CN115453447A

CN115453447A - Online detection method for out-of-tolerance electric meter based on suspected electric meter stepwise compensation rejection

Info

Publication number: CN115453447A
Application number: CN202211144383.0A
Authority: CN
Inventors: 李富盛; 邓建斌; 周密; 郭斌; 钱斌; 许丽娟; 王吉; 冯兴兴; 赵烨; 肖勇; 陈俊艺; 罗奕; 孙颖; 张帆; 谷海彤
Original assignee: CSG Electric Power Research Institute; Guangzhou Power Supply Bureau of Guangdong Power Grid Co Ltd
Current assignee: CSG Electric Power Research Institute; Guangzhou Power Supply Bureau of Guangdong Power Grid Co Ltd
Priority date: 2022-09-20
Filing date: 2022-09-20
Publication date: 2022-12-09

Abstract

When the online detection method for the out-of-tolerance electric meters based on the suspected electric meter stepwise compensation rejection is used for detecting the intelligent electric meters, the line loss electric quantity, the sub-meter electric quantity and the sub-meter relative error of each station partition table in a target station area at each moment in a preset historical period can be determined firstly, the line loss electric quantity, the sub-meter electric quantity and the sub-meter relative error of each station partition table at each moment in the preset historical period are utilized, the station partition table with the largest average suspicion coefficient in each station partition table is determined, the station partition table with the largest average suspicion coefficient is used as a first suspected partition table set, and the first suspected partition table set is obtained by comprehensively analyzing the relative errors, the suspicion coefficients and the line loss factors, so that the result is more objective and comprehensive; then, the method and the device can also continue to screen the station partition tables in the first suspicion partition table set through a preset line loss judgment mechanism to obtain a second suspicion partition table set, and the suspicion partition tables in the set are more in line with actual conditions.

Description

Online detection method for out-of-tolerance electric meter based on suspected electric meter stepwise compensation rejection

Technical Field

The application relates to the technical field of intelligent electric meter detection, in particular to an out-of-tolerance electric meter online detection method and device based on suspected electric meter stepwise compensation rejection, a storage medium and computer equipment.

Background

At the present stage, various industries are forced to promote digital construction, and a power grid enterprise operates a large number of intelligent electric meters so as to collect mass electric energy data with high accuracy and excellent data quality. However, as the service life increases and the influence of environmental effects, equipment abnormalities, artificial damage and other factors, the metering accuracy of the smart meter decreases, thereby degrading the data quality.

At present, the intelligent electric meter mainly carries out error detection in a periodic field manual detection mode, and the method has the advantages of low efficiency, small detection range, serious consumption of manpower and material resources, higher maintenance cost and difficulty in meeting the requirements of digital transformation of the intelligent power grid and the daily operation and maintenance requirements.

Disclosure of Invention

The purpose of the application is to at least solve one of the above technical defects, especially the technical defects that in the prior art, error detection is performed in a periodic field manual detection mode, the method is low in efficiency, small in detection range, serious in manpower and material resource consumption, high in maintenance cost, and difficult to meet the requirements of digital transformation of the smart power grid and daily operation and maintenance requirements.

The application provides an online detection method for a worst electric meter based on suspected electric meter stepwise compensation and elimination, which comprises the following steps:

determining the line loss electric quantity, the sub-meter electric quantity and the sub-meter relative error of each sub-meter in the target station area at each moment in a preset historical time period;

determining the transformer area tables with the largest average suspicion coefficient in each transformer area table by using the line loss electric quantity, the sub-table electric quantity and the sub-table relative error of each transformer area table at each moment in the preset historical time period, and taking the transformer area tables with the largest average suspicion coefficient as a first suspicion sub-table set;

screening the station partition tables in the first suspicion partition table set through a preset line loss judgment mechanism to obtain a second suspicion partition table set;

selecting the station division table with the maximum absolute value of the table relative errors in each station division table to form a first error division table set according to the table relative errors of each station division table at each moment in the preset historical time period, and determining whether the first error division table set and the second suspected division table set have intersection;

if no intersection exists, directly taking the district electric meters in the second suspicion sub-meter set as out-of-tolerance electric meters;

and if the intersection exists, screening the transformer area tables with the intersection from the second suspected spreadsheet set to form a third suspected spreadsheet set, and screening transformer area ammeters in the third suspected spreadsheet set by using a suspected ammeter step-by-step compensation elimination method to obtain a final out-of-tolerance ammeter.

Optionally, the screening the station partition table in the first suspicion partition table set through a preset line loss judgment mechanism to obtain a second suspicion partition table set includes:

and comparing the line loss electric quantity of each station in the first suspicion sublist set at each moment with a first line loss threshold value respectively, determining that the line loss electric quantity in the first suspicion sublist set exceeds the station in the preset history period with the probability of the first line loss threshold value reaching the station in the preset probability threshold value, and forming a second suspicion sublist set by the station in the preset probability threshold value.

Optionally, the screening, by using a suspected electricity meter step-by-step compensation elimination method, the district electricity meters in the third suspected electricity meter set to obtain a final out-of-tolerance electricity meter includes:

taking the sorted first region table in the third suspected sub-table set as a final suspected electric meter, and compensating the sub-table electric quantity of the final suspected electric meter by using the sub-table relative error of the final suspected electric meter to obtain sub-table theoretical electric quantity;

removing the final suspect electric meter from a plurality of district sub-meters of the target district, and after subtracting the theoretical electric quantity of the sub-meters from a district general meter of the target district, determining the line loss electric quantity of each district sub-meter in the target district after the final suspect electric meter is removed at each moment in a preset historical period;

calculating the line loss electric quantity mean value of each transformer area division table after the final suspect electric meter is removed in a preset history time period according to the line loss electric quantity of each transformer area division table after the final suspect electric meter is removed in the target transformer area at each moment in the preset history time period, and judging whether the line loss electric quantity mean value is larger than a second line loss threshold value or not;

if the sum is larger than the preset third suspicion sub-table set, according to the sub-table electric quantity of each sub-table in the target table area after the final suspicion electric meter is removed at each moment in a preset history period and the total table electric quantity obtained by subtracting the theoretical electric quantity of the sub-table from the table area total table of the target table area, updating the relative sub-table error of each sub-table in the target table area after the final suspicion electric meter is removed at each moment in the preset history period and the table area table in the target table area, and returning to execute the step of determining the table area sub-table with the maximum average suspicion coefficient in each sub-table by using the line loss electric quantity, the table electric quantity and the relative sub-table error of each sub-table in the preset history period to obtain a new third suspicion sub-table set;

if the station partition table with the first average suspicion coefficient sorting in the new third suspicion partition table set is overlapped with the station partition table in the last third suspicion partition table set, judging whether the station partition table with the second average suspicion coefficient sorting in the new third suspicion partition table set is overlapped with the station partition table in the last third suspicion partition table set;

if yes, taking the second sorted station partition table as a final out-of-tolerance electric meter;

otherwise, taking the sorted first station partition table as a final out-of-tolerance electric meter;

and if not, taking the final suspected ammeter as an out-of-tolerance ammeter.

Optionally, the determining a sub-table relative error of each table sub-table in the target table area at each time within a preset history period includes:

acquiring first electric quantity data of a station area general table in a target station area in a preset historical time period and second electric quantity data of station area sub tables of which the electric quantity is greater than a preset electric quantity threshold value in the preset historical time period;

and determining the relative sub-meter error of each station sub-meter at each moment in the preset historical time period according to the first electric quantity data and the second electric quantity data corresponding to each station sub-meter.

