Method for sampling electric energy meter close to verification period

Abstract

The invention relates to the technical field of electric energy meter sampling methods, and discloses a method for sampling an electric energy meter close to a verification period, comprising the following steps: acquiring a file and an affiliated unit of the electric energy meter close to the verification period; for the acquired file, taking the electric energy meters close to the verification period as elements, and dividing the electric energy meters into a plurality of first-level sets; performing secondary division on each primary set to obtain a plurality of secondary sets; calculating the proportion Pn of the table number in each second-level set to the table number in the first-level set; determining the sampling sample size M1 of each primary set; calculating the sampling sample size M2 of each secondary set; adding M2 under each first-level set, and judging whether the sum is equal to M1 of the first-level set to which the M2 belongs: if yes, randomly obtaining M2 elements in each second-level set and summarizing the M2 elements to obtain a list; and if not, performing supplementary sampling. According to the invention, efficient and accurate sampling of the electric energy meter close to the verification period is realized, manpower and economic cost are saved, non-sampling errors are avoided, and data support is provided for stable operation of the electric energy meter.

Description

Technical Field

[0001] The invention relates to the technical field of electric energy meter sampling methods, and in particular to a method for sampling an electric energy meter approaching a calibration period. Background Art

[0002] With the continuous development of smart grid technology, the accuracy and reliability of electric energy meters, as important measurement tools in the power system, directly affect the normal operation of the power system and the calculation of users' electricity bills. As the years of use increase, the metering error may increase due to the aging of internal components, especially for those meters approaching their designed service life. This phenomenon is more obvious.

[0003] At present, the quality control of electric energy meters is mainly concentrated in the production stage and the first installation stage, while there is a lack of effective quality evaluation methods for those electric energy meters that have been running in the power grid for many years and are approaching the calibration period. The traditional sampling evaluation method is usually based on the principle of random sampling of all electric energy meters, but this method may not be able to effectively identify potential quality risk points when facing electric energy meters with a specific service life.

[0004] In addition, in a large-scale power grid operation environment, the number of electric energy meters is huge. If a comprehensive inspection method is adopted, it will not only be time-consuming and labor-intensive, but also economically costly. Therefore, an efficient and accurate sampling method suitable for electric energy meters approaching the calibration cycle is particularly important.

[0005] Electricity meter approaching the calibration period: an electricity meter whose remaining calibration period is less than one year (including one year). Summary of the invention

[0006] In view of the shortcomings and defects of the prior art, the present invention provides a method for sampling an electric energy meter near a calibration period, thereby realizing efficient and high-accuracy sampling of an electric energy meter near a calibration period.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] A method for sampling an electric energy meter near a calibration period comprises the following steps:

[0009] Step 1: Obtain the archive data and unit data of the electric energy meter approaching the calibration period in the electricity consumption information collection system;

[0010] Step 2: For the acquired archive data, the production year, manufacturer, accuracy level, rated voltage, and rated current are used as the division dimensions, and the electric energy meters near the calibration period are used as elements to divide them into several first-level sets;

[0011] Step 3: For each first-level set, a second division is performed based on the unit to which the electric energy meter in the adjacent calibration period belongs, to obtain several second-level sets;

[0012] Step 4: Calculate the ratio Pn of the number of electric energy meters in each secondary set to the total number of electric energy meters in the primary set to which it belongs;

[0013] Step 5: Determine the sampling sample size M1 of each first-level set according to the limiting quality;

[0014] Step 6: According to Pn and M1, calculate the sampling sample size M2 of each secondary set;

[0015] Step 7: Add the sample size M2 of the secondary set under each primary set, and determine whether the sum is equal to the sample size M1 of the primary set to which it belongs: if they are equal, jump to step 9; if not, jump to step 8;

[0016] Step 8: Carry out supplementary sampling for sets with unequal sample sizes;

[0017] Step 9: Randomly obtain M2 elements from each secondary set, summarize the obtained elements, and obtain a sample list.

[0018] Preferably, in step 2, the electric energy meters approaching the calibration period with the same production year, manufacturer, accuracy grade, rated voltage and rated current are divided into a first-level set.

[0019] Preferably, the process of determining the sampling sample size M1 of each primary set according to the limit quality in step 5 is performed according to the national standard of the People's Republic of China GB / T2828.2-2008;

[0020] Limiting mass LQ = 2%.

[0021] Preferably, M2 in step 6 is the integer part of the calculation result of M1*Pn.

[0022] Preferably, the supplementary sampling method in step 8 is: to supplement the missing sample size from the secondary set with the largest Pn under the primary set; if there is still a missing sample size after using all the elements in the secondary set with the largest Pn, then continue to supplement the missing sample size from the secondary set with the second largest Pn under the primary set, and so on, until the sampling sample sizes are equal.

[0023] The beneficial technical effect of the present invention is to achieve efficient and high-accuracy sampling of electric energy meters near the calibration period, which not only saves manpower and economic costs, but also avoids non-sampling errors, and provides reliable data support for the stable operation of electric energy meters near the calibration period. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is the overall flow chart of the present invention.

[0025] Figure 2 It is the sampling sample size when LQ=2% in the present invention. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and do not limit the present invention.

[0027] Example: Figure 1 As shown, a method for sampling an electric energy meter near a calibration period comprises the following steps:

[0028] Step 1: Obtain the archive data and unit data of the electric energy meter approaching the calibration period in the electricity consumption information collection system;

[0029] For the acquired archival data and unit data, a total of 1500 electric energy meters are set to be inspected, with meter serial numbers 1-1500, and there are 5 units in total, namely Unit A, Unit B, Unit C, Unit D, and Unit E.

[0030] Among them, there are 300 electric energy meters in Unit A approaching the calibration cycle, and the meter serial numbers are 1-300;

[0031] Among them, there are 450 electric energy meters in Unit B approaching the calibration period, with meter serial numbers 301-750;

[0032] Among them, there are 250 electric energy meters in unit C approaching the calibration cycle, with meter serial numbers 751-1000;

[0033] Among them, there are 300 electric energy meters in unit D approaching the calibration cycle, with meter serial numbers 1001-1300;

[0034] Among them, there are 200 electricity meters in unit E that are approaching the calibration period, and the meter serial numbers are 1301-1500.

[0035] Step 2: For the acquired archive data, the production year, manufacturer, accuracy level, rated voltage, and rated current are used as the division dimensions, and the electric energy meters near the calibration period are used as elements to divide them into several first-level sets;

[0036] For the first-level unit A, it is set to be divided into a first-level set α according to the dimensions of production year, manufacturer, accuracy level, rated voltage and rated current, with a total of 1,000 electric energy meters approaching the calibration period.

[0037] For the 1500 electric energy meters approaching the calibration period, the production year, manufacturer, accuracy level, rated voltage, and rated current are set as the division dimensions, and divided into two first-level sets, namely, first-level set α and first-level set β;

[0038] In the first-level set α, there are 1,000 electric energy meters approaching the calibration period, including 300 from unit A, 450 from unit B, and 250 from unit C;

[0039] Among them, the first-level set β has a total of 500 electricity meters approaching the calibration period, involving 300 meters from unit D and 200 meters from unit E.

[0040] Step 3: For each first-level set, a second division is performed based on the unit to which the electric energy meter in the adjacent calibration period belongs, to obtain several second-level sets;

[0041] For the primary set α: it is divided into three secondary sets, namely, secondary set α1 with a total of 300 electric energy meters, secondary set α2 with a total of 450 electric energy meters, and secondary set α3 with a total of 250 electric energy meters;

[0042] For the primary set β: it is divided into two secondary sets, namely the secondary set β1 with a total of 300 electricity meters and the secondary set β2 with a total of 200 electricity meters.

