CN114282173A - Large-scale intelligent electric meter accurate judgment calculation optimization method and system - Google Patents
Large-scale intelligent electric meter accurate judgment calculation optimization method and system Download PDFInfo
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Abstract
The invention discloses a method and a system for standard judgment, calculation and optimization of a large-scale intelligent electric meter, wherein the system for standard judgment, calculation and optimization of the large-scale intelligent electric meter 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 method, the accurate judgment is carried out on the intelligent electric meters in the transformer area in the subareas, the operation errors are amplified, and the accurate judgment of the intelligent electric meters is carried out according to the fluctuation percentage of the accurate judgment, so that the accurate judgment of the intelligent electric meters in the transformer area in the subareas is facilitated, and the influence of the large number of the intelligent electric meters on the accurate judgment of a small number of the intelligent electric meters with larger operation errors 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 accurate judgment, calculation and optimization of a large-scale intelligent electric meter.
Background
Compared with the traditional electric meter, the intelligent electric meter does not need manual meter reading, a large amount of manpower is saved, and power supply management is facilitated for a power supply unit. However, the smart meter also has an operation error, and the existence of the error can bring certain loss to users or power supply units. However, if the smart meters in the distribution area are accurately judged one by one, the load of the power supply management system is increased, and especially, the accurate judgment needs to be performed periodically. If the accurate judgment of the intelligent electric meter in the transformer area is carried out in a partitioning mode, the problem that the intelligent electric meters with large operation errors cannot be screened out due to the fact that the operation errors of the intelligent electric meters with large operation errors are averaged due to the fact that the number of the intelligent electric meters is large can occur.
Disclosure of Invention
Therefore, the technical problem to be solved by the invention is to provide a large-scale intelligent electric meter calibration judgment calculation optimization method and system, which are beneficial to performing calibration judgment on the intelligent electric meter partitions in a distribution area by performing calibration judgment on the intelligent electric meter partitions in the distribution area and amplifying operation errors, and then judging the intelligent electric meter calibration according to the fluctuation percentage of the intelligent electric meter, and can eliminate or reduce the influence of the large number of intelligent electric meters on the calibration of a small number of intelligent electric meters with larger operation errors.
In order to solve the technical problems, the invention provides the following technical scheme:
the large-scale intelligent electric meter calibration judgment calculation optimization method comprises the following steps:
s1) dividing the intelligent electric meters in the transformer area into n intelligent electric meter partitions, collecting power consumption data of each intelligent electric meter partition, and collecting i groups of power consumption data in a preset period t, wherein each group of power consumption data comprises j power consumption data, and i, j and n are positive integers; wherein, the partition mark of the intelligent electric meter is An；
S2) carrying out accurate judgment on the intelligent electric meter set of each region according to the electricity utilization data acquired in the step S1), f1(ζ)＝1000ζ，f2(ζ)＝(1000ζ)mZeta is the operation error of the intelligent electric meter corresponding to each electricity consumption data, and m is a rational number larger than 1;
s3) calculating the standard fluctuation percentage rho according to the standard calculated in the step S2)(i,j)And according to the mean value of the fluctuation percentage
Judging the running state of the intelligent electric meter when
The intelligent electric meter in the region operates normally, otherwise, the intelligent electric meter in the region operates abnormally, rho0The standard fluctuation percentage is the standard fluctuation percentage of the intelligent electric meter under normal operation; wherein, the standard fluctuation percentage ρ(i,j)And the standard fluctuation percentage average value
Calculated by the formulas (1) and (2), respectively:
s4) judging the smart meter calibration again for the region with the abnormal operation of the smart meter according to the steps S1), S2) and S3) until the abnormal smart meter is screened out.
