CN108761196A - A kind of intelligent electric meter user missing voltage data restorative procedure - Google Patents
A kind of intelligent electric meter user missing voltage data restorative procedure Download PDFInfo
- Publication number
- CN108761196A CN108761196A CN201810275448.2A CN201810275448A CN108761196A CN 108761196 A CN108761196 A CN 108761196A CN 201810275448 A CN201810275448 A CN 201810275448A CN 108761196 A CN108761196 A CN 108761196A
- Authority
- CN
- China
- Prior art keywords
- user
- voltage
- data
- voltage data
- lacks
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R22/00—Arrangements for measuring time integral of electric power or current, e.g. electricity meters
Abstract
The present invention provides a kind of intelligent electric meter user missing voltage data restorative procedure, including：User's voltage data of a taiwan area is obtained based on power information acquisition system；Calculate the average value that each user corresponds to the voltage difference at moment；According to the size of user's voltage difference average value, it is proposed that user grouping foundation, by user grouping；According to group result, it is proposed that user lacks voltage correcting method, and user lacks voltage data between amendment group.The reparation that intelligent electric meter user lacks voltage data may be implemented in the present invention, and important data basis is provided for the development of related service.
Description
Technical field
The present invention relates to intelligent electric meter technical field, specifically a kind of intelligent electric meter user lacks voltage data reparation side
Method.
Background technology
Intelligent electric meter is the intelligent terminal of intelligent grid, in addition to having the function of measuring of traditional electric energy meter basic electricity
Outside, also has the function of power information storage, user terminal control etc..With the construction of intelligent grid, intelligent electric meter is very universal.
Intelligent electric meter can record and to information such as the voltage of power information acquisition system upload user, power, these data are related works
The important foundation data of work.But intelligent electric meter is at runtime, due to communication etc., can cause the loss of voltage data, data
After loss inconvenience is brought to operating analysis.
A kind of the distribution network data identification and modification method based on topological tree of 105514994 A of publication number CN, according to
The amount of capacity of distribution transformer to frontier node distribution transformer measure and fail polishing metric data carry out data identification with
It corrects.A kind of data cleaning method and system of publication number CN 106599193A, according to the data characteristics in data backup model
Value and restriction relation restore the data feature values of cleaning object.The data recovery method of application number 201610399396.0
And data prosthetic device, according to local repair data and relaying repair data, local new node to lose data slot into
Row repairs operation.105514994 A of publication number CN, CN 106599193A and application number 201610399396.0 are not directed to intelligence
Energy ammeter user lacks the reparation of voltage data.
Invention content
The present invention provides a kind of intelligent electric meter missing voltage data restorative procedure, may be implemented to use by the implementation of this method
Family lacks the reparation of voltage data, and data basis is provided for related service.
The technical solution adopted by the present invention is：
A kind of intelligent electric meter missing voltage data restorative procedure, includes the following steps：
A, user's voltage data of a taiwan area is obtained based on power information acquisition system；
B, the user's voltage data obtained based on step A, calculates the average value that each user corresponds to the voltage difference at moment；
C, the average value based on the step B voltage differences calculated, according to the size of average value, by the difference of the same taiwan area
User is grouped；
D, the user grouping based on step C is as a result, user lacks voltage data between amendment group；
Further, user's voltage data described in step A derives from power information acquisition system, be one day 24 points
Voltage data, i.e., the primary voltage data of each hour record.
Further, the average value that each user corresponds to the voltage difference at moment is calculated in step B, specially：User's X whole days
24 points of voltage data is UX1, UX2……UXn, the voltage data of 24 points of user Y whole days is UY1, UY2……UYn, wherein n=24；
The average value for the voltage difference that user X and Y correspond to the moment is：
Wherein UXi、UYiThe voltage value at i-th of moment of respectively user X and Y, i=1 ... n, n=24.
