribution obtained from the current experiment is used as the prior distribution of the next test to continue to participate in the update and iteration of the distribution parameters to match the dynamic changes of the automatic verification line of the electric energy meter. Finally, the posterior distribution of the mth test of the table position j is obtained.

[0092] Step d: Perform m tests on E electric energy meters according to the process of steps b and c, and record the updated posterior distribution variance of each meter after m tests to obtain the posterior distribution variance set D E ;

[0093] Step e: From the posterior distribution variance set D E The largest posterior distribution variance value σ is selected 2 f(m,p) , σ 2 f(m,E) represents the variance of the posterior distribution of the pth electric energy meter updated after m tests, thereby calculating the dynamic uncertainty component u(r f ) is

[0094] Furthermore, in this embodiment, for calculating the dynamic uncertainty component u(r f ) value is as follows: there are E1 meter positions on each electric energy meter calibration device. Under the condition that other conditions remain unchanged, the automatic connection and disconnection mechanism of the electric energy meter automatic calibration line is used to connect the installed standard electric energy meter to the E1 meter positions respectively. When the power factor is 1.0 and 0.5 (L), m experiments are carried out at various current values. Each experiment is repeated n times and the basic error data of the repeatability test is recorded. f(y,i,j) , where y represents the y-th experiment, y=1,2,…m, i represents the ith repetition, i=1,2,…n, and j represents the epitope number, j=1,2…E1.

[0095] The basic error set obtained by performing the first test on the jth epitope and repeating it n times is According to Bayesian theory, it is used as historical data to obtain the prior distribution mean μ of the basic error measured on the jth epitope f(1,j) and the standard deviation σ of the prior distribution f(1,j) ,in, μ f(y,j) and σ f(y,j) Where y represents the yth experiment, y = 1, 2, ... m, i represents the i-th repeated test, i = 1, 2, ... n, j represents the epitope number, j = 1, 2 ... E. μ f(1,j) and σ f(1,j)The calculation formula is as follows.

[0096]

[0097] According to experience and measured data, the basic error set R of the repeatability test f(1,j) Follows normal distribution N(μ f(1,j) ,σ 2 f(1,j) ), and use this as the prior distribution. Then the basic error set obtained by the second test n times is With the prior distribution N(μ f(1,j) ,σ 2 f(1,j )) Combined with the posterior distribution N(μ f(2,j) ,σ 2 f(2,j) ). The posterior distribution obtained this time will continue to participate in the update iteration as the prior distribution of the next test to match the dynamic changes of the automatic verification line of the electric energy meter.

[0098] Taking the jth table as an example, the method of combining the prior distribution function with the newly measured basic error set to obtain the posterior distribution according to Bayesian theory is:

[0099] It is known that its prior distribution follows N(μ f(y,j) ,σ 2 f(y,j) ), mean μ f The prior density function of is:

[0100]

[0101] The newly measured basic error set is R f(y+1,j) = {r f(y+1,1,j) ,r f(y+1,2,j) ...r f(y+1,n,j)}, applying Bayesian conjugate distribution theory, the mean μ f The posterior distribution of is also a normal distribution, and μ f The prior density function and likelihood function of The posterior distribution is obtained to follow N(μ f(y+1,j) ,σ 2 f(y+1,j) ), where p(μ f ) is the prior density function, is the likelihood function, is the posterior density function.

[0102] The likelihood function calculation process is as follows:

[0103] In the formula, is the arithmetic mean of the newly measured basic error set, σ 2r(y+1,j) is the variance of the newly measured basic error set, and the calculation formula is as follows:

[0104]

[0105] The likelihood function is:

[0106]

[0107] μ f The prior density function and likelihood function of The posterior distribution is obtained to follow N(μ f(y+1,j) ,σ 2 f(y+1,j) ), the mean of the posterior distribution μ f(y+1,j) and the variance of the posterior distribution σ 2 f(y+1,j) The calculation is as follows:

[0108]

[0109] Similarly, the distribution mean and distribution variance are updated multiple times using the newly measured basic error data. Follow the distribution In the formula,

[0110]

[0111] Then the updated posterior distribution follows N(μ f(m,j) ,σ 2 f(m,j) ), the mean of the posterior distribution μ f(m,j) and the variance of the posterior distribution σ 2 f(m,j) for:

[0112]

[0113] The distribution parameters of the repeatability test are continuously updated using the newly measured basic errors, and the uncertainty is assessed by making statistical judgments on the basic error data based on the posterior distribution.

[0114] According to the above process, m tests are conducted on E1 electric energy meters, the corresponding basic error data are recorded and the updated posterior variance is calculated. The updated posterior variance set is Select variance set The maximum value σ in 2 f(m,p) , p means that the posterior variance value obtained at the pth epitope is the largest; because the estimated value of the measured value is obtained by the average value of n measurements, the standard uncertainty u(r f )for

[0115] (II) Standard uncertainty component u(r) introduced by multi-channel output consistency d ):

[0116] There are multiple calibration devices in the automatic calibration line of electric energy meters. Each device has E meter positions. The inconsistency between the meter positions on each device will introduce standard uncertainty. d Each device selects q from E meter positions. d Connect the installed standard electric energy meter to q d When the power factor is 1.0 and 0.5 (L), respectively, the basic error value of the output of the jth meter position of the i-th device is r d(i,j) , i=1,2,…p d , j = 1, 2, ... q d .