In the method for judging, calculating and optimizing the calibration of the large-scale intelligent electric meter, in step S1), the display value of the intelligent electric meter is collected, and simultaneously, influence factors of the calibration of the intelligent electric meter are collected, wherein the influence factors of the calibration of the intelligent electric meter comprise environmental temperature, environmental humidity, power supply voltage and circuit current; in step S2), influence factors lambda, f influencing the smart meter calibration are introduced when the calibration judgment is carried out on the smart meter of each region1′(ζ)＝1000ζ(1-λ)，f′2(ζ)＝[1000ζ(1-λ)]m(ii) a In step S3), the quasi-fluctuation percentage ρ'(i,j)Calculated by equation (3):
wherein λ is obtained by the settlement of equation (4):
Uifor collecting supply voltage U when intelligent electric meter displays value0For stabilizing the supply voltage for the cell, IiFor collecting the supply current, I, of the smart meter during the display of the value0For stabilizing supply current, T, for the station areaiFor collecting ambient temperature T when intelligent electric meter displays value0At room temperature of 20 ℃, WiFor acquiring humidity when intelligent electric meter displays value, W0The daily humidity is 30%; | is the absolute value.
In the method for calculating and optimizing the calibration of the large-scale smart meter, in step S2), m is less than or equal to 2.
In the method for calculating and optimizing the calibration 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 method for accurately judging, calculating and optimizing the large-scale intelligent electric meter, in step S2), when the temperature is lower than 0 ℃, m is less than or equal to 1.5.
In the large-scale intelligent electric meter accurate judgment calculation optimization method, in step S1), when the intelligent electric meters in the distribution area are partitioned, the number of the intelligent electric meters in each intelligent electric meter partition is unequal.
In the method for accurately judging, calculating and optimizing the large-scale smart meter metering, in step S1), when the smart meters in the distribution area are partitioned, the number of the smart meters in at least one smart meter partition is odd.
The system for carrying out large-scale intelligent electric meter accurate judgment calculation optimization by using the large-scale intelligent electric meter accurate judgment calculation optimization method comprises the following steps:
the data acquisition module is used for acquiring the electricity utilization data of each intelligent ammeter partition and the temperature and humidity during the collection of the electricity utilization data;
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 electric meter standards of each intelligent electric meter partition by using 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 achieves the following beneficial technical effects:
1. according to the method, the operation errors of the partitions of the intelligent electric meters are amplified, so that the standard difference of the meters is amplified, the operation errors of a single intelligent electric meter or a small number of intelligent electric meters with large operation errors are prevented from being diluted by the number of the intelligent electric meters within the normal operation error range, the partition of the intelligent electric meters in a transformer area is favorably judged accurately, and the operation pressure of a power supply management system is favorably reduced.
2. According to the method, the influence of the environment temperature and the environment humidity on the intelligent electric meter calibration is introduced into the intelligent electric meter calibration judgment, so that the intelligent electric meter calibration misjudgment caused by environment factors can be avoided.
Drawings
FIG. 1 is a schematic diagram of the working principle of a large-scale intelligent electric meter accurate judgment calculation optimization system;
FIG. 2 is a flow chart of a large-scale intelligent electric meter accurate judgment calculation optimization method.
Detailed Description
As shown in fig. 1, the system for determining, calculating and optimizing the surface level of the large-scale smart meter in the present invention includes:
the data acquisition module is used for acquiring the electricity utilization data of each intelligent ammeter partition and the temperature and humidity during the collection of the electricity utilization data;
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 electric meter standards of each intelligent electric meter partition by using 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 electric meter accurate judgment calculation optimization system is used for carrying out large-scale intelligent electric meter accurate judgment calculation optimization, and the large-scale intelligent electric meter accurate judgment calculation optimization is realized through the following steps:
s1) dividing the intelligent electric meters in the transformer area into n intelligent electric meter partitions, collecting power consumption data of each intelligent electric meter partition, and collecting i groups of power consumption data in a preset period t, wherein each group of power consumption data comprises j power consumption data, and i, j and n are positive integers; wherein, the partition mark of the intelligent electric meter is AnWhen the intelligent electric meters in the transformer area are partitioned, the number of the intelligent electric meters in each intelligent electric meter partition is unequal, and the number of the intelligent electric meters in at least one intelligent electric meter partition is odd;