Further, user is grouped, specially according to the size of each user's voltage difference average value in step C：
As user X and user Y correspond to the average value U of the voltage difference at momentX-Y<1, then it is one group to divide user X and Y, other situations,
The rest may be inferred.
Further, user lacks voltage data between amendment group in step D, specially：If user C1, C2, C3 and C4 are
One group, certain moment user's voltage missing includes following several situations：
1) 1 user's voltage data missing：If certain moment user C1 voltage lacks, then lacking voltage correction formula is：Other situations, and so on；
2) 2 user's voltage data missings：If certain moment user's C1 and C2 voltage lacks, then voltage correction formula is lacked
For：Other situations, and so on；
3) 3 user's voltage data missings：If certain moment user C1, C2 and C3 voltage lacks, then lacks voltage and correct public affairs
Formula is：UC1=UC2=UC3=UC4, other situations, and so on；
4) when 4 user's voltage datas lack, missing data is not repaired.
The average value that the present invention corresponds to the voltage difference at moment by calculating each user, then according to the size of average value,
The different user of the same taiwan area is grouped, so based on user grouping as a result, between amendment group user lack voltage data,
The reparation that user lacks voltage data may be implemented, provide data basis for related service, and verify by actual items, lack
Amendment error when mistake is respectively less than 1V, shows the amendment error of modification method of the present invention within the acceptable range, table
The feasibility of institute's extracting method of the present invention is illustrated.
Description of the drawings
Fig. 1 is the flow diagram that intelligent electric meter user of the present invention lacks voltage data restorative procedure.
Specific implementation mode
Below in conjunction with the attached drawing in the present invention, the technical solution in the present invention is clearly and completely described.
Fig. 1 is please referred to, is that the flow that a kind of intelligent electric meter user provided by the invention lacks voltage data restorative procedure is shown
It is intended to, described method includes following steps：
Step A obtains user's voltage data of a taiwan area based on power information acquisition system, is one day 24 points of voltage
Data, i.e., the primary voltage data of each hour record.Have collected 20 emphasis users of certain taiwan area from September in 2017 28 days to
On January 8th, 2018, totally 110 days user's voltage datas, the voltage data of wherein September user C1 on the 28th are as shown in the table：
The voltage value of 1 2017 years Septembers of table user C1 on the 28th
| Moment | Voltage/V | Moment | Voltage/V | Moment | Voltage/V |
| 0 point | 233.7 | 8 points | 232.1 | 16 points | 231.1 |
| 1 point | 232.2 | 9 points | 233.6 | 17 points | 239.1 |
| 2 points | 232.1 | 10 points | 235.2 | 18 points | 238.3 |
| 3 points | 232.7 | 11 points | 235.8 | 19 points | 239.4 |
| 4 points | 230.8 | 12 points | 233.6 | 20 points | 237.4 |
| 5 points | 230.4 | 13 points | 233.1 | 21 points | 231.6 |
| 6 points | 232.4 | 14 points | 230.5 | 22 points | 237.2 |
| 7 points | 231.8 | 15 points | 235.7 | 23 points | 238.4 |
Step B：The taiwan area includes altogether 20 emphasis users, calculates separately the voltage difference between 20 emphasis users
Average value calculates user's C1 and C2 voltage difference, the results are shown in table below by taking September in 2017 28 days as an example；
The voltage difference result of calculation of 2 2017 years Septembers of table user C1 and C2 on the 28th
| Moment | Voltage difference/V | Moment | Voltage difference/V | Moment | Voltage difference/V |
| 0 point | 0.3 | 8 points | 0.4 | 16 points | 0.1 |
| 1 point | 0 | 9 points | 0.7 | 17 points | 0.2 |
| 2 points | 0 | 10 points | 0.1 | 18 points | 0.8 |
| 3 points | 0 | 11 points | 1.2 | 19 points | 0.1 |
| 4 points | 1.3 | 12 points | 0.9 | 20 points | 0.1 |
| 5 points | 0.1 | 13 points | 0.2 | 21 points | 1.6 |
| 6 points | 0.2 | 14 points | 0.2 | 22 points | 0 |
| 7 points | 0.3 | 15 points | 0.4 | 23 points | 0.4 |
According to voltage difference result of calculation, the voltage difference average value between September in 2017 user C1 and C2 on the 28th is acquired
It is 0.4, the average value for continuing to acquire this 110 days user's voltage differences on January 8th, 28 days 1 of September in 2017 is UC1-C2
=0.49, other users are also done with similar calculating.