[0117] Calculate the maximum value of basic error δ between different epitopes in the same device d(i)max The minimum value of basic error δ d(i)min The difference between d(i) =δ d(i)max -δ d(i)min , take p d The maximum value of the error difference in the device δ dmax , according to the rectangular distribution but:

[0118]

[0119] (III) The standard uncertainty component u(r) introduced by ambient temperature T ):

[0120] The automated calibration line for electric energy meters covers a large area, and multiple facilities are operated simultaneously during calibration. As the temperature control facilities age and wear out over the years of operation, it is difficult for the ambient temperature to stabilize within the initial standard requirements. In addition, the ambient temperature of the calibration devices for electric energy meters varies greatly due to their different distribution areas. For example, the calibration device at the interface of the electric energy meter storage warehouse is lower in temperature than other areas due to ventilation with the warehouse at a fixed temperature, while the calibration device at the center of the area is the highest in temperature because it is surrounded by other operating devices and has no vents.

[0121] For the energy meter calibration device, one of the main environmental parameters that affects its uncertainty is temperature. The ambient temperature will affect the stability of the reference voltage output by the voltage reference source and will affect the gain error of the ADC. By controlling the ambient temperature experimentally, the curve of the reference voltage of the ADC in the energy meter calibration device changing with temperature is obtained. By fitting the curve with the least squares method, the relationship between the reference voltage and temperature is obtained as follows:

[0122] V ref (T) = a0V T +a1V T T+a2 T T 2 (15)

[0123] Where V T is the voltage equivalent of temperature, V ref (T) is the reference voltage, a0, a1, and a2 are the parameters of the fitted function. The temperature when the curve has a temperature coefficient close to zero is taken as the reference temperature, recorded as T1.

[0124] The relationship between the ideal transfer function of ADC, taking the current channel as an example, the relationship between the input discrete time voltage and the output digital quantity can be expressed as:

[0125]

[0126] Where U i (t n ) is the discrete time analog voltage; D i (n) is the output digital quantity, D i (n)=(2 N-1 a N +2 N-2 a N-1 +...+2 1 a2+a1); D N =2 N is the maximum output code of ADC; N is the number of bits of ADC;

[0127] If there is a gain error, equation (16) will be expressed as:

[0128]

[0129] Where U i (t n ) is the analog signal after current homogenization, k is the gain error factor, when k = 1 there is no gain error, when the temperature changes k ≠ 1, gain error will occur.

[0130] Considering the ADC quantization error, the A / D converter output voltage signal code D i (n) is expressed as:

[0131]

[0132] In the formula, INT is the rounding function, D i (n) is the natural binary code of the ADC output.

[0133] Similarly, the output code D of the voltage signal can be obtained v (n) expression.

[0134] Substituting the expression (15) of the voltage reference source changing with temperature into the expression (18), we can get the expression of the input and output relationship of the voltage channel of the ADC affected by temperature, which is:

[0135]

[0136] Similarly, the relationship between the input and output of the ADC current channel affected by temperature can be expressed as:

[0137]

[0138] According to the principle of power and electric energy measurement, the voltage and current digital signal D v (n), D i (n) Perform digital dot product multiplication and cumulative sum to obtain the electric energy value measured by the electric energy meter. The measurement algorithm of the active electric energy value can be expressed as:

[0139]

[0140] Where m is the number of sampling points; T S is the sampling cycle time; D v (n) and D i (n) are the output codes of ADC signals, and the other parameters have the same meanings as before.

[0141] The working environment temperature affects the basic error of the automatic calibration line of the electric energy meter by affecting the accumulated electric energy value of the working standard meter in the automatic calibration device of the electric energy meter.

[0142] The electric energy value measured by the working standard meter at the same temperature is E T , the electric energy value measured by the installed standard meter is E0, then the relative error of electric energy measurement including temperature influence is r T as follows:

[0143]

[0144] Furthermore, in order to analyze the distribution characteristics of the error of the automatic verification line of the electric energy meter at different temperatures, starting from the lowest temperature T1, with ΔT℃ as a level change unit, the entire verification line is divided into A areas with different temperature levels S1, S2...S A , the corresponding ambient temperature of the area is T1, T2...T A In the same temperature level area, a device is randomly selected and q T The installed standard electric energy meter is connected with the automatic wiring mechanism for detection and calculation of the basic error rT (T i,j ), i represents the ith temperature value, i = 1, 2…A, j represents the jth epitope under the temperature value, j = 1, 2…q T . represents the q obtained under the i-th ambient temperature value T The basic temperature error value.

[0145] Start the test from the lowest temperature level area S1 of the electric energy meter automatic verification line and obtain the arithmetic mean of the basic error value. Then, the same test is carried out on the S2 area with the temperature increased by one level and the average value of the basic temperature error is recorded. Gradually increase the temperature level until the actual maximum ambient temperature T A And record the average value of basic temperature error The data obtained are shown in Table 1. Among them, the temperature arithmetic mean value of the temperature basic error value is The calculation formula is as follows:

[0146]

[0147] Using the arithmetic mean of temperature The weighted average temperature of all regions is calculated using the data. i and the temperature-weighted average The calculation of is shown in formula (24) and formula (25):

[0148] p1:p2:...:p i :...:p A =T1:T2:...:T i :...:T A (twenty four)

[0149]

[0150] The standard deviation of the temperature-weighted average is calculated as the standard uncertainty introduced by the ambient temperature, as shown in equations (26) and (27):

[0151]

[0152] The standard uncertainty component u(r T ) is σ T .

