CN114282173B - Large-scale intelligent ammeter standard judgment calculation optimization method and system - Google Patents
Large-scale intelligent ammeter standard judgment calculation optimization method and system Download PDFInfo
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
The invention discloses a standard judgment, calculation and optimization method and a system for a large-scale intelligent ammeter, wherein the standard judgment, calculation and optimization system for the large-scale intelligent ammeter comprises a data acquisition module, a data storage module, a data processing module and a processing result display module; the data acquisition module is in communication connection with the data storage module, the data storage module is in communication connection with the data processing module, and the data processing module is in communication connection with the processing result display module. According to the intelligent ammeter partition judgment method and system, the intelligent ammeter partition in the platform area is subjected to the standard judgment, the running error is amplified, and the intelligent ammeter standard is judged according to the standard fluctuation percentage, so that the intelligent ammeter partition in the platform area is favorably judged, and the influence of large quantity of intelligent ammeters on the standard of a few intelligent ammeters with larger running error can be eliminated or reduced.
Description
Technical Field
The invention relates to the technical field of power grids. In particular to a method and a system for judging, calculating and optimizing the meter standard of a large-scale intelligent electric meter.
Background
Compared with the traditional ammeter, the intelligent ammeter does not need manual meter reading, saves a large amount of manpower, and is convenient for a power supply unit to carry out power supply management. However, the intelligent ammeter has operation errors, and certain loss is brought to a user or a power supply unit due to the existence of the errors. However, if the smart meters in the areas are subjected to the standard judgment one by one, the burden of the power supply management system is increased, and particularly, the standard judgment needs to be performed regularly. If the intelligent electric meter in the platform area is partitioned for standard judgment, the problem that the intelligent electric meters with larger running errors cannot be screened out due to the fact that the running errors of the intelligent electric meters with larger running errors are averaged due to the fact that the number of the intelligent electric meters is larger can occur.
Disclosure of Invention
Therefore, the technical problem to be solved by the invention is to provide the large-scale intelligent ammeter standard judging, calculating and optimizing method and system, which are beneficial to judging the standard of the intelligent ammeter subareas in the platform region by judging the standard of the intelligent ammeter subareas in the platform region and amplifying the operation errors and judging the standard of the intelligent ammeter according to the standard fluctuation percentage, and can eliminate or reduce the influence of the large number of the intelligent ammeters on the standard of a few intelligent ammeters with larger operation errors.
In order to solve the technical problems, the invention provides the following technical scheme:
the method for judging, calculating and optimizing the meter standard of the large-scale intelligent electric meter comprises the following steps:
S1) dividing intelligent electric meters in a platform area into n intelligent electric meter subareas, collecting electricity utilization data of each intelligent electric meter subarea, and collecting i groups of electricity utilization data in a preset period t, wherein each group of electricity utilization data comprises j pieces of electricity utilization data, and i, j and n are positive integers; the intelligent ammeter partition is marked as A n;
s2) performing standard judgment on the intelligent ammeter set of each zone according to the electricity consumption data acquired in the step S1), and f 1(ζ)＝1000ζ,f2(ζ)＝(1000ζ)m, wherein ζ is the intelligent ammeter operation error corresponding to each electricity consumption data, and m is a rational number greater than 1;
S3) calculating a standard fluctuation percentage rho (i,j) according to the standard calculated in the step S2), and averaging according to the standard fluctuation percentage Judging the running state of the intelligent ammeter whenThe intelligent ammeter in the area operates normally, otherwise, the intelligent ammeter in the area is abnormal, and ρ 0 is the standard fluctuation percentage of the intelligent ammeter under normal operation; wherein, the standard fluctuation percentage ρ (i,j) and the standard fluctuation percentage average valueCalculated by the formula (1) and the formula (2), respectively:
S4) judging the intelligent ammeter standard again for the area with the intelligent ammeter operation disorder according to the steps S1), S2) and S3) until the abnormal intelligent ammeter is screened out.