Optionally, the obtaining first electric quantity data of a station area general table in a target station area in a preset history period and second electric quantity data of station area sub tables of which electric quantities of the respective sub tables are greater than a preset electric quantity threshold in the preset history period includes:

determining first original data of a station area general table in a target station area in a preset historical time period and second original data of each station area table in the preset historical time period;

preprocessing the first original data and the second original data to obtain first electric quantity data and second electric quantity data, wherein the preprocessing operation comprises the steps of eliminating abnormal data, filling missing data, correcting clock deviation data and correcting user variable data by utilizing a user variable file;

and according to the second electric quantity data of each station partition table, removing the station partition table with the sub-table electric quantity not greater than a preset electric quantity threshold value in each station partition table to obtain the second electric quantity data of the station partition table with the sub-table electric quantity greater than the preset electric quantity threshold value in the preset historical time period.

Optionally, the determining, according to the first electric quantity data and the second electric quantity data corresponding to each station partition table, a partition table relative error of each station partition table at each moment in the preset historical period includes:

performing time interval division on the first electric quantity data of the station area master table in the preset historical time interval and the second electric quantity data of each station division table in the preset historical time interval by using a sliding window with a preset length to obtain the master table electric quantity and the division table electric quantity in each time interval;

establishing a sub-meter relative error solving equation set corresponding to the total meter electric quantity and the sub-meter electric quantity in a plurality of time intervals based on an energy conservation law, and solving the sub-meter relative error solving equation set to obtain a point estimation value of a to-be-solved quantity, wherein the to-be-solved quantity comprises the sub-meter relative error, a line loss rate and fixed loss;

and determining the sub-table relative error of each station sub-table according to the point estimation value of the quantity to be solved.

Optionally, the determining, by using the line loss electric quantity, the sub-meter electric quantity, and the sub-meter relative error of each station partition table at each time in the preset history period, the station partition table with the largest average suspicion coefficient in each station partition table, and using the station partition table with the largest average suspicion coefficient as the first suspicion partition table set includes:

calculating confidence intervals corresponding to the sub-table relative errors of each table partition table by using T test in the significance test, and calculating the average suspicion coefficient of each table partition table in the preset historical period according to the confidence intervals corresponding to the sub-table relative errors of each table partition table;

calculating the correlation between the line loss electric quantity and the sub-meter electric quantity of each partition table in the preset history period according to the line loss electric quantity and the sub-meter electric quantity of each partition table at each moment in the preset history period, and determining the average suspicion coefficient correction factor of each partition table in the preset history period by utilizing a correlation influence mechanism;

and correcting the average suspicion coefficient of the corresponding table by using the average suspicion coefficient correction factor of each table, sorting the tables in a descending order according to the corrected average suspicion coefficient, and forming the table with the largest sorting into a first suspicion table set.

The application also provides an out-of-tolerance ammeter on-line measuring device based on suspected ammeter substep compensation is rejected, includes:

the parameter determining module is used for determining the line loss electric quantity, the sub-meter electric quantity and the sub-meter relative error of each sub-meter in the target station area at each moment in a preset historical time period;

the first suspicion division table determining module is used for determining a division table with the largest average suspicion coefficient in each division table by using the line loss electric quantity, the division table electric quantity and the division table relative error of each division table at each moment in the preset historical time period, and taking the division table with the largest average suspicion coefficient as a first suspicion division table set;

the second suspicion sublist determination module is used for screening the station partition tables in the first suspicion sublist set through a preset line loss judgment mechanism to obtain a second suspicion sublist set;

the table division judging module is used for selecting the table division table with the maximum absolute value of the table division relative error in each table division table to form a first error table division set according to the table division relative error of each table division table at each moment in the preset historical time period, and determining whether the first error table division set and the second suspected table division set have intersection;

the first out-of-tolerance electric meter determining module is used for directly taking the district electric meters in the second suspected sub-meter set as out-of-tolerance electric meters if no intersection exists;

and the second out-of-tolerance electric meter determining module is used for screening the district electric meters with the intersection from the second suspected branch meter set to form a third suspected branch meter set if the intersection exists, and screening the district electric meters in the third suspected branch meter set by using a suspected electric meter step-by-step compensation elimination method to obtain the final out-of-tolerance electric meter.

The application further provides a storage medium, wherein computer readable instructions are stored in the storage medium, and when the computer readable instructions are executed by one or more processors, the one or more processors execute the steps of the suspected electric meter step-by-step compensation elimination-based out-of-tolerance electric meter online detection method according to any one of the above embodiments.

The present application further provides a computer device, comprising: one or more processors, and a memory;

the memory stores computer readable instructions, and the computer readable instructions, when executed by the one or more processors, perform the steps of the suspected electric meter step-by-step compensation elimination-based out-of-tolerance electric meter online detection method according to any one of the above embodiments.

According to the technical scheme, the embodiment of the application has the following advantages:

when the online detection method for the out-of-tolerance electric meters based on the suspected electric meter stepwise compensation rejection is used for detecting the intelligent electric meters, the line loss electric quantity, the sub-meter electric quantity and the sub-meter relative error of each station partition table in a target station area at each moment in a preset historical period can be determined firstly, the line loss electric quantity, the sub-meter electric quantity and the sub-meter relative error of each station partition table at each moment in the preset historical period are utilized, the station partition table with the largest average suspicion coefficient in each station partition table is determined, the station partition table with the largest average suspicion coefficient is used as a first suspected partition table set, and the first suspected partition table set is obtained by comprehensively analyzing the relative errors, the suspicion coefficients and the line loss factors, so that the result is more objective and comprehensive; then, the method can further continue to screen the table partition tables in the first suspicion table set through a preset line loss judgment mechanism to obtain a second suspicion table set, the suspicion table partition tables in the set are more in line with practical conditions, and finally, the method can further select the table partition table with the largest absolute value of the table partition table relative error in each table partition table to form a first error table set according to the table partition table relative error of each table partition table at each moment in a preset historical time period, determine whether the first error table set and the second suspicion table set have an intersection, and directly take the table partition table electric meters in the second suspicion table set as out-of-tolerance electric meters if the first error table set and the second suspicion table set do not have the intersection; if the intersection exists, the station area electric meters with the intersection are screened from the second suspected branch meter set to form a third suspected branch meter set, and the station area electric meters in the third suspected branch meter set are screened by a suspected electric meter step-by-step compensation and elimination method, so that the suspected coefficient space is further compressed, the finally obtained out-of-tolerance electric meters are more accurate and reliable, the process can reduce the workload of field detection personnel, the detection rate is improved, and the false detection rate is reduced.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the description below are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without inventive labor.

Fig. 1 is a schematic flow chart of an online detection method for an out-of-tolerance electric meter based on suspected electric meter stepwise compensation and elimination according to an embodiment of the present application;

fig. 2 is a schematic structural diagram of an out-of-tolerance electric meter online detection device based on suspected electric meter distribution compensation elimination according to an embodiment of the present application;

fig. 3 is a schematic internal structural diagram of a computer device according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

At present, the intelligent electric meter mainly carries out error detection in a periodic field manual detection mode, and the method has the advantages of low efficiency, small detection range, serious consumption of manpower and material resources, higher maintenance cost and difficulty in meeting the requirements of digital transformation of the intelligent power grid and the daily operation and maintenance requirements. Based on this, the following technical solutions are proposed in the present application, specifically see the following:

in an embodiment, as shown in fig. 1, fig. 1 is a schematic flow chart of an online detection method for an out-of-tolerance electric meter based on suspected electric meter stepwise compensation and elimination according to an embodiment of the present application; the application provides an online detection method for a worst electric meter based on suspected electric meter stepwise compensation and elimination, which comprises the following steps:

s110: and determining the line loss electric quantity, the sub-meter electric quantity and the sub-meter relative error of each station partition table in the target station area at each moment in a preset historical time period.