[0153] Table 1 Ambient temperature values ​​and corresponding basic error values

[0154]

[0155] (IV) Standard uncertainty component u(r) introduced by ambient humidity ρ):

[0156] During the calibration of the automatic calibration line of electric energy meters, multiple facilities are operated at the same time. As the operation time increases and the temperature changes, it is difficult for the humidity control facilities to stabilize the ambient humidity within the initial standard requirements. In addition, the ambient humidity in different distribution areas also varies greatly, and the humidity is difficult to maintain constant. The ambient humidity is an important factor causing the measurement error of the calibration system: when the air humidity is high, water vapor molecules will enter the calibration device. If a large amount of them are adsorbed on the surface of the internal resistance components, the impedance will be reduced; if a large amount of them are adsorbed on the surface of the transformer, the insulation performance of the surface will be reduced. When the automatic calibration line of electric energy meters works in a high-temperature and dry environment for a long time, static electricity will be generated on the surface of the internal electronic components, affecting the normal operation of the assembly line. The following is an experimental analysis of the impact of humidity on the automatic calibration line of electric energy meters.

[0157] The humidity usually measured is relative humidity, and the calculation formula is:

[0158]

[0159] Among them, H represents relative humidity, the unit is %RH; E1 is the water vapor pressure in the air, the unit is hPa; E2 is the saturated water vapor pressure of a pure horizontal ice surface, the unit is hPa.

[0160] The relationship between ambient temperature and relative humidity is given in Appendix A (Normative Appendix) of the national standard GB / T 17215.211-2006. It can be seen that relative humidity is greatly affected by temperature. Absolute humidity refers to the actual value of the weight of water vapor contained in a unit volume of air, so relative humidity is converted into absolute humidity to study its impact on the basic error of the automatic verification line of electric energy meters.

[0161] The conversion formula between relative humidity and absolute humidity is as follows:

[0162]

[0163] T=273.15+θ (31)

[0164] Where: ρ is absolute humidity, unit is g / m 3 ; H is relative humidity; E is saturated water vapor pressure; T is temperature, the unit is absolute temperature scale K; θ is Celsius temperature, the unit is ℃.

[0165] The distribution characteristics of the automatic verification line of electric energy meters under different absolute humidity are analyzed. The lowest absolute humidity ρ1 is taken as the starting point, and Δρ% is taken as a level change unit. The entire verification line is divided into C areas S1, S2...S with different absolute humidity levels. C , the corresponding environmental absolute humidity of the area is ρ1, ρ2...ρ CIn the same absolute humidity level area, randomly select a device and randomly select q from the E positions of the device. ρ Connect the installed standard electric energy meter to the meter position, calculate and record the basic error r ρ (ρ i,j ), i represents the i-th absolute humidity value, i = 1, 2…C, j represents the j-th position, j = 1, 2…q ρ , The absolute humidity is ρ i The q measured at ρ The basic error value of humidity.

[0166] The test was started from the lowest absolute humidity level area S1 of the electric energy meter automatic verification line, and the average value of the basic humidity error value was obtained. Then, the same test is carried out on the S2 area with the absolute humidity increased by one level, and the average value of the basic humidity error is recorded. Gradually increase the absolute humidity level until the actual maximum ambient absolute humidity ρ C And record the average value of humidity basic error The obtained data are shown in Table 2.

[0167] Among them, the arithmetic mean of the basic error value of humidity The calculation formula is as follows:

[0168]

[0169] Using humidity arithmetic mean The data is used to calculate the weighted average, with weight p i and humidity weighted average The calculation of is shown in formula (33) and formula (34):

[0170] p1:p2:...:p i :...:p C =ρ1:ρ2:...:ρ i :...:ρ C (33)

[0171]

[0172] The standard deviation of the humidity weighted average is calculated as the standard uncertainty introduced by the ambient absolute humidity, as shown in equations (35) and (36):

[0173]

[0174] The standard uncertainty component u(r ρ ) is σ ρ .

[0175] Table 2 Environmental absolute humidity values ​​and corresponding basic error values

[0176]

[0177] (V) The standard uncertainty component u(r) introduced by the common impedance of the voltage loop and the current loop R ):

[0178] Since the voltage loop and current loop of the electric energy meter automatic verification line are coupled, there is a common impedance R between the voltage loop and the current loop. L , which will introduce the standard uncertainty u R ; R L =R1+R2, where R1 is the impedance of the common line, and R2 is the contact resistance generated by the wire connection and disconnection mechanism during crimping, and the contact resistance generated during each crimping is different. L The voltage drop generated when IR L , which is superimposed on the reference voltage U of the voltage loop, thus introducing an error

[0179] Extract a set number q from a randomly selected electric energy meter calibration device B The installed standard electric energy meter is connected to each meter position by automatic wiring mechanism and experimental detection is carried out respectively to determine the basic error corresponding to each meter position and obtain the basic error set. Filter out the maximum value r from each basic error Lmax and the minimum value r Lmin ; Then, the installed standard electric energy meter is directly connected to the voltage circuit and current circuit of the selected electric energy meter calibration device through manual wiring for experimental detection (at this time, the common impedance R L =0), and the basic error r O ;

[0180] Take the maximum value of the difference between the basic error measured in the automatic wiring case and the basic error measured in the manual wiring case δ Rmax =max{|r Lmax -r O |,|r O -r Lmin |}, distributed according to rectangle The standard uncertainty component u(r) introduced by the common impedance of the voltage loop and the current loop is calculated and obtained. R ) can be:

[0181] (VI) The standard uncertainty component u(r) introduced by the working standard table b ):

[0182] The automatic verification line of the electric energy meter in this embodiment adopts a single-phase standard meter of level k as the working standard meter. The standard uncertainty introduced by the working standard meter is:

[0183]

[0184] (VII) Standard uncertainty component u(r) introduced by electromagnetic interference signal B ):

[0185] The RF signal in the large-scale assembly line space mainly comes from the wireless transmission signal of mobile communication. Under normal working conditions, select a device, connect the installed standard electric energy meter to the calibration device, and record the basic error r Bm After turning off the mobile communication, connect the installed standard electric energy meter to the same meter position and record the basic error r B0 ; Calculate the variation δ B =r Bm -r B0 , according to the rectangular distribution Then u(r B )for

[0186] Step 3: The root mean square of the values ​​of the uncertainty components is used as the synthetic standard uncertainty of the electric energy meter automatic verification line, and its expression is as follows:

[0187]

[0188] Step 4: multiply the synthetic standard uncertainty by the set confidence factor to obtain the evaluation result of the expanded uncertainty of the electric energy meter automatic calibration line.

[0189] Among them, the expanded uncertainty takes the confidence factor K. Since there are many sources of uncertainty and the larger components are similar in size, the central limit theorem can be used to determine that the measured estimate is close to a normal distribution. If K = 2, the corresponding inclusion probability is about 95%. If K = 3, the corresponding inclusion probability is about 99%. The expanded uncertainty is

[0190] U=Ku c (39)

[0191] In general, the method for evaluating the measurement uncertainty of the automatic calibration line of the electric energy meter provided by the present invention is that the stability of the internal devices and the external environment of the automatic calibration line of the electric energy meter will change over time, so the uncertainty of the assembly line should be a dynamic parameter that changes over time; the present invention more comprehensively and accurately considers the sources of uncertainty of the automatic calibration line of the electric energy meter, and for the dynamic uncertainty introduced by repeatability, the Bayesian theory is used to obtain its prior distribution based on historical data and experience, and then the prior distribution is updated in real time in combination with the latest data sample to obtain the posterior distribution, and the uncertainty is evaluated based on the posterior distribution; the environmental impact quantity is classified more finely, and different environmental impact quantities are regarded as separate impact individuals to determine the uncertainty components of the environmental impact quantity; the same installed standard electric energy meter (a high-precision miniature standard electric energy meter compatible with the electrical characteristics, size and external structure of the smart electric energy meter) is used to replace multiple ordinary low-level smart electric energy meters for testing, and the accumulated electric energy value of the installed standard electric energy meter is used as the reference value, and the relative error between it and the accumulated electric energy value of the working standard meter (the instrument used to measure electric energy in the smart electric energy meter calibration device) is used as the basic error. The method of the present invention is based on dynamic data and updates uncertainty components according to the latest measurement data of different working conditions. It is more suitable for the time-varying state inside the assembly line system and the unstable external environment, and can more accurately evaluate the measurement uncertainty of the automatic calibration line of the electric energy meter.

[0192] The following is a further explanation of the application of the method for evaluating the measurement uncertainty of the electric energy meter automatic calibration line provided by the present invention through a specific experimental example.

[0193] (1) Standard uncertainty introduced by repeatability experiments

[0194] Randomly select 5 positions on the A4D2 calibration device, with position numbers 1, 37, 47, 2, and 58. Place the installed standard electric energy meter on the above 5 positions, and conduct 5 experiments in total. In each experiment, the 5 electric energy meters are tested for repeatability 5 times. The calibration voltage used is 220V, the calibration current is 60A and 30A respectively, the power factor is 1.0 and 0.5L respectively, the device ambient temperature is 29.8℃, and the relative humidity is 30.58%RH.

[0195] The basic error data of the calibration device with the table number 1 is selected as an example, as shown in Table 3.

[0196] Table 3 Repeatability basic error data of table 1

[0197]

[0198]

[0199]

[0200]

[0201] When the test current is 60A, the power factor When the arithmetic mean and arithmetic variance of the five data obtained in the first test are used as the distribution mean and distribution variance of the prior distribution, the prior distribution obeys N(-0.0510, 0.00088201). According to equations (4)-(9), the five basic error data obtained in the second test obey the distribution The updated distribution N (-0.0468, 0.00012096) was obtained by using the arithmetic mean and variance of the basic error of the second experiment. The third to fifth basic error data were then iterated to obtain the final distribution N (-0.0575, 0.00003628). The same algorithm was used to process 60A. 30A, and 30A, The basic error data under these conditions, the distribution mean and distribution variance obtained by iteration are shown in Table 4.

[0202] Table 4 Distribution parameter values ​​of basic error of table 1

[0203]

[0204]

[0205]

[0206]

[0207]

[0208] The same operation and calculation are performed on the selected 5 tables to obtain the posterior variance after the fifth repeatability test at each load point of each table. f(5,j) The maximum value is shown in Table 5.