s2) carrying out accurate judgment on the intelligent electric meter set of each region according to the electricity utilization data acquired in the step S1), f1(ζ)＝1000ζ，f2(ζ)＝(1000ζ)mZeta is the operation error of the intelligent electric meter corresponding to each electricity consumption data, and m is a rational number larger than 1;
s3) calculating the standard fluctuation percentage rho according to the standard calculated in the step S2)(i,j)And according to the mean value of the fluctuation percentage
Judging the running state of the intelligent electric meter when
The intelligent electric meter in the region operates normally, otherwise, the intelligent electric meter in the region operates abnormally, rho0The standard fluctuation percentage is the standard fluctuation percentage of the intelligent electric meter under normal operation; wherein, the standard fluctuation percentage ρ(i,j)And the standard fluctuation percentage average value
Calculated by the formulas (1) and (2), respectively:
s4) judging the smart meter calibration again for the region with the abnormal operation of the smart meter according to the steps S1), S2) and S3) until the abnormal smart meter 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 affected, in step S1), the display value of the intelligent electric meter is collected, and meanwhile, the influence factors of the accuracy of the intelligent electric meter are collected, wherein the influence factors of the accuracy of the intelligent electric meter include an environmental temperature, an environmental humidity, a power supply voltage and a circuit current; in step S2), influence factors lambda, f influencing the smart meter calibration are introduced when the calibration judgment is carried out on the smart meter of each region1′(ζ)＝1000ζ(1-λ)，f′2(ζ)＝[1000ζ(1-λ)]m(ii) a In step S3), the quasi-fluctuation percentage ρ'(i,j)Calculated by equation (3):
wherein λ is obtained by the settlement of equation (4):
Uifor collecting supply voltage U when intelligent electric meter displays value0For stabilizing the supply voltage for the cell, IiFor collecting the supply current, I, of the smart meter during the display of the value0For stabilizing supply current, T, for the station areaiFor collecting ambient temperature T when intelligent electric meter displays value0At room temperature of 20 ℃, WiFor acquiring humidity when intelligent electric meter displays value, W0The daily humidity is 30%; | is the absolute value.
For a single environmental factor, m may be chosen to be different, for example, m is less than or equal to 1.6 when the humidity is greater than or equal to 60%; 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 local conditions.
And T is convenient for more accurately carrying out accurate judgment on the intelligent electric meteriFor acquiring average ambient temperature W of intelligent electric meter during displaying valueiThe method is used for acquiring the average relative humidity when the intelligent electric meter displays the value.
The invention will now be described by taking the standard judgment of the smart meter in the winter season in the Nanning area as an example.
And in 2021, 7 months and 7 days, in cloudy days, the highest temperature is 8 ℃, the lowest temperature is 4 ℃, the smart electric meters in a certain low-voltage transformer area of Nanning are subjected to accurate surface judgment in the early morning of 3: 00-3: 30, at noon of 12: 30-13: 00 and at night of 9: 00-9: 30, and the smart electric meters in the low-voltage transformer area are divided into 7 smart electric meter partitions. The air temperature and the relative humidity in three periods are shown in table 1, and the accurate judgment results of the intelligent electric meter in the low-voltage transformer area in the three periods are shown in tables 2-4.
TABLE 1 air temperature and relative humidity in three periods of time in the area of the low-voltage area
| Time period | Air temperature (. degree. C.) | Relative humidity (%) |
| 3:00～3:30 | 4.6 | 85 |
| 12:30～13:00 | 7.8 | 97 |
| 9:00～9:30 | 6.3 | 92 |
Meter 23: 00 ~ 3:30 time interval intelligent electric meter in low voltage station zone accurate judgment result (m is 1.6)
Meter 312: 30 ~ 13:00 time interval intelligent electric meter in low voltage station zone accurate judgment result (m is 1.6)
Meter accuracy judgment result of intelligent electric meter in low-voltage transformer area in meter 49: 00-9: 30 time period (m is 1.6)
T is based on 0.5 percent of operation error of the intelligent electric meter used in the local area in normal operation0And W0In the environment of (1), the calculated rho0162%, as can be seen from tables 2 to 4,
less than rho0And further, the fact that the intelligent electric meter in the low-voltage transformer area is normal can be known.
Zeta calculated by utilizing collected power utilization data is used as the error operation of the intelligent electric meter influenced by power supply voltage, environment temperature and environment humidityPoor, errors in actual meter operation should have the effect of these factors removed, ζFruit of Chinese wolfberry＝(1-λ)ζ。
When the intelligent electric meter calibration of another low-voltage transformer area is judged, in the process of judging the intelligent electric meter calibration of three periods, the intelligent electric meter which has a problem in the calibration is determined in five minutes each time. The artificial field is checked in the next day, and the intelligent electric meter is found to be exposed in a humid environment for a long time, so that the intelligent electric meter circuit board is affected with damp, corrosion points appear, and then the intelligent electric meter is caused to have problems.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications are possible which remain within the scope of the appended claims.