In this step, when the smart electric meter is detected online, a target station area may be determined first, and then the line loss electric quantity, the sub-meter electric quantity and the sub-meter relative error of each station partition table in the target station area at each moment in a preset historical time period are determined, so that the station partition table with the largest average suspicion coefficient in each station partition table can be determined according to the line loss electric quantity, the sub-meter electric quantity and the sub-meter relative error.

The preset historical time period can be set according to the number of the station partition tables in the target station area and the accuracy of the detection result, when the number of the station partition tables in the target station area is large, the preset historical time period can be set to be a long time period in order to obtain a more accurate detection result, but the data volume needing to be calculated is larger when the longer the time period is considered, so that the setting can be performed in combination with the actual situation, for example, the setting is performed to acquire the electric quantity data in the past month, or two months, three months and the like, and the limitation is not made herein.

It can be understood that the station area of the present application refers to a power supply range or area of a transformer, the target station area is an area in the present application where an out-of-tolerance electric meter needs to be detected online, the out-of-tolerance electric meter refers to an area where a measurement error range of the electric meter exceeds the precision of the electric meter, the out-of-tolerance electric meter has a positive out-of-tolerance and a negative out-of-tolerance, and the measurement of the out-of-tolerance electric meter is inaccurate and cannot be used any more, so online detection is needed.

Further, when the influence of line loss electricity on electric meter error estimation is considered in the existing method, the influence of line loss randomness on electric meter error solution equations is often reduced by estimating line loss, but the line loss is time-varying and is difficult to accurately estimate. And according to simulation experiment results, an equation is solved for the electric meter errors, when the line loss electric quantity and the sub-meter electric quantity are in strong correlation, the shortage of the line loss electric quantity and the shortage of the sub-meter electric quantity can be mutually transferred, and the sub-meter errors obtained by the mutual transfer are inaccurate. Therefore, the line loss electric quantity and the sub-meter electric quantity of each sub-meter in the target sub-meter at each moment in the preset historical time period can be determined firstly, and then the suspected electric meters obtained by using the relative errors of the sub-meters are further screened through the correlation between the line loss electric quantity and the sub-meter electric quantity, so that the identification accuracy of the out-of-tolerance electric meters is improved.

S120: and determining the station division table with the maximum average suspicion coefficient in each station division table by using the line loss electric quantity, the division table electric quantity and the division table relative error of each station division table at each moment in a preset historical time period, and taking the station division table with the maximum average suspicion coefficient as a first suspicion division table set.

In this step, after the line loss electric quantity, the sub-meter electric quantity and the sub-meter relative error of each sub-meter in the target sub-meter at each moment in the preset history period are determined through S110, the sub-meter with the largest average suspicion coefficient in each sub-meter can be determined by using the line loss electric quantity, the sub-meter electric quantity and the sub-meter relative error of each sub-meter at each moment in the preset history period, and the sub-meter with the largest average suspicion coefficient is taken as the first suspicion sub-meter set.

Specifically, in order to further screen the suspect electric meters obtained by using the relative errors of the branch meters through the correlation between the line loss electric quantity and the electric quantity of the branch meters, the average suspect coefficient of each branch meter in the preset history period can be calculated according to the confidence interval of the relative errors of the branch meters of each branch meter at each moment in the preset history period, so that the average suspect coefficient can be corrected by using the correlation between the line loss electric quantity and the electric quantity of the branch meters, and a more accurate identification result can be obtained.

It can be understood that, because the confidence interval obtained in the present application is the confidence interval corresponding to the relative error of each table in each time in the preset history period, when the average suspicion coefficient of each table in the preset history period is calculated, the suspicion coefficient of each table in each time in the preset history period may be calculated first, and then the final average suspicion coefficient may be obtained according to the suspicion coefficient of each time.

Further, when the average suspicion coefficient is corrected by using the correlation between the line loss electric quantity and the sub-meter electric quantity, the average suspicion coefficient correction factor of each sub-meter in a preset historical period can be determined by using a correlation influence mechanism, so that the average suspicion coefficient can be corrected by the average suspicion coefficient correction factor, and the sub-meters are sorted in a descending order according to the corrected average suspicion coefficient.

The relevance influence mechanism of the present application refers to determining influence on an average suspicion coefficient of a certain partition table according to relevance between electric quantity of the partition table and electric quantity of the partition table within a preset time period. For example, when the line loss capacity of a certain division table and the capacity of a certain division table are negatively correlated, the situation is consistent with the performance of electricity stealing behavior, and therefore the average suspicion coefficient of the division table should be increased; when the line loss electric quantity and the sub-meter electric quantity of a certain sub-meter are in negative correlation, the sub-meter relative error estimation is carried out on the sub-meter by utilizing a multivariate linear regression method or an improved algorithm thereof, the sub-meter electric quantity and the line loss electric quantity have the same status in mathematics, so that the errors can be shared between the sub-meter relative error and the line loss rate, the calculation results of the sub-meter relative error and the line loss rate are deviated, and the average suspicion coefficient of the sub-meter is reduced at the moment.

S130: and screening the station partition tables in the first suspicion partition table set through a preset line loss judgment mechanism to obtain a second suspicion partition table set.

In this step, after the station partition table with the largest average suspicion coefficient in each station partition table is determined through S120, and the station partition table with the largest average suspicion coefficient is taken as the first suspicion partition table set, since the average suspicion coefficient is the average value of each suspicion coefficient in the preset historical period, the duration of the relative error of the partition tables is generally not too long, and whether the average suspicion coefficient is out of tolerance is determined according to the electric meter error in the current period in the field detection, the influence of the suspicion coefficient at the historical time is introduced in the method, so that the average suspicion coefficient in the current period is wrong.

Based on this, after obtaining first suspicion spread sheet set, this application can screen the platform district table in the first suspicion spread sheet set through predetermined line loss judgment mechanism, constitute second suspicion spread sheet set with the platform district table that line loss electric quantity exceeds certain threshold value in the first suspicion spread sheet set to this further improves the identification accuracy of super-poor ammeter.

S140: selecting the station division table with the largest absolute value of the table relative errors in each station division table to form a first error division table set according to the table relative errors of each station division table at each moment in a preset historical time period, and determining whether the first error division table set and the second suspected division table set have intersection; if there is no intersection, executing S150; if there is an intersection, S160 is executed.

In this step, the table partition tables in the first suspicion sublist set are screened through S130, and after the second suspicion sublist set is obtained, because the second suspicion sublist set only considers the average suspicion coefficient and does not combine with sublist relative error for further screening, therefore, the second suspicion sublist set can be further screened by using sublist relative error to further improve the identification accuracy.

Specifically, the absolute value of the sub-table relative error of each sub-table in the second suspected sub-table set at each time within the preset history period may be calculated according to the sub-table relative error of each sub-table in the second suspected sub-table set at each time within the preset history period, then the sub-table with the largest absolute value of the sub-table relative error in each sub-table is selected to form the first error sub-table set, then the first error sub-table set and the second suspected sub-table set may be compared to determine whether an intersection exists between the first error sub-table set and the second suspected sub-table set, if the intersection does not exist, it is indicated that the current second suspected sub-table set does not include the sub-table with the largest absolute value of the sub-table relative error, at this time, the sub-tables in the second suspected sub-table set may be directly used as super-error electric meters, if the intersection exists, it is indicated that the current second suspected sub-table set includes the sub-table with the largest absolute value of the sub-table relative error, and at this time, a further screening operation is required.