[0043] Step 4: Calculate the ratio Pn of the number of electric energy meters in each secondary set to the total number of electric energy meters in the primary set to which it belongs;

[0044] Secondary set α1: Pn = 300 / 1000 = 30%;

[0045] Secondary set α2: Pn = 450 / 1000 = 45%;

[0046] Secondary set α3: Pn = 250 / 1000 = 25%;

[0047] Secondary set β1: Pn = 300 / 500 = 60%;

[0048] Secondary set β2: Pn=200 / 500=40%.

[0049] Step 5: Determine the sampling sample size M1 of each primary set according to the limit quality; wherein this process; limit quality LQ = 2%;

[0050] First level set α: reference Figure 2 The sampling sample size according to the limit quality requirement LQ = 2% is M1 = 125;

[0051] Level 1 set β: Reference Figure 2The sampling sample size when the limit quality requirement LQ=2% is M1=105.

[0052] Step 6: According to Pn and M1, calculate the sample size M2 of each secondary set; where M2 is the integer part of the calculation result of M1*Pn;

[0053] Secondary set α1: 125*30%=37.5, after rounding M2=37;

[0054] Secondary set α2: 125*45%=56.25, after rounding M2=56;

[0055] Secondary set α3: 125*25%=31.25, after rounding, M2=31;

[0056] Secondary set β1: 105*60%=63, after rounding M2=63;

[0057] Secondary set β2: 105*40%=42, after rounding M2=42.

[0058] Step 7: Add the sample size M2 of the secondary set under each primary set, and determine whether the sum is equal to the sample size M1 of the primary set to which it belongs: if they are equal, jump to step 9; if not, jump to step 8;

[0059] According to the judgment basis of step 7, the first-level set α needs to sample 125 electric energy meters close to the calibration period, and the sum of the sampling sample size M2 of the second-level set α1, the second-level set α2, and the second-level set α3 is 37+56+31=124, which are not equal, so jump to step 8;

[0060] According to the judgment basis of step 7, the first-level set β needs to sample 105 electric energy meters that are close to the calibration period. The sum of the sampling sample size M2 of the second-level set β1 and the second-level set β2 is 63+42=105, and the two are equal, so jump to step 9.

[0061] Step 8: Supplementary sampling is performed for the sets with unequal sample sizes. The supplementary sampling method is: supplement the missing sample size from the secondary set with the largest Pn under the primary set. If there is still a missing sample size after using all the elements in the secondary set with the largest Pn, then continue to supplement the missing sample size from the secondary set with the second largest Pn under the primary set, and so on, until the sample sizes are equal;

[0062] For the primary set α, one additional electric energy meter needs to be sampled; according to step 8, one additional electric energy meter is sampled from the secondary set α2 with the largest Pn. After the additional sampling of the secondary set α2, the sampling size M1 of the primary set α is equal to M2 of the secondary sets α1, α2, and α3.

[0063] After supplementary sampling, M2 is:

[0064] Secondary set α1: M2 = 37;

[0065] Secondary set α2: M2 = 57;

[0066] Secondary set α3: M2 = 31;

[0067] Step 9: Randomly obtain M2 elements from each secondary set, summarize the obtained elements, and obtain a sample list;

[0068] It is assumed that the random acquisition method is: after internally random sorting each secondary set using the Fisher-Yates algorithm, the first M2 electric energy meters of each secondary set are obtained;

[0069] For the secondary set α1: Assume that the order of the electric energy meters in the adjacent verification period after random sorting is: the meter serial numbers are meter 300, meter 299...meter 2, meter 1; then the sampling sample list of the secondary set α1 is meter 300, meter 299...meter 264, a total of 37;

[0070] For the secondary set α2: Assume that the order of the electric energy meters in the adjacent verification period after random sorting is: the meter serial numbers are meter 750, meter 749...meter 302, meter 301; then the sampling sample list of the secondary set α2 is meter 750, meter 749...meter 694, a total of 57;