[0209] Table 5 Maximum value of standard deviation of posterior distribution at each load point

[0210]

[0211] In actual measurement, the basic error is the average value of 5 measurements. Therefore, under this experimental data, the standard uncertainty u(r f )for When the standard uncertainty introduced by repeatability is When the standard uncertainty introduced by repeatability is

[0212] (2) Standard uncertainty introduced by multi-channel output consistency

[0213] The single-phase electric energy meter automatic calibration system has multiple devices, each with 60 meter positions. Relative to the single-phase electric energy meter automatic calibration system, the inconsistency between different devices and between different meter positions will introduce standard uncertainty. Five devices, A4D1-A4D5, were selected, and 10 meter positions were randomly selected from each device. The automatic connection and disconnection mechanism of the electric energy meter automatic calibration system was used to connect the installed standard meter to the device for calibration to obtain the basic error data. The calibration voltage used was 220V, the calibration current was 60A and 30A respectively, the power factor was 1.0 and 0.5L respectively, the device ambient temperature was 26.84℃, and the absolute humidity was 30.58%RH. The basic error data was recorded as shown in Appendix 1. The maximum error and the minimum error were selected at each load point of the five devices, and the maximum variation δ dmax , as shown in Table 6. According to the rectangular distribution Then u(r d )for

[0214] Table 6 Maximum variation of basic error of each calibration device

[0215]

[0216]

[0217] When the standard uncertainty introduced by the consistency of multiple outputs is When the standard uncertainty introduced by the consistency of multiple outputs is

[0218] (3) Standard uncertainty introduced by the working standard table

[0219] A single-phase standard meter of level 0.05 is used as the working standard meter. The standard uncertainty component u(r b )for:

[0220]

[0221] (4) Standard uncertainty introduced by unstable ambient temperature

[0222] Select four temperatures of 18℃, 24℃, 30℃, and 35℃, select a device, and place the installed standard meter at 5 meter positions to measure its basic error data. The calibration voltage used is 220V, and the calibration power factors are 1.0 and 0.5L respectively. Calculate the average value of the basic error at each temperature according to formula (23)-(27), and calculate the weighted average value r TThe standard deviation from the weighted mean is T , as shown in Table 7, then u(r T )=σ T .

[0223] Table 7 Weighted average value and standard deviation of weighted average value under different power factors

[0224]

[0225] When the ambient temperature is unstable, the standard uncertainty u(r T )=0.0519%;

[0226] The standard uncertainty u(r T )=0.0748%.

[0227] Since the humidity change on site of the automated calibration line is very small, the uncertainty component introduced by the absolute humidity of the environment is not considered.

[0228] (5) Standard uncertainty introduced by the common impedance of the voltage loop and the current loop

[0229] Since the voltage loop and current loop of the electric energy meter automatic verification system are coupled, there is a common impedance R between the voltage loop and the current loop. L , will introduce standard uncertainty. Among them, R L =R1+R2, where R1 is the impedance of the common line in the calibration device, and R2 is the contact resistance generated when the wire connection and disconnection mechanism is crimped.

[0230] The installed standard energy meter is placed in the 48th position of the calibration device with serial number A3D6. The energy indications of the installed standard energy meter and the working standard meter are recorded under 220V, 60A and 30A, automatic wiring in full meter state and manual wiring to connect the energy meter directly to the power supply, and their basic errors are calculated. The energy values ​​and basic error data measured under the two conditions of normal crimping of the calibration device and direct power supply wiring are shown in Table 8.

[0231] Table 8 Basic error data of manual wiring and automatic wiring of wiring mechanism

[0232]

[0233]

[0234]

[0235] When the reference voltage is 220V and the current is 60A, the error difference is basically between 0.1538% and 0.1900%. When the reference voltage is 220V and the current is 30A, the error difference is basically between 0.0745% and 0.0963%, with a maximum value of 0.1900%. According to the rectangular distribution Then u(r R )=0.1097%.

[0236] (6) Standard uncertainty introduced by electromagnetic interference signals

[0237] The RF signal in the large-scale assembly line space mainly comes from the wireless transmission signal of mobile communication. Under normal working conditions, the installed standard electric energy meter is connected to the calibration device table position and the basic error r is recorded. Bm After turning off the mobile communication, connect the installed standard electric energy meter to the same meter position and record the basic error r B0 Calculate the variation δ B =r Bm -r B0 , according to the rectangular distribution

[0238] The experimental results show that r Bm =0.0613%, r B0 =0.0550%, δ B =r Bm -r B0 =0.0063%, then

[0239] (7) Combined standard uncertainty and expanded uncertainty

[0240] Since the standard uncertainty components are independent of each other, the correlation coefficient is taken as 0.

[0241] The combined standard uncertainty is:

[0242] hour,

[0243]

[0244] hour,

[0245]

[0246] The expanded uncertainty takes the confidence factor 2, then:

[0247] When U 1.0 =2u c1.0 (r)=0.2686%

[0248] When U 0.5L =2u c0.5L (r) = 0.2894%

[0249] The beneficial effects of the present invention are as follows:

[0250] 1. The present invention considers the sources of uncertainty of the automatic calibration line of the electric energy meter more comprehensively and accurately. It is not limited to the classification standards of Class A uncertainty and Class B uncertainty in the traditional GUM method, but classifies the environmental impact quantities more finely, regards different environmental impact quantities as separate influencing individuals, and performs weighted processing on the basic errors measured within a certain range of environmental impact quantities, so as to more specifically study the influence of different environmental quantities on the automatic calibration line of the electric energy meter within a certain range of variation; in addition, the present invention is not limited to the static data of a single measurement, and does not simply divide the uncertainty range according to the measurement limit value, but combines historical data with newly measured samples, takes into account the changes in time and working conditions, and thus evaluates a more real-time and accurate measurement uncertainty.