In the above large-scale smart meter standard judging, calculating and optimizing method, in step S1), the display value of the smart meter is collected, and the smart meter standard influencing factors are collected at the same time, wherein the smart meter standard influencing factors include the ambient temperature, the ambient humidity, the power supply voltage and the circuit current; in step S2), an influence factor λ, f 1′(ζ)＝1000ζ(1-λ),f′2(ζ)＝[1000ζ(1-λ)]m that influences the meter alignment of the smart meter is introduced when the meter alignment determination is performed on each area smart meter; in step S3), the apparent fluctuation percentage ρ' (i,j) is calculated by the formula (3):
wherein λ is determined by the formula (4):
U i is the power supply voltage when the display value of the intelligent electric meter is acquired, U 0 is the stable power supply voltage of the platform area, I i is the power supply current when the display value of the intelligent electric meter is acquired, I 0 is the stable power supply current of the platform area, T i is the environment temperature when the display value of the intelligent electric meter is acquired, T 0 is the normal temperature of 20 ℃, W i is the humidity when the display value of the intelligent electric meter is acquired, and W 0 is the daily humidity of 30%; and | is absolute value.
In the above large-scale smart meter standard judgment calculation optimization method, in step S2), m is less than or equal to 2.
In the above method for optimizing the calculation of the calibration judgment of the large-scale smart meter, in step S2), when the humidity is greater than or equal to 60%, m is less than or equal to 1.6.
In the above method for optimizing the calculation of the calibration judgment of the large-scale smart meter, in step S2), when the temperature is lower than 0 ℃, m is less than or equal to 1.5.
In the above large-scale smart meter standard judgment, calculation and optimization method, in step S1), when smart meters in a station area are partitioned, the number of smart meters in each smart meter partition is not equal.
In the above large-scale smart meter standard judgment, calculation and optimization method, in step S1), when smart meters in a station area are partitioned, the number of smart meters in at least one smart meter partition is an odd number.
The system for performing large-scale intelligent ammeter standard judgment, calculation and optimization by using the large-scale intelligent ammeter standard judgment, calculation and optimization method comprises the following steps:
the data acquisition module is used for acquiring electricity utilization data of each intelligent ammeter subarea and air temperature and humidity when the electricity utilization data are acquired;
the data storage module is used for storing the data acquired by the data acquisition module;
The data processing module is used for calculating and analyzing the intelligent ammeter standard of each intelligent ammeter partition by utilizing the electricity consumption data, the air temperature and the humidity acquired by the data acquisition module;
the processing result display module is used for displaying the processing result of the data processing module;
The data acquisition module is in communication connection with the data storage module, the data storage module is in communication connection with the data processing module, and the data processing module is in communication connection with the processing result display module.
In the system, the data processing module is in communication connection with the server.
The technical scheme of the invention has the following beneficial technical effects:
1. According to the invention, the operation errors of the intelligent ammeter subareas are amplified, so that the amplified meter standard is differentiated, the operation errors of single intelligent ammeter or few intelligent ammeters with larger operation errors are prevented from being desalted by the quantity of the intelligent ammeters within the normal operation error range, the method is beneficial to carrying out the standard judgment on the subareas of the intelligent ammeters, and the method is beneficial to reducing the operation pressure of a power supply management system.
2. According to the intelligent ammeter standard judgment method, the influence of the ambient temperature and the ambient humidity on the intelligent ammeter standard is introduced into the intelligent ammeter standard judgment, so that the intelligent ammeter standard misjudgment caused by the ambient factors can be avoided.
Drawings
FIG. 1 is a schematic diagram of the working principle of a large-scale intelligent ammeter standard judgment calculation optimization system;
Fig. 2 is a flowchart of a method for optimizing the accuracy judgment calculation of the large-scale smart meter.