S150: and directly taking the district electric meters in the second suspected branch meter set as out-of-tolerance electric meters.

In this step, it can be known from S140 that, when there is no intersection between the first error sub-table set and the second suspected sub-table set, it indicates that there is no station partition table in the second suspected sub-table set that includes the station partition table with the largest absolute value of the relative error of the sub-tables, and at this time, the station partition table in the second suspected sub-table set may be directly used as the out-of-tolerance electric meter.

S160: and screening the district electric meters in the third suspicion branch meter set by using a suspicion electric meter step-by-step compensation elimination method to obtain the final out-of-tolerance electric meter.

In this step, it can be known from S140 that, when there is an intersection between the first error sub-meter set and the second suspected sub-meter set, it indicates that the current second suspected sub-meter set includes the station sub-meter with the largest absolute value of the relative error of the sub-meters, at this time, the station sub-meter with the intersection can be screened from the second suspected sub-meter set to form a third suspected sub-meter set, and the station sub-meters in the third suspected sub-meter set are screened by using the suspected electric meter step-by-step compensation elimination method, so as to obtain the final out-of-tolerance electric meter.

The suspected ammeter step-by-step compensation removal method is a method for gradually removing the suspected ammeter by combining relative errors of the ammeter, the suspected coefficient and the line loss factor, and the accuracy of the out-of-tolerance ammeter obtained through the method is high.

In the above embodiment, when the smart meter is detected, the line loss electric quantity, the sub-meter electric quantity and the sub-meter relative error of each sub-meter in the target sub-meter at each time in the preset history period may be determined, the sub-meter with the largest average suspicion coefficient in each sub-meter is determined by using the line loss electric quantity, the sub-meter electric quantity and the sub-meter relative error of each sub-meter at each time in the preset history period, and the sub-meter with the largest average suspicion coefficient is taken as the first suspicion sub-meter set obtained by performing comprehensive analysis by using the sub-meter relative error, the suspicion coefficient and the line loss factor, so that the result is more objective and comprehensive; then, the method can further continue to screen the table partition tables in the first suspicion table set through a preset line loss judgment mechanism to obtain a second suspicion table set, the suspicion table partition tables in the set are more in line with practical conditions, and finally, the method can further select the table partition table with the largest absolute value of the table partition table relative error in each table partition table to form a first error table set according to the table partition table relative error of each table partition table at each moment in a preset historical time period, determine whether the first error table set and the second suspicion table set have an intersection, and directly take the table partition table electric meters in the second suspicion table set as out-of-tolerance electric meters if the first error table set and the second suspicion table set do not have the intersection; if the intersection exists, the station area tables with the intersection are screened out from the second suspected table set to form a third suspected table set, and the station area electric meters in the third suspected table set are screened by using a suspected electric meter step-by-step compensation and elimination method, so that the suspected coefficient space is further compressed, the finally obtained out-of-tolerance electric meters are more accurate and reliable, the process can reduce the workload of field detection personnel, the detection rate is improved, and the false detection rate is reduced.

In an embodiment, the screening the station partition tables in the first suspicion partition table set through a preset line loss judgment mechanism in S130 to obtain a second suspicion partition table set may include:

s131: and comparing the line loss electric quantity of each station in the first suspicion sublist set at each moment with a first line loss threshold value respectively, determining that the line loss electric quantity in the first suspicion sublist set exceeds the station in the preset history period with the probability of the first line loss threshold value reaching the station in the preset probability threshold value, and forming a second suspicion sublist set by the station in the preset probability threshold value.

In this embodiment, when the station partition tables in the first suspicion partition table set are screened through the line loss judgment mechanism, the line loss electric quantity of each station partition table in the first suspicion partition table set at each moment in the preset history period may be compared with the first line loss threshold value, and then the station partition table in which the line loss electric quantity exceeds the first line loss threshold value in the preset history period is determined, and then the station partition table in which the probability that the line loss electric quantity exceeds the first line loss threshold value reaches the preset probability threshold value may be selected from the station partition tables, and the second suspicion partition table set is formed by the station partition tables.

The first line loss threshold and the preset probability threshold are both parameters which can be adjusted manually. For example, according to field experience, the first line loss threshold value is set to be 2% by default, the preset probability threshold value is set to be 80% by default, and if the field detection rate is required to be improved, the first line loss threshold value can be reduced or the preset probability threshold value can be increased, and the setting can be performed according to actual conditions, and is not limited herein.

In an embodiment, the screening, in S160, the area electric meters in the third suspected partial table set by using a suspected electric meter step-by-step compensation elimination method to obtain a final out-of-tolerance electric meter may include:

s161: taking the sorted first district tables in the third suspicion table set as final suspicion electric meters, and compensating the sub-meter electric quantity of the final suspicion electric meters by using the sub-meter relative error of the final suspicion electric meters to obtain sub-meter theoretical electric quantity;

s162: removing the final suspect electric meter from a plurality of district sub-meters of the target district, and after subtracting the theoretical electric quantity of the sub-meters from a district general meter of the target district, determining the line loss electric quantity of each district sub-meter in the target district after the final suspect electric meter is removed at each moment in a preset historical period;

s163: calculating the line loss electric quantity mean value of each transformer area division table after the final suspect electric meter is removed in a preset history time period according to the line loss electric quantity of each transformer area division table after the final suspect electric meter is removed in the target transformer area at each moment in the preset history time period, and judging whether the line loss electric quantity mean value is larger than a second line loss threshold value or not; if yes, executing S164; if not, go to S168.

S164: and updating the table division relative error of each table division table after the final suspected ammeter is removed in the target table area at each moment in a preset history period and the table division table in the target table area, and returning to the step of executing S120-S160 to obtain a new third suspected table set according to the table division electric quantity of each table division table after the final suspected ammeter is removed in the target table area in the preset history period and the total table electric quantity obtained by subtracting the theoretical electric quantity of the table division from the table area total table of the target table area.

S165: if the station partition table with the first average suspicion coefficient sorting in the new third suspicion partition table set is overlapped with the station partition table in the last third suspicion partition table set, judging whether the station partition table with the second average suspicion coefficient sorting in the new third suspicion partition table set is overlapped with the station partition table in the last third suspicion partition table set; if yes, go to S166; otherwise, S167 is executed.

S166: and taking the sorted second station distinguishing table as a final out-of-tolerance electric meter.

S167: and taking the sorted first station distinguishing table as a final out-of-tolerance electric meter.

S168: and if not, taking the final suspected ammeter as an out-of-tolerance ammeter.

In this embodiment, when the suspected electric meter stepwise compensation and elimination method is used to screen the district electric meters in the third suspected part list set, the first sorted district electric meter in the third suspected part list may be used as the final suspected electric meter, and the relative error of the branch electric meter of the final suspected electric meter is used to compensate the branch electric quantity of the final suspected electric meter, so as to obtain the theoretical electric quantity of the branch electric meter.

For example, in the present application, establishing a sub-table relative error solving equation based on the law of conservation of energy specifically includes:

wherein J is the number of table distinguishing tables; y (i) is the total electric quantity of the station area total table at the metering time i; x is a radical of a fluorine atom _j (i) Distinguishing the sub-meter electric quantity of the sub-meter at the metering time i for the jth station; e.g. of the type _j (i) For the jth stationThe sub-table relative error of the table at the metering time i; e.g. of the type _line (i) The line loss rate at the metering time i; e.g. of a cylinder _line (i) y (i) is the line loss electric quantity at the metering moment i and is in direct proportion to the total electric quantity of the distribution room total table; e.g. of a cylinder ₀ (i) Is the fixed loss at metering instant i.