[0251] 2. The present invention adopts the relative error between the same installed standard electric energy meter and the working standard meter as the basic error, eliminating the error introduced by the different electric energy meters used in the test, and further assessing the uncertainty of the electric energy meter automatic calibration line itself; the daily operation of the electric energy meter automatic calibration line can be monitored and evaluated based on the assessment results of the present invention, and abnormal operation can be discovered in time.

[0252] 3. The present invention takes into account the dynamic changes of system internal parameters and external environmental influences, uses the Bayesian rule to dynamically process the data obtained from repeatable measurements, combines the real-time measured sample information with the prior distribution to obtain the posterior distribution of the system state, and makes full use of the real-time dynamic information of the measurement system to obtain the repeatability uncertainty component caused by the time-varying internal parameters; adopts the control variable method to establish a mathematical relationship between uncertainty and temperature and humidity, and can calculate the corresponding uncertainty value according to the dynamic changes of the actual environment; finally, the total dynamic uncertainty is synthesized by combining its own structure with the remaining static uncertainty components to realize the analysis of the dynamic measurement uncertainty of the automatic calibration line of the electric energy meter; the present invention can more finely analyze the uncertainty source of the automatic calibration line of the electric energy meter, and can use real-time data to evaluate the uncertainty more accurately and in real time, effectively improving the accuracy of the measurement uncertainty evaluation, and can monitor the daily operation of the automatic calibration line of the electric energy meter in a more real-time and targeted manner;

[0253] 4. In summary, the present invention uses Bayesian theory combined with the latest test data to update the dynamic uncertainty introduced by repeatability in real time; for environmental quantity changes, the standard uncertainty introduced is weighted by comprehensively calculating the basic errors under all environmental impact values; compared with traditional static uncertainty assessment methods, the present invention is based on dynamic data and is more suitable for the time-varying state inside the assembly line system and the unstable external environment. It can more comprehensively and accurately assess the uncertainty of the measurement of the automatic calibration line of the electric energy meter, and provides a scientific basis for the dynamic uncertainty assessment of the automatic calibration line system of the electric energy meter in the current electric energy metering field.

[0254] Embodiment 2:

[0255] The present invention provides a device for evaluating the measurement uncertainty of an electric energy meter automatic verification line, the device is used to implement the steps of the method in the above-mentioned embodiment 1, and the device specifically comprises:

[0256] The uncertainty component determination module is used to analyze various uncertainty sources according to the structure of the automatic verification line of the electric energy meter to be evaluated and the environmental conditions in which it is located, so as to determine various uncertainty components for evaluation;

[0257] A basic error calculation module is used to use the same installed standard electric energy meter to carry out measurement experiments, and use the data measured by the installed standard electric energy meter as a reference value, and use the relative error between the data measured by the installed standard electric energy meter and the data displayed by the working standard meter as the basic error of the electric energy meter automatic verification line to calculate the value of each uncertainty component;

[0258] A synthetic standard uncertainty calculation module, used for taking the root mean square of the values ​​of each uncertainty component as the synthetic standard uncertainty of the electric energy meter automatic verification line;

[0259] The expanded uncertainty calculation module is used to multiply the synthetic standard uncertainty by a set confidence factor to obtain an evaluation result of the expanded uncertainty of the electric energy meter automatic verification line.

[0260] The device for evaluating the measurement uncertainty of the automatic calibration line of an electric energy meter provided in an embodiment of the present invention and the method for evaluating the measurement uncertainty of the automatic calibration line of an electric energy meter provided in Example 1 are based on the same technical concept, and can produce the beneficial effects described in Example 1. For the contents not described in detail in this embodiment, please refer to Example 1.

[0261] Embodiment three:

[0262] A terminal provided by an embodiment of the present invention includes a processor and a storage medium;

[0263] The storage medium is used to store instructions;

[0264] The processor is used to operate according to the instruction to execute the steps of the method according to any one of the first embodiments.

[0265] Embodiment 4:

[0266] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps of any method described in Embodiment 1 are implemented.

[0267] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0268] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0269] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0270] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0271] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for evaluating the measurement uncertainty of an electric energy meter automatic verification line, characterized in that: Methods include: Step 1: Analyze the uncertainty sources according to the structure of the automatic verification line of the electric energy meter to be evaluated and the environment in which it is located, so as to determine the uncertainty components used for evaluation; Step 2: Use the same installed standard electric energy meter to carry out measurement experiments, and use the data measured by the installed standard electric energy meter as the reference value. The relative error between the data measured by the installed standard electric energy meter and the data displayed by the working standard meter is used as the basic error of the electric energy meter automatic verification line to calculate the value of each uncertainty component. Step 3: taking the root mean square of the values ​​of each uncertainty component as the synthetic standard uncertainty of the electric energy meter automatic verification line; Step 4: multiply the synthetic standard uncertainty by the set confidence factor to obtain the evaluation result of the expanded uncertainty of the electric energy meter automatic calibration line.

2. The method for evaluating the measurement uncertainty of the electric energy meter automatic verification line according to claim 1 is characterized in that: In step 1, the uncertainty components include: The dynamic uncertainty component u(r f ), the standard uncertainty component u(r T ), the standard uncertainty component u(r ρ ), the standard uncertainty component u(r b ), the standard uncertainty component u(r d ), the standard uncertainty component u(r R ) and / or the standard uncertainty component u(r B ).

3. The method for evaluating the measurement uncertainty of the electric energy meter automatic verification line according to claim 1 is characterized in that: In step 2, the basic error r of the electric energy meter automatic verification line is calculated as follows: In the formula, E b and E0 respectively represent the electric energy value of the working standard meter and the electric energy value of the installed standard electric energy meter collected under the same conditions.

4. The method for evaluating the measurement uncertainty of the electric energy meter automatic verification line according to claim 1 is characterized in that: In step 2, the calculation of the values ​​of each uncertainty component includes: combining Bayesian theory to calculate the dynamic uncertainty component u(r f ), the process includes: Step a: randomly select a calibration device, use the same installed standard electric energy meter to conduct m tests on all the table positions E on the calibration device, and repeat the basic error n times in each experiment; where E is the total number of table positions on the selected calibration device, and m and n are the set number of experiments and repeated measurements, respectively; Step b: For any epitope j, j = 1, 2, ... E, repeat the first test n times to obtain the basic error set R f(1,j) As historical data, the mean and variance of the basic error set are used as the prior distribution mean and the prior distribution standard deviation respectively, so as to obtain the first prior normal distribution; Step c: Combine the basic error set obtained by repeating the second test n times with the first prior distribution to obtain the second posterior distribution. Similarly, the posterior distribution obtained from the current experiment is used as the prior distribution of the next test to continue to participate in the update and iteration of the distribution parameters to match the dynamic changes of the automatic verification line of the electric energy meter. Finally, the posterior distribution of the mth test of the table position j is obtained. Step d: Perform m tests on E electric energy meters according to the process of steps b and c, and record the updated posterior distribution variance of each meter after m tests to obtain the posterior distribution variance set D E ; Step e: From the posterior distribution variance set D E The largest posterior distribution variance value σ is selected 2 f(m,p) , σ 2 f(m,E) represents the variance of the posterior distribution of the pth electric energy meter updated after m tests, thereby calculating the dynamic uncertainty component u(r f ) is 5. The method for evaluating the measurement uncertainty of the electric energy meter automatic verification line according to claim 4 is characterized in that: In step c, the update iteration of the distribution parameters includes the update iteration of the posterior distribution mean and the posterior distribution variance, and the update formulas are as follows: In the formula, μ f(y+1,j) and μ f(y,j) are the means of the posterior distributions of epitope j updated in the y+1th and yth trials, respectively; σ 2 f(y+1,j) and σ 2 f(y,j) are the variances of the posterior distribution of epitope j updated in the y+1th and yth trials, respectively; is the arithmetic mean of the basic error set measured in the y+1th experiment for epitope j; σ 2 r(y+1,j) The variance of the basic error set measured for epitope j in the y+1th experiment.

6. The method for evaluating the measurement uncertainty of the electric energy meter automatic verification line according to claim 1 is characterized in that: In step 2, the calculation of the values ​​of each uncertainty component includes: combining the basic error weighted arithmetic to calculate the standard uncertainty component u(r T ), the process includes: Starting from the lowest temperature T1, with the set temperature change ΔT as a level change unit, the entire electric energy meter automatic verification line is divided into A areas S1, S2...S with different temperature levels. A ; Randomly select an electric energy meter calibration device from the same temperature level area, and randomly select a set number q from the electric energy meter calibration device T The installed standard electric energy meter is connected to each meter position by an automatic wiring mechanism and experimental tests are carried out respectively to determine the temperature basic error corresponding to each meter position, so as to obtain the temperature basic error value set corresponding to each temperature grade area; Calculate the arithmetic mean of the basic temperature error value set of each temperature level area respectively, and then calculate the weighted average value of the temperature error of all areas; The standard deviation of the weighted mean of the temperature error is taken as the standard uncertainty component u(r T ) value.

7. The method for evaluating the measurement uncertainty of the electric energy meter automatic verification line according to claim 6 is characterized in that: The standard uncertainty component u(r T ) is expressed as follows: In the formula, σ T Represents the weighted average of temperature error The standard deviation, p i represents the temperature weight of the i-th temperature level area, i = 1, 2…A; Represents the arithmetic mean of the temperature basic error value set for the i-th temperature level area.

8. The method for evaluating the measurement uncertainty of the electric energy meter automatic verification line according to claim 1 is characterized in that: In step 2, the calculation of the values ​​of each uncertainty component includes: combining the basic error weighted arithmetic to calculate the standard uncertainty component u(r ρ ), the process includes: Taking the lowest absolute humidity ρ1 as the starting point and the set humidity change Δρ% as a level change unit, the entire electric energy meter automatic verification line is divided into C areas S1, S2...S with different absolute humidity levels. C ; Randomly select one electric energy meter calibration device from the same absolute humidity level area, and randomly select a set number q from the electric energy meter calibration device ρ The installed standard electric energy meter is connected to each meter position by an automatic wiring mechanism and experimental tests are carried out respectively to determine the basic error corresponding to each meter position, so as to obtain the humidity basic error value set corresponding to each absolute humidity level area; Calculate the arithmetic mean of the humidity basic error value set of each absolute humidity level area respectively, and then calculate the weighted average of the humidity errors of all areas; The standard deviation of the weighted mean value of the humidity error is taken as the standard uncertainty component u(r ρ ) value.