[0071] For the secondary set α3: Assume that the order of the electric energy meters in the adjacent verification period after random sorting is: meter serial number is meter 1000, meter 999...meter 752, meter 751; then the sampling sample list of the secondary set α3 is meter 1000, meter 999...meter 970, a total of 31;

[0072] For the secondary set β1: Assume that the order of the electric energy meters in the adjacent verification period after random sorting is: meter serial number is meter 1300, meter 1299...meter 1002, meter 1001; then the sampling sample list of the secondary set β1 is meter 1300, meter 1299...meter 1238, a total of 63;

[0073] For the secondary set β2: Assume that the order of the electric energy meters in the adjacent verification period after random sorting is: meter serial number is meter 1500, meter 1499...meter 1302, meter 1301; then the sampling sample list of the secondary set β2 is meter 1500, meter 1499...meter 1459, a total of 42;

[0074] Summarize the obtained elements of each secondary collection;

[0075] Sampling samples of the first-level set α: Table 300, Table 299...Table 264, Table 750, Table 749...Table 694, Table 1000, Table 999...Table 970;

[0076] Sampling samples of the first-level set β: Table 1300, Table 1299…Table 1238, Table 1500, Table 1499…Table 1459.

[0077] The above embodiments are descriptions of specific implementation methods of the present invention rather than limitations of the present invention. Technical personnel in the relevant technical field may make various changes and modifications to obtain corresponding equivalent technical solutions without departing from the spirit and scope of the present invention. Therefore, all equivalent technical solutions should be included in the patent protection scope of the present invention.

Claims

1. A method for sampling an electric energy meter near a calibration period, characterized in that: The following steps are involved: Step 1: Obtain the archive data and unit data of the electric energy meter approaching the calibration period in the electricity consumption information collection system; Step 2: For the acquired archive data, the production year, manufacturer, accuracy level, rated voltage, and rated current are used as the division dimensions, and the electric energy meters near the calibration period are used as elements to divide them into several first-level sets; Step 3: For each first-level set, a second division is performed based on the unit to which the electric energy meter in the adjacent calibration period belongs, to obtain several second-level sets; Step 4: Calculate the ratio Pn of the number of electric energy meters in each secondary set to the total number of electric energy meters in the primary set to which it belongs; Step 5: Determine the sampling sample size M1 of each first-level set according to the limiting quality; Step 6: According to Pn and M1, calculate the sampling sample size M2 of each secondary set; Step 7: Add the sample size M2 of the secondary set under each primary set, and determine whether the sum is equal to the sample size M1 of the primary set to which it belongs: if they are equal, jump to step 9; if not, jump to step 8; Step 8: Carry out supplementary sampling for sets with unequal sample sizes; Step 9: Randomly obtain M2 elements from each secondary set, summarize the obtained elements, and obtain a sample list.

2. A method for sampling an electric energy meter near a calibration period according to claim 1, characterized in that: In the step 2, the electric energy meters approaching the calibration period with the same production year, manufacturer, accuracy level, rated voltage and rated current are divided into a first-level set.

3. A method for sampling an electric energy meter near a calibration period according to claim 1, characterized in that: According to the National Standard of the People's Republic of China GB / T2828.2-2008, the process of determining the sampling sample size M1 of each primary set according to the limit quality in step 5 is performed; Limiting mass LQ = 2%.

4. A method for sampling an electric energy meter near a calibration period according to claim 1, characterized in that: M2 in step 6 is the integer part of the calculation result of M1*Pn.

5. A method for sampling an electric energy meter near a calibration period according to claim 1, characterized in that: The supplementary sampling method in step 8 is: to supplement the missing sample size from the secondary set with the largest Pn under the primary set. If there is still a missing sample size after using all the elements in the secondary set with the largest Pn, then continue to supplement the missing sample size from the secondary set with the second largest Pn under the primary set, and so on, until the sampling sample sizes are equal.

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