Detailed Description
As shown in fig. 1, the system for optimizing the standard judgment and calculation of the large-scale intelligent electric meter in the invention comprises:
the data acquisition module is used for acquiring electricity utilization data of each intelligent ammeter subarea and air temperature and humidity when the electricity utilization data are acquired;
the data storage module is used for storing the data acquired by the data acquisition module;
The data processing module is used for calculating and analyzing the intelligent ammeter standard of each intelligent ammeter partition by utilizing the electricity consumption data, the air temperature and the humidity acquired by the data acquisition module;
the processing result display module is used for displaying the processing result of the data processing module;
The data acquisition module is in communication connection with the data storage module, the data storage module is in communication connection with the data processing module, and the data processing module is in communication connection with the processing result display module. The data processing module is in communication connection with the server so as to store the data processing result in the server for backup.
The large-scale intelligent ammeter standard judgment calculation optimization system is utilized to carry out large-scale intelligent ammeter standard judgment calculation optimization, and the method comprises the following steps:
S1) dividing intelligent electric meters in a platform area into n intelligent electric meter subareas, collecting electricity utilization data of each intelligent electric meter subarea, and collecting i groups of electricity utilization data in a preset period t, wherein each group of electricity utilization data comprises j pieces of electricity utilization data, and i, j and n are positive integers; the intelligent electric meter partition is marked as A n, when the intelligent electric meters in the platform area are partitioned, the quantity of the intelligent electric meters in each intelligent electric meter partition is unequal, and the quantity of the intelligent electric meters in at least one intelligent electric meter partition is odd;
s2) performing standard judgment on the intelligent ammeter set of each zone according to the electricity consumption data acquired in the step S1), and f 1(ζ)＝1000ζ,f2(ζ)＝(1000ζ)m, wherein ζ is the intelligent ammeter operation error corresponding to each electricity consumption data, and m is a rational number greater than 1;
S3) calculating a standard fluctuation percentage rho (i,j) according to the standard calculated in the step S2), and averaging according to the standard fluctuation percentage Judging the running state of the intelligent ammeter whenThe intelligent ammeter in the area operates normally, otherwise, the intelligent ammeter in the area is abnormal, and ρ 0 is the standard fluctuation percentage of the intelligent ammeter under normal operation; wherein, the standard fluctuation percentage ρ (i,j) and the standard fluctuation percentage average valueCalculated by the formula (1) and the formula (2), respectively:
S4) judging the intelligent ammeter standard again for the area with the intelligent ammeter operation disorder according to the steps S1), S2) and S3) until the abnormal intelligent ammeter is screened out.
In view of the fact that in an actual use environment, when some environmental factors reach a certain degree, the operation of the intelligent electric meter is influenced, in step S1), the display value of the intelligent electric meter is collected, and meanwhile, the intelligent electric meter alignment influencing factors are collected, wherein the intelligent electric meter alignment influencing factors comprise environmental temperature, environmental humidity, power supply voltage and circuit current; in step S2), an influence factor λ, f 1′(ζ)＝1000ζ(1-λ),f′2(ζ)＝[1000ζ(1-λ)]m that influences the meter alignment of the smart meter is introduced when the meter alignment determination is performed on each area smart meter; in step S3), the apparent fluctuation percentage ρ' (i,j) is calculated by the formula (3):
wherein λ is determined by the formula (4):
U i is the power supply voltage when the display value of the intelligent electric meter is acquired, U 0 is the stable power supply voltage of the platform area, I i is the power supply current when the display value of the intelligent electric meter is acquired, I 0 is the stable power supply current of the platform area, T i is the environment temperature when the display value of the intelligent electric meter is acquired, T 0 is the normal temperature of 20 ℃, W i is the humidity when the display value of the intelligent electric meter is acquired, and W 0 is the daily humidity of 30%; and | is absolute value.
For a single environmental factor, m may take different values, e.g., when humidity is greater than or equal to 60%, m is less than or equal to 1.6; when the temperature is lower than 0 ℃, m is less than or equal to 1.5. The value of m can be specifically selected according to the local situation.
In order to facilitate accurate determination of the smart meter, T i is the average ambient temperature when the smart meter display value is acquired, and W i is the average relative humidity when the smart meter display value is acquired.
The invention will now be described by taking the standard judgment of the intelligent ammeter in winter season in nan ning area as an example.