After the final suspect ammeter is determined, the sub-ammeter electric quantity of the final suspect ammeter can be compensated according to the sub-ammeter relative error of the final suspect ammeter, and the sub-ammeter theoretical electric quantity obtained after compensation can be x _j (i)(1-e _j (i))。

After the sub-meter theoretical electric quantity is obtained, the final suspected ammeter can be removed from the target station area, the sub-meter theoretical electric quantity is subtracted from the station area total table, then, the line loss electric quantity of each station area table in the target station area at each moment in a preset historical time period can be recalculated, the line loss electric quantity mean value of each station area table in the preset historical time period is determined, whether the line loss electric quantity mean value is larger than a second line loss threshold value or not is judged, and if the line loss electric quantity mean value is not larger than the second line loss threshold value, the final suspected ammeter is used as an out-of-tolerance ammeter; if the average suspicion coefficient of each station partition table is larger than the average suspicion coefficient of each station partition table, the station partition table with the maximum average suspicion coefficient in each station partition table is used as a new first suspicion partition table set; then, screening the station partition tables in the new first suspicion table set through a preset line loss judgment mechanism to obtain a new second suspicion table set; then selecting the table partition table with the maximum absolute value of the table partition relative error in each table partition table to form a new first error table partition set according to the table partition relative error of each table partition table at each moment in a preset historical time period, and determining whether the new first error table partition set and the new second suspected table partition set have intersection or not; if no intersection exists, directly taking the district electric meters in the new second suspicion sub-meter set as out-of-tolerance electric meters; if the intersection exists, screening the station partition table with the intersection from the new second suspicion table set to form a new third suspicion table set; then, the application may determine whether the table section table with the first ranked average suspected coefficient in the new third suspected sub-table set coincides with the table section table in the last third suspected sub-table set, if no, the step of S161 to S168 is returned to after the first ranked table is taken as the final suspected electric table, if yes, it is continuously determined whether the table section table with the second ranked average suspected coefficient in the new third suspected sub-table set coincides with the table section table in the last third suspected sub-table set, if yes, the table section table with the second ranked average suspected coefficient is taken as the final out-of-tolerance electric table, otherwise, the table section table with the first ranked is taken as the final out-of-tolerance electric table.

In one embodiment, the determining the sub-table relative error of each table sub-table in the target table area at each moment in the preset history period in S110 may include:

s111: the method comprises the steps of obtaining first electric quantity data of a station area general table in a target station area in a preset historical time period and second electric quantity data of station area sub tables of which the electric quantity is larger than a preset electric quantity threshold value in the preset historical time period.

S112: and determining the relative sub-meter error of each station sub-meter at each moment in the preset historical time period according to the first electric quantity data and the second electric quantity data corresponding to each station sub-meter.

In this embodiment, when detecting an out-of-tolerance electric meter, in order to improve detection accuracy, first electric quantity data of the station area master meter in a preset history period and second electric quantity data of each station area meter in the preset history period may be combined to analyze, so that a sub-meter electric quantity of each station area meter at each moment in the preset history period may be obtained, and a line loss electric quantity of each station area meter may also be obtained.

In one embodiment, the obtaining, in S111, first electric quantity data of a station area total table in a target station area in a preset history period and second electric quantity data of a station area partial table in which each electric quantity of the partial table is greater than a preset electric quantity threshold in the preset history period may include:

s1110: first original data of a station area general table in a target station area in a preset historical time period and second original data of each station area table in the preset historical time period are determined.

S1111: and preprocessing the first original data and the second original data to obtain first electric quantity data and second electric quantity data, wherein the preprocessing operation comprises the steps of eliminating abnormal data, filling missing data, correcting clock deviation data and correcting user variable data by utilizing a user variable file.

S1112: and according to the second electric quantity data of each station partition table, removing the station partition table with the sub-table electric quantity not greater than a preset electric quantity threshold value in each station partition table to obtain the second electric quantity data of the station partition table with the sub-table electric quantity greater than the preset electric quantity threshold value in the preset historical time period.

In this embodiment, when determining the first electric quantity data of the station area summary table in the target station area in the preset history period, the first original data of the station area summary table in the preset history period may be obtained first, and then the first original data is subjected to a preprocessing operation, so that the first electric quantity data of the station area summary table in the preset history period may be obtained. When the second electric quantity data of the station division tables in the target station area, of which the electric quantity is greater than the preset electric quantity threshold, in the preset historical period is determined, the second original data of all the station division tables in the target station area in the preset historical period can be obtained first, and then after the second original data is preprocessed, the station division tables, of which the electric quantity is not greater than the preset electric quantity threshold, can be removed according to the second electric quantity data of each station division table, so that the second electric quantity data of the station division tables of which the electric quantity is greater than the preset electric quantity threshold in the preset historical period can be obtained.

Furthermore, in the prior art, when the out-of-tolerance electric meter is identified, the data quality of the measured data is not considered, and a comprehensive data repairing means is lacked, including the influence of factors such as data abnormity, missing, clock deviation, user variable files and the like, so that the accuracy of the final identification result is low. Therefore, before data analysis is performed by using the first original data and the second original data, when preprocessing operation can be performed on the first original data and the second original data, the preprocessing operation can include operations of removing abnormal data, filling missing data, correcting clock deviation data, correcting user variable data by using a user variable file, and the like, so that the identification accuracy of the ultra-poor electricity meter is effectively improved.

Specifically, when the first raw data and the second raw data are preprocessed, the station data of the target station area may be obtained first, where the station data may include an electricity meter user shift pattern, and a user shift indicates that a user belongs to the station area a before a certain time, but belongs to the station area B after the certain time, so that the affiliation relationship between users at different periods and the target station area may be cleared through the electricity meter user shift pattern, thereby assisting in correcting the total electric quantity of the station area total table in the target station area. Certainly, the station data of the present application may further include a station attribution area name, a station attribution line name, a station number, an electricity meter measuring point number, and the like, which is not limited herein.

After the station area data is obtained, abnormal data in the station area data, the first original data and the second original data can be removed by using a statistical discrimination method, wherein the statistical discrimination method has multiple methods, and the basic method is to give a confidence level (such as 95% and 99%), then find out a corresponding confidence interval, then judge data outside the confidence interval as abnormal values, and remove the abnormal values. According to the method and the device, the abnormal data in the platform area data, the first original data and the second original data can be removed in a mode of setting confidence intervals.

After the abnormal data is removed, missing data in the area data, the first original data and the second original data from which the abnormal data is removed can be filled, and a plurality of filling methods for the missing data are provided, and specifically, the filling methods for the missing data include: filling a fixed value, filling a mean value, filling a mode, filling a median, interpolating and filling, filling adjacent values before or after a missing position, establishing a regression model by taking a non-missing value as a characteristic to obtain a missing value and the like. According to the method and the device, any missing data filling method can be selected to fill missing data in the station area data, the first original data and the second original data after the abnormal data are removed.

Next, since the first original data in the present application may include not only the total table clock bias but also the total table electric quantity of the station area total table at each moment in the preset history period, the second original data may include not only the sub-table clock bias but also the sub-table electric quantity of each station area sub-table at each moment in the preset history period. Therefore, the method and the device can also correct the total electric quantity of the distribution area master table at each moment in the preset historical period and the sub-table electric quantity of each distribution area table at each moment in the preset historical period after the missing data is filled by utilizing the clock deviation of the master table and the clock deviation of the sub-table of each distribution area table, and second electric quantity data are obtained.

For example, the summary meter clock deviation and a certain sub-meter clock deviation can be compared with the standard time, when the comparison result shows that the station area summary meter is slower by 2 minutes and the station area sub-meter is faster by 5 minutes, the summary meter electric quantity at each moment of the station area summary meter in the preset historical period and the sub-meter electric quantity at each moment of the station area sub-meter in the preset historical period need to be subjected to time comparison again, otherwise, the summary meter data and the sub-meter data at the same moment cannot be in one-to-one correspondence.

Moreover, one station area has a plurality of users, and one station area only has one station area general table, and each user corresponds to one station area table. When a user changes, that is, a user belongs to the a station area before a certain time, but belongs to the B station area after the certain time, the sub-metering electric quantity of the user should be calculated into the total metering electric quantity of the a station area before the certain time, and the total metering electric quantity of the B station area after the certain time, if the user change relationship is not considered, a large error is introduced. Therefore, the corrected total electric quantity of the distribution room at each moment in the preset historical time period can be continuously corrected according to the electric meter user variable file in the distribution room data after the missing data is filled, and the final first electric quantity data can be obtained.

Furthermore, when removing the station partition tables with the electric quantity not greater than the preset electric quantity threshold value in each station partition table, the method can firstly determine the partition table electric quantity of each station partition table at each moment in the preset history period according to the second electric quantity data of each station partition table in the preset history period, then the method can take 1/K of the median of the partition table electric quantity of each station partition table at each moment in the preset history period as the small electric quantity threshold value at the moment, and at each moment, the station partition table with the electric quantity less than the small electric quantity threshold value at the moment in each station partition table is given a judgment value of 1, the rest of the station partition tables are given judgment values of 0, and finally, the average value of the sum of the judgment values of all the station partition tables at the preset history period is calculated, and the station partition table with the average value greater than the preset probability threshold value in each station partition table is taken as the station partition table not greater than the preset electric quantity threshold value to remove, so as to obtain the station partition table with the second electric quantity greater than the preset electric quantity threshold value of each station partition table in the preset history period.

It can be understood that, after the station partition table at each moment is given a judgment value of 1 or 0, the range of the average value of the sum of the judgment values of all the moments of each station partition table in the preset historical time period obtained by subsequent calculation is [0,1], the physical meaning of the average value is equal to the probability of the occurrence of the small electric quantity partition table, and when the probability of the occurrence of the small electric quantity partition table is higher, the deviation of the calculation result is higher, therefore, the value range of the preset probability threshold in the application can be set to [0.05,0.1], so as to control the probability of the occurrence of the small electric quantity partition table, and further, when the small electric quantity threshold is determined, the value range of K can be set according to the analysis, so that the identification accuracy of the super-difference electric meter is effectively improved.

In an embodiment, in S112, determining a sub-table relative error of each station partition table at each time in the preset history period according to the first electric quantity data and the second electric quantity data corresponding to each station partition table may include:

s1121: and carrying out time interval division on the first electric quantity data of the station area general table in the preset historical time interval and the second electric quantity data of each station division table in the preset historical time interval by using a sliding window with a preset length to obtain the total table electric quantity and the division table electric quantity in each time interval.

S1122: and establishing a sub-meter relative error solving equation set corresponding to the total meter electric quantity and the sub-meter electric quantity in a plurality of time intervals based on an energy conservation law, and solving the sub-meter relative error solving equation set to obtain a point estimation value of the quantity to be solved, wherein the quantity to be solved comprises the sub-meter relative error, the line loss rate and the fixed loss.

S1123: and determining the sub-table relative error of each station sub-table according to the point estimation value of the quantity to be solved.

In this embodiment, after obtaining first electric quantity data of a station total table in a target station area in a preset history period and second electric quantity data of station partition tables of which the electric quantity of each partition table is greater than a preset electric quantity threshold value in the preset history period, the present application may calculate a relative error of each station partition table at each moment in the preset history period and a confidence interval of the relative error of each partition table according to the first electric quantity data and the second electric quantity data corresponding to each station partition table, and judge a probability of meter out-of-tolerance according to the confidence interval of the relative error of each partition table, thereby obtaining a suspect meter.

Specifically, when calculating the relative error of each sub-meter at each moment in the preset history period of each sub-meter, the method may first set the size of the sliding window to be D (D is greater than or equal to (J + 2)), and then perform time interval division on the first electric quantity data of the table area summary table in the preset history period and the second electric quantity data of each table area sub-meter in the preset history period by using the sliding window, so as to obtain the total table electric quantity and the sub-meter electric quantity in each period. Then, a sub-table relative error solving equation set corresponding to the total sub-table electric quantity and the sub-table electric quantity in a plurality of time intervals can be established based on the energy conservation law, the sub-table relative error solving equation set is solved, point estimation values of the quantity to be solved are obtained, the quantity to be solved comprises the sub-table relative error, the line loss rate and the fixed loss, finally, the sub-table relative error of each station partition table can be determined according to the point estimation values of the quantity to be solved, and a confidence interval corresponding to the sub-table relative error of each station partition table is calculated by utilizing a T test in the significance test.

Further, establishing a sub-table relative error solving equation based on the energy conservation law specifically comprises:

wherein J is the number of table distinguishing tables; y (i) is the total electric quantity of the station area total table at the metering time i; x is the number of _j (i) Distinguishing the sub-meter electric quantity of the sub-meter at the metering time i for the jth station; e.g. of the type _j (i) Distinguishing a sub-table relative error of a sub-table at a metering time i for a jth station; e.g. of the type _line (i) The line loss rate at the metering time i; e.g. of a cylinder _line (i) y (i) is the line loss electric quantity at the metering moment i and is in direct proportion to the total electric quantity of the distribution room total table; e.g. of the type ₀ (i) Is the fixed loss at metering time i.

Because the first electric quantity data and the second electric quantity data are divided into D time intervals, a sub-table relative error solving equation set at D moments can be constructed through the sub-table relative error solving equation, in the solving equation, the total table electric quantity and the table area table electric quantity of a table area are known quantities, and the sub-table relative error, the line loss rate and the fixed loss are quantities to be solved, so that the matrix representation form of the multiple linear regression equation set is as follows:

Y＝XB (2)

wherein, Y is the electric quantity vector of the table area general table, Y = [ Y (1), Y (2) ], Y (D)] ^T (ii) a X is an electric quantity matrix of a table distinguishing table, and X = [ X (1), X (2) ], X (D)] ^T Wherein x (D) = [ x = ₁ (D),x ₂ (D),...,x _J (D),y(D),1](ii) a B is the quantity matrix to be calculated, B = [ B (1), B (2),. -, B (D)],B(D)＝[1-e ₁ (D),1-e ₂ (D),...,1-e _J (D),e _line (D),e ₀ (D)] ^T 。

The point estimation value of the quantity to be solved B obtained by matrix transformation is:

in one embodiment, in S120, determining, by using the line loss electric quantity, the sub-table electric quantity, and the sub-table relative error of each sub-table at each time in the preset history period, a sub-table with a largest average suspected coefficient in each sub-table, and using the sub-table with the largest average suspected coefficient as the first suspected sub-table set may include:

s121: and calculating confidence intervals corresponding to the relative errors of the sub-tables of each table zone table by utilizing the T test in the significance test, and calculating the average suspicion coefficient of each table zone table in the preset historical period according to the confidence interval corresponding to the relative error of the sub-table of each table zone table.

S122: and calculating the correlation between the line loss electric quantity and the sub-meter electric quantity of each partition table in the preset history period according to the line loss electric quantity and the sub-meter electric quantity of each partition table at each moment in the preset history period, and determining the average suspicion coefficient correction factor of each partition table in the preset history period by utilizing a correlation influence mechanism.

S123: and correcting the average suspicion coefficient of the corresponding table by using the average suspicion coefficient correction factor of each table, sorting the tables in a descending order according to the corrected average suspicion coefficient, and forming the table with the largest sorting into a first suspicion table set.

In this embodiment, when the first suspected table set is determined, the confidence interval corresponding to the relative error of the table of each station partition table may be calculated by using a T test in the significance test. Specifically, as can be seen from equation (3), the point estimate of the amount B to be determined

Influenced by the measured data of the electric quantity of the station main meter and the electric quantity of the station sub-meters due to the existence of the measured dataSystematic and random errors, point estimates of the quantity to be solved B

There is also an error. The confidence interval centered on the point estimation value can contain the point estimation value with a certain confidence, so that the confidence interval can be used for measuring the point estimation value

The error range of (2). Specifically, as seen from the T test in the significance test, at the confidence of (1-. Alpha.)

The confidence interval of (a) is:

wherein, T can be inquired in the T test critical value table through the confidence coefficient (1-alpha) and the degree of freedom (D-1) _α/2 Value of (a), s _D Calculated for multiple repetitions

Standard deviation of (d).

Then, from the equations (2) and (4), the confidence interval (θ) of the relative error is easily obtained _L ,θ _U ) Comprises the following steps:

since the confidence interval obtained in the present application is the confidence interval corresponding to the relative error of each table in each preset history period, when the average suspicion coefficient of each table in the preset history period is calculated, the suspicion coefficient of each table in each preset history period may be calculated first, and then the final average suspicion coefficient may be obtained according to the suspicion coefficient of each time.

Wherein, when calculating suspicion coefficient of each station partition table at each moment in the preset history period, the credibility range (theta) can be set according to the credibility range confirmation mechanism _L0 ,θ _U0 ) Then, calculating a corresponding suspicion coefficient, specifically, calculating a formula as follows:

the smaller the confidence range is set in the application, the higher the on-site precision ratio of the suspicion score table is. Of course, the confidence range may be set according to field experience, and is generally set to (-2,2). After the suspicion coefficient of each station partition table at each moment in the preset historical period is obtained, the sum of the suspicion coefficients of the corresponding station partition tables can be divided by the total amount moment to obtain the average suspicion coefficient.

Further, in order to avoid the situation that the calculation result of the relative errors of the sub-meters is inaccurate due to the influence of the correlation between the line loss electric quantity and the sub-meter electric quantity when the relative errors of the sub-meters of each sub-meter are solved, the correlation between the line loss electric quantity and the sub-meter electric quantity of each sub-meter in the preset history period can be calculated according to the line loss electric quantity and the sub-meter electric quantity of each sub-meter at each moment in the preset history period.

For example, when the line power loss of a certain partition table is negatively correlated with the power consumption of a certain partition table, the situation is consistent with the performance of power stealing behavior, so the average suspicion coefficient of the partition table should be increased, and at this time, the average suspicion coefficient correction factor of the partition table in a preset history period may be set to a value greater than 1; when the line loss electric quantity and the sub-meter electric quantity of a certain sub-meter are in negative correlation, the sub-meter relative error estimation is carried out on the sub-meter by utilizing a multivariate linear regression method or an improved algorithm thereof, the sub-meter electric quantity and the line loss electric quantity have the same position mathematically, so that the error can be shared between the sub-meter relative error and the line loss rate, the calculation results of the sub-meter relative error and the line loss rate are deviated, and the average suspicion coefficient of the sub-meter is reduced at the moment, for example, the average suspicion coefficient correction factor of the sub-meter in a preset historical period is set to be a numerical value smaller than 1.

Based on the above consideration, the calculation formula of the mean suspicion coefficient correction factor λ in the present application is as follows:

wherein c is the average suspicion coefficient of the station partition table, a ₁ 、a ₂ To correct the coefficient, 0<a ₁ <1，1<a ₂ <1.5, take a as an example ₁ ＝0.5，a ₂ ＝1.2。

The online detection device for the out-of-tolerance electric meters based on the suspected electric meter stepwise compensation rejection provided by the embodiment of the application is described below, and the online detection device for the out-of-tolerance electric meters based on the suspected electric meter stepwise compensation rejection described below and the online detection method for the out-of-tolerance electric meters based on the suspected electric meter stepwise compensation rejection described above can be referred to correspondingly.

In an embodiment, as shown in fig. 2, fig. 2 is a schematic structural diagram of an out-of-tolerance electric meter online detection apparatus based on suspected electric meter distribution compensation elimination according to an embodiment of the present application; the application also provides an online detection device for the out-of-tolerance electric meters based on the step-by-step compensation and rejection of the suspected electric meters, which can comprise a parameter determining module 210, a first suspected sub-meter determining module 220, a second suspected sub-meter determining module 230, a sub-meter judging module 240, a first out-of-tolerance electric meter determining module 250 and a second out-of-tolerance electric meter determining module 260, and specifically comprises the following components:

the parameter determining module 210 is configured to determine line loss electric quantity, sub-meter electric quantity, and a sub-meter relative error of each table in the target table area at each time within a preset history period.

The first suspicion sub-table determining module 220 is configured to determine, by using the line loss electric quantity, the sub-table electric quantity, and the sub-table relative error of each partition table at each time within the preset history period, a partition table with a largest average suspicion coefficient in each partition table, and use the partition table with the largest average suspicion coefficient as a first suspicion sub-table set.

And a second suspicion sublist determining module 230, configured to screen the partition tables in the first suspicion sublist set through a preset line loss judgment mechanism, so as to obtain a second suspicion sublist set.

The sub-table judging module 240 is configured to select, according to the sub-table relative error of each table in the preset historical time period at each moment, the table with the largest absolute value of the sub-table relative error in each table to form a first error sub-table set, and determine whether the first error sub-table set and the second suspected sub-table set have an intersection.

And a first out-of-tolerance electric meter determining module 250, configured to directly use the platform area electric meters in the second suspected sub-meter set as out-of-tolerance electric meters if there is no intersection.

And the second out-of-tolerance electric meter determining module 260 is used for screening the district electric meters with the intersection from the second suspected branch meter set to form a third suspected branch meter set if the intersection exists, and screening the district electric meters in the third suspected branch meter set by using a suspected electric meter step-by-step compensation elimination method to obtain the final out-of-tolerance electric meters.

In the above embodiment, when the smart meter is detected, the line loss electric quantity, the sub-meter electric quantity and the sub-meter relative error of each sub-meter in the target zone at each time within the preset history period may be determined, the zone meter with the largest average suspected coefficient in each zone sub-meter is determined by using the line loss electric quantity, the sub-meter electric quantity and the sub-meter relative error of each zone sub-meter at each time within the preset history period, and the zone meter with the largest average suspected coefficient is used as a first suspected sub-meter set obtained by performing comprehensive analysis by using the sub-meter relative error, the suspected coefficient and the line loss factor, so that the result is more objective and comprehensive; then, the method can further continue to screen the table division tables in the first suspected division table set through a preset line loss judgment mechanism to obtain a second suspected division table set, the suspected division tables in the set are more in line with actual conditions, finally, the method can further select the table division table with the largest absolute value of the relative errors of the division tables in each table division table to form a first error division table set according to the relative errors of the division tables at each moment in a preset historical time period, determine whether the first error division table set and the second suspected division table set intersect or not, and directly take the table division electric meters in the second suspected division table set as out-of-tolerance electric meters if the first error division table set and the second suspected division table set intersect; if the intersection exists, the station area tables with the intersection are screened out from the second suspected table set to form a third suspected table set, and the station area electric meters in the third suspected table set are screened by using a suspected electric meter step-by-step compensation and elimination method, so that the suspected coefficient space is further compressed, the finally obtained out-of-tolerance electric meters are more accurate and reliable, the process can reduce the workload of field detection personnel, the detection rate is improved, and the false detection rate is reduced.