Claims (9)
1. The large-scale intelligent electric meter calibration judgment calculation optimization method is characterized by comprising the following steps:
s1) dividing the intelligent electric meters in the transformer area into n intelligent electric meter partitions, collecting power consumption data of each intelligent electric meter partition, and collecting i groups of power consumption data in a preset period t, wherein each group of power consumption data comprises j power consumption data, and i, j and n are positive integers; wherein, the partition mark of the intelligent electric meter is An；
S2) carrying out accurate judgment on the intelligent electric meter set of each region according to the electricity utilization data acquired in the step S1), f1(ζ)＝1000ζ，f2(ζ)＝(1000ζ)mZeta is the operation error of the intelligent electric meter corresponding to each electricity consumption data, and m is a rational number larger than 1;
s3) calculating the standard fluctuation percentage rho according to the standard calculated in the step S2)(i，j)And according to the mean value of the fluctuation percentage
Judging the running state of the intelligent electric meter when
The intelligent electric meter in the region operates normally, otherwise, the intelligent electric meter in the region operates abnormally, rho0The standard fluctuation percentage is the standard fluctuation percentage of the intelligent electric meter under normal operation; wherein, the standard fluctuation percentage ρ(i，j)And the standard fluctuation percentage average value
Calculated by the formulas (1) and (2), respectively:
s4) judging the smart meter calibration again for the region with the abnormal operation of the smart meter according to the steps S1), S2) and S3) until the abnormal smart meter is screened out.
2. The large-scale intelligent electric meter accuracy judgment calculation optimization method according to claim 1, wherein in step S1), the display value of the intelligent electric meter is collected and simultaneously the influence factors of the intelligent electric meter accuracy are collected, wherein the influence factors of the intelligent electric meter accuracy comprise ambient temperature, ambient humidity, power supply voltage and circuit current; in step S2), influence factors lambda, f 'influencing the smart meter calibration are introduced when the calibration judgment is carried out on the smart meter of each district'1(ζ)＝1000ζ(1-λ)，f′2(ζ)＝[1000ζ(1-λ)]m(ii) a In step S3), the quasi-fluctuation percentage ρ'(i，j)Calculated by the following formula (3):
wherein λ is obtained by the settlement of equation (4):
Uifor collecting supply voltage U when intelligent electric meter displays value0For stabilizing the supply voltage for the cell, IiFor collecting the supply current, I, of the smart meter during the display of the value0For stabilizing supply current, T, for the station areaiFor collecting ambient temperature T when intelligent electric meter displays value0At room temperature of 20 ℃, WiFor acquiring humidity when intelligent electric meter displays value, W0The daily humidity is 30%; and | | is an absolute value.
3. The method as claimed in claim 2, wherein m is less than or equal to 2 in step S2).
4. The large-scale smart meter leveling, judging and calculating optimization method of claim 3, wherein in step S2), when the humidity is greater than or equal to 60%, m is less than or equal to 1.6.
5. The method as claimed in claim 3, wherein in the step S2), m is less than or equal to 1.5 when the temperature is lower than 0 ℃.
6. The large-scale smart meter accuracy judgment calculation optimization method according to any one of claims 1 to 5, wherein in step S1), when partitioning the smart meters in the distribution area, the number of smart meters in each smart meter partition is not equal.
7. The method as claimed in claim 6, wherein in step S1), when partitioning the smart meters in the distribution area, the number of smart meters in at least one smart meter partition is odd.
8. The system for carrying out large-scale intelligent electric meter accurate judgment calculation optimization by using the large-scale intelligent electric meter accurate judgment calculation optimization method according to any one of claims 1 to 7, is characterized by comprising the following steps:
the data acquisition module is used for acquiring the electricity utilization data of each intelligent ammeter partition and the temperature and humidity during the collection of the electricity utilization data;
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 electric meter standards of each intelligent electric meter partition by using 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.
9. The system of claim 8, wherein the data processing module is communicatively coupled to the server.
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