And (3) in 2021, 7 days of 1 month, in cloudy days, the highest temperature is 8 ℃, the lowest temperature is 4 ℃, and in the morning, the intelligent electric meters in a certain low-voltage station area of Nanning are subjected to standard judgment in the ranges of 3:00-3:30, 12:30-13:00 noon and 9:00-9:30, wherein the intelligent electric meters in the low-voltage station area are divided into 7 intelligent electric meter areas. The air temperature and relative humidity of the three periods are shown in table 1, and the determination results of the intelligent ammeter in the low-voltage transformer area of the three periods are shown in tables 2-4.
TABLE 1 air temperature and relative humidity in three time periods in the region of Low pressure area
| Time period of | Air temperature (DEG C) | Relative humidity (%) |
| 3:00～3:30 | 4.6 | 85 |
| 12:30～13:00 | 7.8 | 97 |
| 9:00～9:30 | 6.3 | 92 |
Table 2 Table 3:00-3:30 time period in low-voltage area intelligent ammeter standard judgment result (m 1.6)
Table 3 Table 12:30-13:00 time period in low-voltage area intelligent ammeter standard judgment result (m 1.6)
Table 4 Table 9:00-9:30 time period in the low-voltage area intelligent ammeter standard judgment result (m 1.6)
Based on the fact that the operation error of the intelligent ammeter used in the local area is 0.5% in normal operation, the calculated rho 0 is 162% in the environments of T 0 and W 0, as can be seen from tables 2 to 4,And the intelligent ammeter is smaller than rho 0, so that the intelligent ammeter in the low-voltage transformer area is normal.
The zeta obtained by calculation through the collected electricity data is an intelligent ammeter operation error influenced by the power supply voltage, the ambient temperature and the ambient humidity, and the influence of the factors should be removed by the actual ammeter operation error, so that zeta Real world = (1-lambda) zeta.
When judging the intelligent ammeter standard of another low-voltage station area, determining the intelligent ammeter with the problematic meter standard in five minutes respectively in the process of judging the intelligent ammeter standard of three time periods. The next day is checked on the artificial site, and the intelligent electric meter is found to be exposed in a humid environment for a long time, so that the circuit board of the intelligent electric meter is wetted, corrosion points appear, and further the intelligent electric meter is caused to have problems, and after the circuit board is replaced for the intelligent electric meter, the operation error of the intelligent electric meter is in a preset range.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. While the obvious variations or modifications which are extended therefrom remain within the scope of the claims of this patent application.
Claims (8)
1. The method for judging, calculating and optimizing the meter standard of the large-scale intelligent electric meter is characterized by comprising the following steps of:
S1) dividing intelligent electric meters in a platform area into n intelligent electric meter subareas, collecting electricity utilization data of each intelligent electric meter subarea, and collecting i groups of electricity utilization data in a preset period t, wherein each group of electricity utilization data comprises j pieces of electricity utilization data, and i, j and n are positive integers; the intelligent ammeter partition is marked as A n; acquiring a display value of the intelligent electric meter and acquiring the intelligent electric meter standard influence factors at the same time, wherein the intelligent electric meter standard influence factors comprise environment temperature, environment humidity, power supply voltage and circuit current;
s2) performing standard judgment on the intelligent ammeter set of each zone according to the electricity consumption data acquired in the step S1), and f 1(ζ)＝1000ζ,f2(ζ)＝(1000ζ)m, wherein ζ is the intelligent ammeter operation error corresponding to each electricity consumption data, and m is a rational number greater than 1; introducing influence factors lambda, f' 1(ζ)＝1000ζ(1-λ),f'2(ζ)＝[1000ζ(1-λ)]m for influencing the intelligent ammeter alignment when performing alignment judgment on the intelligent ammeter in each zone;
S3) calculating a standard fluctuation percentage rho (i,j) according to the standard calculated in the step S2), and averaging according to the standard fluctuation percentage Judging the running state of the intelligent ammeter whenThe intelligent ammeter in the area operates normally, otherwise, the intelligent ammeter in the area is abnormal, and ρ 0 is the standard fluctuation percentage of the intelligent ammeter under normal operation; wherein, the standard fluctuation percentage ρ (i,j) and the standard fluctuation percentage average valueCalculated by the formula (1) and the formula (2), respectively:
the apparent fluctuation percentage ρ' (i,j) is calculated by the following formula (3):
wherein λ is determined by the formula (4):
U i is the power supply voltage when the display value of the intelligent electric meter is acquired, U 0 is the stable power supply voltage of the platform area, I i is the power supply current when the display value of the intelligent electric meter is acquired, I 0 is the stable power supply current of the platform area, T i is the environment temperature when the display value of the intelligent electric meter is acquired, T 0 is the normal temperature of 20 ℃, W i is the humidity when the display value of the intelligent electric meter is acquired, and W 0 is the daily humidity of 30%; | is taken as the absolute value;
S4) judging the intelligent ammeter standard again for the area with the intelligent ammeter operation disorder according to the steps S1), S2) and S3) until the abnormal intelligent ammeter is screened out.