In one embodiment, the present application further provides a storage medium, in which computer readable instructions are stored, and when executed by one or more processors, the one or more processors execute the steps of the suspected electric meter step compensation culling-based out-of-tolerance electric meter online detection method according to any one of the above embodiments.

In one embodiment, the present application further provides a computer device comprising: one or more processors, and a memory.

Fig. 3 is a schematic diagram illustrating an internal structure of a computer device according to an embodiment of the present disclosure, and the computer device 300 may be provided as a server. Referring to fig. 3, a computer device 300 includes a processing component 302, which further includes one or more processors, and memory resources, represented by memory 301, for storing instructions, such as applications, that are executable by the processing component 302. The application program stored in memory 301 may include one or more modules that each correspond to a set of instructions. Furthermore, the processing component 302 is configured to execute instructions to perform the suspected electricity meter step-by-step compensation culling based out-of-tolerance electricity meter online detection method of any of the embodiments described above.

The computer device 300 may also include a power component 303 configured to perform power management of the computer device 300, a wired or wireless network interface 304 configured to connect the computer device 300 to a network, and an input output (I/O) interface 305. The computer device 300 may operate based on an operating system stored in memory 301, such as Windows Server, mac OS XTM, unix, linux, free BSDTM, or the like.

Those skilled in the art will appreciate that the architecture shown in fig. 3 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.

In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, the embodiments may be combined as needed, and the same and similar parts may be referred to each other.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An online detection method for an out-of-tolerance electric meter based on suspected electric meter stepwise compensation rejection is characterized by comprising the following steps:

determining the station partition table with the largest average suspicion coefficient in each station partition table by using the line loss electric quantity, the partition table electric quantity and the partition table relative error of each station partition table at each moment in the preset historical time period, and taking the station partition table with the largest average suspicion coefficient as a first suspicion partition table set;

and if the intersection exists, screening the platform area tables with the intersection from the second suspected table set to form a third suspected table set, and screening the platform area electric meters in the third suspected table set by using a suspected electric meter step-by-step compensation elimination method to obtain the final out-of-tolerance electric meter.

2. The online detection method for the out-of-tolerance electric meters based on the suspected electric meters step-by-step compensation rejection as claimed in claim 1, wherein the step of screening the transformer area tables in the first suspected sub-table set through a preset line loss judgment mechanism to obtain a second suspected sub-table set comprises the following steps:

3. The online detection method for the out-of-tolerance electric meters based on the suspected electric meter step-by-step compensation elimination as claimed in claim 1, wherein the suspected electric meter step-by-step compensation elimination method is used for screening the district electric meters in the third suspected branch meter set to obtain the final out-of-tolerance electric meter, and the method comprises the following steps:

taking the sorted first district tables in the third suspicion table set as final suspicion electric meters, and compensating the sub-meter electric quantity of the final suspicion electric meters by using the sub-meter relative error of the final suspicion electric meters to obtain sub-meter theoretical electric quantity;

if the average suspicion coefficient of each subarea table in the target subarea is larger than the average suspicion coefficient of each subarea table in the target subarea, determining the subarea table with the maximum average suspicion coefficient in each subarea table according to the subarea electric quantity of each subarea table in the target subarea at each moment in a preset history period and the total table electric quantity of the target subarea after the theoretical electric quantity of each subarea is subtracted from the subarea total table of the target subarea, updating the subarea table in the target subarea at each moment in the preset history period and the subarea table in the target subarea, and returning to execute the step of determining the subarea table with the maximum average suspicion coefficient in each subarea table by using the line loss electric quantity, the subarea electric quantity and the subarea table relative error of each subarea table in the preset history period to obtain a new third suspicion subarea table set;

if yes, taking the sorted second station partition table as a final out-of-tolerance electric meter;

and if not, taking the final suspected ammeter as an out-of-tolerance ammeter.

4. The online detection method for the out-of-tolerance electric meters based on the suspected electric meters step-by-step compensation rejection as claimed in claim 1, wherein the determining of the relative error of the partial meters of each partial meter in the target area at each moment in a preset historical period comprises:

5. The online detection method for the extra-poor electricity meters based on the suspect electricity meter step-by-step compensation rejection of claim 4, wherein the step of obtaining the first electricity quantity data of the station area master table in the target station area in a preset historical time period and the second electricity quantity data of the station area branch tables of which the electricity quantity of each branch table is larger than a preset electricity quantity threshold in the preset historical time period comprises the following steps:

according to the second electric quantity data of each station division meter, the station division meters with the division meter electric quantities not larger than a preset electric quantity threshold value in each station division meter are removed, and the second electric quantity data of the station division meters with the division meter electric quantities larger than the preset electric quantity threshold value in the preset historical time period are obtained.

6. The suspected electricity meter step-by-step compensation elimination-based out-of-tolerance electricity meter online detection method according to claim 4, wherein the determining of the relative error of each partition table at each moment in the preset historical period according to the first electric quantity data and the second electric quantity data corresponding to each partition table comprises:

performing time interval division on the first electric quantity data of the station area master meter in the preset historical time interval and the second electric quantity data of each station division meter in the preset historical time interval by using a sliding window with a preset length to obtain the master meter electric quantity and the sub-meter electric quantity in each time interval;

7. The online detection method for the out-of-tolerance electric meters based on the suspected electric meters step-by-step compensation rejection as claimed in claim 1, wherein the step of determining the station partition table with the largest average suspicion coefficient in each station partition table by using the line loss electric quantity, the partition table electric quantity and the partition table relative error of each station partition table at each moment in the preset historical period and using the station partition table with the largest average suspicion coefficient as the first suspicion partition table set comprises:

calculating the correlation between the line loss electric quantity and the sub-meter electric quantity of each partition meter in the preset history period according to the line loss electric quantity and the sub-meter electric quantity of each partition meter at each moment in the preset history period, and determining an average suspicion coefficient correction factor of each partition meter in the preset history period by utilizing a correlation influence mechanism;

8. The utility model provides an out of tolerance ammeter on-line measuring device based on suspected ammeter substep compensation is rejected which characterized in that includes:

the parameter determination module is used for determining the line loss electric quantity, the sub-meter electric quantity and the sub-meter relative error of each sub-meter in the target station area at each moment in a preset historical time period;

the first suspicion sub-table determining module is used for determining a sub-table with the largest average suspicion coefficient in each sub-table by using the line loss electric quantity, the sub-table electric quantity and the sub-table relative error of each sub-table at each moment in the preset historical time period, and taking the sub-table with the largest average suspicion coefficient as a first suspicion sub-table set;

the second suspicion sub-table determining module is used for screening the station partition tables in the first suspicion sub-table set through a preset line loss judging mechanism to obtain a second suspicion sub-table set;

9. A storage medium, characterized by: the storage medium stores computer readable instructions, which when executed by one or more processors, cause the one or more processors to execute the steps of the suspected electric meter step-by-step compensation culling-based out-of-tolerance electric meter online detection method according to any one of claims 1 to 7.

10. A computer device, comprising: one or more processors, and a memory;

the memory stores computer readable instructions, which when executed by the one or more processors, perform the steps of the suspected electric meter step-by-step compensation culling based out-of-tolerance electric meter online detection method according to any one of claims 1 to 7.