9. The method for evaluating the measurement uncertainty of the electric energy meter automatic verification line according to claim 8 is characterized in that: The standard uncertainty component u(r ρ ) is expressed as follows: In the formula, σ ρ Represents the weighted average of humidity error The standard deviation, p i represents the humidity weight of the i-th absolute humidity level area, i = 1, 2…C; Represents the arithmetic mean of the humidity basic error value set for the i-th absolute humidity level area.

10. The method for evaluating measurement uncertainty of an electric energy meter automatic verification line according to claim 1, characterized in that: In step 2, the calculation of the values ​​of each uncertainty component includes: calculating the standard uncertainty component u(r) introduced by the working standard table by the following formula: b ): Where k represents the grade of the working standard meter used in the automatic calibration line of electric energy meters.

11. The method for evaluating measurement uncertainty of an electric energy meter automatic verification line according to claim 1, characterized in that: In step 2, the calculation of the values ​​of each uncertainty component includes: calculating the standard uncertainty component u(r d ), the process is as follows: Randomly select the set number p d A set number q is randomly selected from each electric energy meter calibration device. d The installed standard electric energy meter is connected to each meter position by an automatic wiring mechanism and experimental tests are carried out respectively to determine the basic error corresponding to each meter position; Calculate the maximum value of basic error δ between different epitopes in each device d(i)max The minimum value of basic error δ d(i)min The difference between d(i) =δ d(i)max -δ d(i)min , take p d The maximum value of the error difference in the device δ dmax , according to the rectangular distribution Calculate and obtain the standard uncertainty component u(r) introduced by the consistency of multi-channel output d ) can be:

12. The method for evaluating measurement uncertainty of an electric energy meter automatic verification line according to claim 1, characterized in that: In step 2, the calculation of the values ​​of each uncertainty component includes: calculating the standard uncertainty component u(r R ), the process is as follows: Extract a set number q from a randomly selected electric energy meter calibration device B The installed standard electric energy meter is connected to each meter position by automatic wiring mechanism and experimental detection is carried out to determine the basic error corresponding to each meter position, and the maximum value r is selected from each basic error. Lmax and the minimum value r Lmin ; The installed standard electric energy meter is directly connected to the voltage circuit and current circuit of the selected electric energy meter calibration device by manual wiring for experimental detection, and the basic error r is obtained. O ; Take the maximum value of the difference between the basic error measured in the automatic wiring case and the basic error measured in the manual wiring case δ Rmax =max{|r Lmax -r O |,|r O -r Lmin |}, distributed according to rectangle The standard uncertainty component u(r) introduced by the common impedance of the voltage loop and the current loop is calculated and obtained. R ) can be:

13. The method for evaluating measurement uncertainty of an electric energy meter automatic verification line according to claim 1, characterized in that: In step 2, the calculation of the values ​​of each uncertainty component includes: calculating the standard uncertainty component u(r B ), the process is as follows: A meter position is selected from a randomly selected electric energy meter calibration device. The installed standard electric energy meter is connected to the meter position by an automatic wiring mechanism under normal working conditions and when the mobile communication is turned off for experimental testing. The basic error r of the meter position under normal working conditions is obtained. Bm and the basic error r when mobile communication is turned off B0 ; Calculate the variation δ B =r Bm -r B0 , and distributed in a rectangular manner Calculate and obtain the standard uncertainty component u(r) introduced by the electromagnetic interference signal B ) is 14. The method for evaluating measurement uncertainty of an electric energy meter automatic verification line according to claim 1, characterized in that: In step 3, the expression of the combined standard uncertainty is as follows: In the formula, u c is the combined standard uncertainty, u(r f ) is the dynamic uncertainty component introduced by the repeatability experiment, u(r d ) is the uncertainty component introduced into the standard by the consistency of multi-channel output, u(r R ) is the standard uncertainty component introduced by the common impedance of the voltage loop and the current loop, u(r T ) is the standard uncertainty component introduced by the ambient temperature, u(r b ) is the standard uncertainty component introduced by the working standard table, u(r ρ ) is the uncertainty component introduced by the ambient humidity, u(r B ) is the standard uncertainty component introduced by the electromagnetic interference signal.

15. The method for evaluating the measurement uncertainty of the electric energy meter automatic verification line according to claim 14, characterized in that: In step 4, the expression of the expanded uncertainty is as follows: U=U c Where U is the expanded uncertainty and K is the set confidence factor.

16. A device for evaluating the measurement uncertainty of an automatic verification line of an electric energy meter, running the method for evaluating the measurement uncertainty of an automatic verification line of an electric energy meter as claimed in any one of claims 1 to 15, characterized in that: The device includes: The uncertainty component determination module is used to analyze various uncertainty sources according to the structure of the automatic verification line of the electric energy meter to be evaluated and the environmental conditions in which it is located, so as to determine various uncertainty components for evaluation; A basic error calculation module is used to use the same installed standard electric energy meter to carry out measurement experiments, and use the data measured by the installed standard electric energy meter as a reference value, and use the relative error between the data measured by the installed standard electric energy meter and the data displayed by the working standard meter as the basic error of the electric energy meter automatic verification line to calculate the value of each uncertainty component; A synthetic standard uncertainty calculation module, used for taking the root mean square of the values ​​of each uncertainty component as the synthetic standard uncertainty of the electric energy meter automatic verification line; The expanded uncertainty calculation module is used to multiply the synthetic standard uncertainty by a set confidence factor to obtain an evaluation result of the expanded uncertainty of the electric energy meter automatic verification line.

17. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-15.

18. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 15 are implemented.

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