2. The large-scale smart meter calibration judgment calculation optimization method according to claim 1, wherein m is less than or equal to 2 in step S2).
3. The large-scale smart meter criterion evaluation calculation optimization method according to claim 2, wherein m is less than or equal to 1.6 when the humidity is greater than or equal to 60% in step S2).
4. The method according to claim 2, wherein m is less than or equal to 1.5 when the temperature is lower than 0 ℃ in step S2).
5. The method according to any one of claims 1 to 4, wherein in step S1), the number of smart meters per smart meter zone is not equal when the smart meters in the station zone are partitioned.
6. The method according to claim 5, wherein in step S1), when the smart meters in the area are partitioned, the number of smart meters in at least one smart meter partition is an odd number.
7. A system for performing large-scale smart meter accuracy judgment calculation optimization by using the large-scale smart meter accuracy judgment calculation optimization method according to any one of claims 1 to 6, comprising:
the data acquisition module is used for acquiring electricity utilization data of each intelligent ammeter subarea and air temperature and humidity when the electricity utilization data are acquired;
the data storage module is used for storing the data acquired by the data acquisition module;
The data processing module is used for calculating and analyzing the intelligent ammeter standard of each intelligent ammeter partition by utilizing the electricity consumption data, the air temperature and the humidity acquired by the data acquisition module;
the processing result display module is used for displaying the processing result of the data processing module;
The data acquisition module is in communication connection with the data storage module, the data storage module is in communication connection with the data processing module, and the data processing module is in communication connection with the processing result display module.
8. The system of claim 7, wherein the data processing module is communicatively coupled to the server.
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| CN111398885A (en) * | 2020-03-27 | 2020-07-10 | 天津大学 | Intelligent electric meter operation error monitoring method combining line loss analysis |
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| CN112346000B (en) * | 2020-10-30 | 2022-07-08 | 国网山东省电力公司营销服务中心(计量中心) | Intelligent electric energy meter operation error data statistical processing system and method |
| CN112684396B (en) * | 2020-11-20 | 2024-03-01 | 国网江苏省电力有限公司营销服务中心 | Data preprocessing method and system for electric energy meter operation error monitoring model |
| CN112686493A (en) * | 2020-11-24 | 2021-04-20 | 国网新疆电力有限公司营销服务中心(资金集约中心、计量中心) | Method for evaluating running state and replacing of intelligent electric meter in real time by relying on big data |
| CN113640732A (en) * | 2021-07-22 | 2021-11-12 | 黑龙江省电工仪器仪表工程技术研究中心有限公司 | Electric energy meter metering accuracy estimation system and method based on Pareto distribution |
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| CN109886465A (en) * | 2019-01-20 | 2019-06-14 | 东北电力大学 | A kind of distribution network load prediction technique based on intelligent electric meter user's clustering |
| CN111398885A (en) * | 2020-03-27 | 2020-07-10 | 天津大学 | Intelligent electric meter operation error monitoring method combining line loss analysis |
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