Step C：20 users of the taiwan area are grouped according to the result of calculation of step B, due to UC1-C2=0.49,
UC1-C3=0.93, UC1-C4=0.77, therefore it is one group that can divide user C1, C2, C3, C4.
Step D：According to the group result of step C, the voltage data of user's C1, C2, C3 and C4 missing is modified, under
Face illustrates by way of example：
It lacks voltage data and repairs result：
1) 1 user's voltage data missing
For when 3 days 4 October in 2017, which only has user's C2 voltages missing, the voltage value of user C1, C3 and C4
Respectively 231.2V, 231.8V and 231.7V have according to correction formula：
2) 2 user's voltage data missings
For when September in 2017 21 days 20, this when be carved with 2 users (C1 and C3) voltage missing, user C2's and C4
Voltage value is respectively 232V and 228.6V, is had according to correction formula：
3) 3 user's voltage data missings
For when 8 days 13 January in 2018, this when be carved with 3 user (C1, C2 and C3) voltages missing, the electricity of user C4
Pressure value is 220.9V, is had according to correction formula：
UC1=UC2=UC3=UC4=220.9V
According to above method, the missing voltage data of user C1~C4 is modified, the amendment side will be analyzed below
The error condition of method.
The error condition of modification method：
1) 1 user's voltage missing
For when September in 2017 21 days 0, moment C1~C4 users voltage is respectively 234.4V, 234.6V, 234.5V
And 234.6V.Assuming that user's C1 voltages lack, then correction result isCorrection result with it is true
The error of real result is 0.2V.According to said method, amendment average error was calculated for 0.66V to 110 days all data.
2) 2 user's voltage missings
For when September in 2017 21 days 3, moment C1~C4 users voltage is respectively 234.7V, 234.6V, 234.7V
And 235V, it is assumed that user's C3 and C4 voltage lacks, then correction result isThen user C3
Correction result error be 0.05V, the correction result error of user C4 is 0.35V.According to said method, all data to 110 days
It is 0.69V to calculate and correct error.
3) 3 user's voltage missings
For when September in 2017 21 days 7, moment C1~C4 users voltage is respectively 235.2V, 235.3V, 234.8V
And 235.5V, it is assumed that user's C1~C3 voltages lack, then correction result is UC1=UC2=UC3=UC4=235.5V, user C1~
The amendment error of C3 is respectively 0.3V, 0.2V and 0.7V.According to said method, it is to 110 days all data calculating amendment errors
0.77V。
By analyzing above, the amendment error when shortage of data of 3 types is respectively less than 1V, shows amendment of the present invention
The amendment error of method within the acceptable range, shows the feasibility of institute's extracting method of the present invention.
Claims (5)
1. a kind of intelligent electric meter user lacks voltage data restorative procedure, it is characterised in that include the following steps：
A, user's voltage data of a taiwan area is obtained based on power information acquisition system；
B, the user's voltage data obtained based on step A, calculates the average value that each user corresponds to the voltage difference at moment；
C, the average value based on the step B voltage differences calculated, according to the size of average value, by the different user of the same taiwan area
It is grouped；
D, the user grouping based on step C is as a result, user lacks voltage data between amendment group.
2. intelligent electric meter user according to claim 1 lacks voltage data restorative procedure, it is characterised in that：In step A
User's voltage data derives from power information acquisition system, is one day 24 points of voltage data, i.e., each hour record
Primary voltage data.
3. intelligent electric meter user according to claim 1 lacks voltage data restorative procedure, it is characterised in that：In step B
The average value that each user corresponds to the voltage difference at moment is calculated, specially：24 points of the voltage data of certain day user X is UX1,
UX2……UXn, 24 points of the voltage data of user Y is UY1, UY2……UYn, wherein n=24；
The average value for calculating the voltage difference that user X and Y correspond to the moment in m days time ranges is：
Wherein UXi、UYiThe voltage value at i-th of moment of respectively user X and Y, i=1 ... n, when user X and Y are without shortage of data
When n=24；J=1 ... m, m are the number of days calculated.
4. intelligent electric meter user according to claim 1 lacks voltage data restorative procedure, it is characterised in that：In step C
According to the size of each user's voltage difference average value, user is grouped, specially：As user X and user Y correspond to the moment
The average value U of voltage differenceX-Y<1, then it is one group to divide user X and Y, other situations, and so on.
5. intelligent electric meter user according to claim 1 lacks voltage data restorative procedure, it is characterised in that：In step D
User lacks voltage data between amendment group, if it is one group to have P user, when the voltage data of P user lacks, does not then repair
Multiple missing data；Other shortage of data situations, repair missing data；Specially：If user C1, C2, C3 and C4 are one group, certain
Moment user's voltage missing includes following several situations：
1) 1 user's voltage data missing：If certain moment user C1 voltage lacks, then lacking voltage correction formula is：Other situations, and so on；
2) 2 user's voltage data missings：If certain moment user's C1 and C2 voltage lacks, then lacking voltage correction formula is：Other situations, and so on；
3) 3 user's voltage data missings：If certain moment user C1, C2 and C3 voltage lacks, then lacking voltage correction formula is：
UC1=UC2=UC3=UC4, other situations, and so on；
4) when 4 user's voltage datas lack, missing data is not repaired.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810275448.2A CN108761196B (en) | 2018-03-30 | 2018-03-30 | Method for repairing missing voltage data of intelligent electric meter user |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810275448.2A CN108761196B (en) | 2018-03-30 | 2018-03-30 | Method for repairing missing voltage data of intelligent electric meter user |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108761196A true CN108761196A (en) | 2018-11-06 |
| CN108761196B CN108761196B (en) | 2020-01-21 |
Family
ID=63980878
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810275448.2A Active CN108761196B (en) | 2018-03-30 | 2018-03-30 | Method for repairing missing voltage data of intelligent electric meter user |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108761196B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111680074A (en) * | 2019-12-31 | 2020-09-18 | 国网浙江省电力有限公司 | Clustering algorithm-based electric power collection load leakage point feature mining method |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102521080A (en) * | 2011-12-07 | 2012-06-27 | 福建省电力有限公司 | Computer data recovery method for electricity-consumption information collecting system for power consumers |
| CN103020166A (en) * | 2012-11-26 | 2013-04-03 | 宁波电业局 | Real-time electric data exception detection method |
| CN104200395A (en) * | 2014-08-22 | 2014-12-10 | 国家电网公司 | Technology for achieving missing data intelligent automatic additional recording based on automatic additional recording strategy definition |
| CN104216349A (en) * | 2013-05-31 | 2014-12-17 | 三星Sds株式会社 | Yield analysis system and method using sensor data of fabrication equipment |
| JP2015087240A (en) * | 2013-10-30 | 2015-05-07 | 三菱電機株式会社 | Power measurement system, power measurement method, and program |
| CN104915652A (en) * | 2015-06-09 | 2015-09-16 | 迪安杰科技无锡有限公司 | Fingerprint image restoration method and device |
| CN106250515A (en) * | 2016-08-04 | 2016-12-21 | 复旦大学 | Miss path restoration methods based on historical data |
-
2018
- 2018-03-30 CN CN201810275448.2A patent/CN108761196B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102521080A (en) * | 2011-12-07 | 2012-06-27 | 福建省电力有限公司 | Computer data recovery method for electricity-consumption information collecting system for power consumers |
| CN103020166A (en) * | 2012-11-26 | 2013-04-03 | 宁波电业局 | Real-time electric data exception detection method |
| CN104216349A (en) * | 2013-05-31 | 2014-12-17 | 三星Sds株式会社 | Yield analysis system and method using sensor data of fabrication equipment |
| JP2015087240A (en) * | 2013-10-30 | 2015-05-07 | 三菱電機株式会社 | Power measurement system, power measurement method, and program |
| CN104200395A (en) * | 2014-08-22 | 2014-12-10 | 国家电网公司 | Technology for achieving missing data intelligent automatic additional recording based on automatic additional recording strategy definition |
| CN104915652A (en) * | 2015-06-09 | 2015-09-16 | 迪安杰科技无锡有限公司 | Fingerprint image restoration method and device |
| CN106250515A (en) * | 2016-08-04 | 2016-12-21 | 复旦大学 | Miss path restoration methods based on historical data |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111680074A (en) * | 2019-12-31 | 2020-09-18 | 国网浙江省电力有限公司 | Clustering algorithm-based electric power collection load leakage point feature mining method |
| CN111680074B (en) * | 2019-12-31 | 2023-07-04 | 国网浙江省电力有限公司 | Clustering algorithm-based power acquisition load leakage point feature mining method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108761196B (en) | 2020-01-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111061821A (en) | Low-voltage distribution network topology verification method and system based on improved k value clustering algorithm | |
| CN110516912B (en) | Method for identifying household transformer relation of distribution station | |
| CN102623993B (en) | Distributed power system state estimation method | |
| CN110212521B (en) | Power distribution network line loss calculation method and system | |
| CN102521080B (en) | Computer data recovery method for electricity-consumption information collecting system for power consumers | |
| CN112698123B (en) | Decision tree-based low-voltage area user topological relation identification method | |
| CN111064180A (en) | Medium-voltage distribution network topology detection and identification method based on AMI power flow matching | |
| CN103632235A (en) | Improved forward-backward sweep algorithm based power-grid low-voltage platform area line loss information processing method | |
| CN104731636A (en) | Dispatcher Training Simulating Device And Operation Method Thereof | |
| CN108761196A (en) | A kind of intelligent electric meter user missing voltage data restorative procedure | |
| CN112054507A (en) | Power distribution low-voltage distribution area theoretical line loss interval calculation method based on convolutional neural network | |
| CN102545205A (en) | Distributed power system state estimating method based on automatic differentiation technology | |
| CN112418687A (en) | User electricity utilization abnormity identification method and device based on electricity utilization characteristics and storage medium | |
| CN105045958A (en) | Implementation system and method of GPS (Global Positioning System) elevation fitting on the basis of BP (Back Propagation) neural network | |
| Azzolini et al. | Calculating PV hosting capacity in low-voltage secondary networks using only smart meter data | |
| CN107146165B (en) | Distribution network planning data management method and system | |
| CN107146166B (en) | Distribution network planning auxiliary system | |
| CN111506874A (en) | Noise-containing sag source positioning data missing value estimation method | |
| CN115908051A (en) | Method for determining energy storage capacity of power system | |
| CN114676931A (en) | Electric quantity prediction system based on data relay technology | |
| CN108414851B (en) | Intelligent checking method based on EMS theory electric quantity data and collected electric quantity data | |
| CN114221334A (en) | Rapid state estimation method based on graph neural network | |
| CN103106350B (en) | Based on the line loss analyzing data restoration method of artificial neural network | |
| CN105405068A (en) | Power data detection method and apparatus | |
| CN112783681B (en) | Self-service big data multistage closed loop restoration method and device for power enterprise |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |