CN110531303B - Batch fault early warning method and system for in-transit intelligent electric energy meters - Google Patents

Batch fault early warning method and system for in-transit intelligent electric energy meters Download PDF

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CN110531303B
CN110531303B CN201910695045.8A CN201910695045A CN110531303B CN 110531303 B CN110531303 B CN 110531303B CN 201910695045 A CN201910695045 A CN 201910695045A CN 110531303 B CN110531303 B CN 110531303B
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batch
fault
electric energy
energy meter
intelligent electric
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CN110531303A (en
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蒋超
曹宏宇
凌璐
刘春芳
汪亮
张翔宇
叶剑斌
李捷
徐石明
陈新贺
朱东升
张亮
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State Grid Corp of China SGCC
State Grid Heilongjiang Electric Power Co Ltd
NARI Group Corp
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State Grid Corp of China SGCC
State Grid Heilongjiang Electric Power Co Ltd
NARI Group Corp
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R35/00Testing or calibrating of apparatus covered by the other groups of this subclass
    • G01R35/04Testing or calibrating of apparatus covered by the other groups of this subclass of instruments for measuring time integral of power or current

Abstract

The invention discloses an on-the-spot intelligent electric energy meter batch fault early warning method and a system thereof, wherein the method comprises the following steps: dividing all the intelligent electric energy meters in operation into operation batches according to batch division rules; step 2: processing verification data and operation data of the intelligent electric energy meter; and step 3: calculating a historical fault rate and a field inspection misalignment rate according to a statistical period to serve as a batch fault early warning study and judgment triggering condition, if the historical fault rate and the field inspection misalignment rate reach or exceed a set threshold value, entering a step 4, and if the historical fault rate and the field inspection misalignment rate do not reach the set threshold value, returning to the step 2; and 4, step 4: outputting a batch fault judgment result; and 5: judging whether the running batch meets the batch fault judgment condition, and if so, entering the step 6; if not, returning to the step 2; step 6: and generating an intelligent electric energy meter operation batch fault disposal scheme. The advantages are that: the workload of verification personnel is reduced; resource waste is avoided, and investment is saved; on the premise of ensuring accurate and reliable measurement of the intelligent electric energy meter, resource saving and efficient utilization are ensured.

Description

Batch fault early warning method and system for in-transit intelligent electric energy meters
Technical Field
The invention relates to an in-transit intelligent electric energy meter batch fault early warning method and system, and belongs to the technical field of intelligent measurement.
Background
The national grid company has started to sign intelligent electric energy meters in large scale in 2010, and at present, 4.57 hundred million electric energy meters are in network hanging operation, including electric energy meters installed before 2010. Regarding the installation and use of the electric energy meter, the national grid company adopts the principle that the same arrival batch is installed within the latter half of the arrival date, the electric energy meter is hung on the grid to operate after the first forced verification is qualified, the electric energy meter is operated in a spot check in 1/3/5/7/8 th in the use process, and the electric energy meter is rotated due after the use period of 8 years expires.
With the progress of metering and informatization technology in China, the production process and the intelligence level of the electric energy meter are greatly improved. At present, the national power grid realizes the full coverage and full life cycle informatization real-time management and control of the intelligent electric energy meter, if a fixed verification cycle system is still executed, the disassembly and recovery verification of a large amount of intelligent electric energy meters with still good metering performance is carried out, huge verification and installation and debugging costs are required to be invested, and great resource waste is caused; meanwhile, the method is not suitable for the development requirements of metering technology progress, intelligent electric energy meter operation characteristics and green energy conservation in the new era.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a batch fault early warning method and a batch fault early warning system for an in-operation intelligent electric energy meter.
In order to solve the technical problem, the invention provides an in-transit intelligent electric energy meter batch early warning method which is characterized by comprising the following steps of:
step 1: dividing all the intelligent electric energy meters in operation into operation batches according to batch division rules;
step 2: extracting and counting the verification and inspection data and the operation data of the intelligent electric energy meter according to the operation batches of the intelligent electric energy meter, and calculating the number of equipment in the operation batches, the geographic distribution, the verification time and the installation time;
and step 3: calculating a historical fault rate and a field inspection misalignment rate according to a statistical period to serve as a batch fault early warning study and judgment triggering condition, if the historical fault rate and the field inspection misalignment rate reach or exceed a set threshold value, entering a step 4, and if the historical fault rate and the field inspection misalignment rate do not reach the set threshold value, returning to the step 2;
and 4, step 4: calculating each judgment item of the running batch, constructing a batch fault early warning model, calculating a bathtub curve and an inflection point interval of the running batch, and outputting a batch fault judgment result;
and 5: judging whether the running batch meets the batch fault judgment condition, and if so, entering the step 6; if not, returning to the step 2;
step 6: and generating an intelligent electric energy meter operation batch fault disposal scheme according to the batch fault judgment result.
Further, the run batches in step 1 are:
and under one bidding batch, the batches of the electric energy meters with the same supplier number, specification, arrival year, installation year, management unit, platform area number and rate type.
The supplier number, specification, arrival year, installation year and management unit are optional rules; the number of the platform area and the type of the rate are optional rules.
Further, the verification and inspection data in the step 2 comprise the number of equipment, the year of arrival, verification time and the number of faults; the operation data comprises installation time, field inspection quantity and inspection out-of-tolerance quantity.
Further, in step 3:
the historical fault rate is equal to the quantity of the batch historical fault table/the total quantity of the batch equipment multiplied by 100 percent;
the field inspection misalignment rate is equal to the number of unqualified tables for field inspection/total number of batch equipment multiplied by 100%.
Further, in step 4:
the judging items comprise first inspection error normal distribution curves, fault dismantling numbers, sorting detection unqualified numbers, sorting detection qualified numbers, field inspection unqualified numbers, operation error calculation result normal distribution curves, historical early warning numbers and grade evaluation result distribution of all equipment details under the operation batch;
the input characteristics of the batch fault early warning model comprise the running time of the electric energy meter, the model, the manufacturing unit, the chip manufacturer, the trip mode of the card meter, the voltage, the wiring mode, the overload multiple, the metering abnormal event, the acquisition abnormal event and the first acquisition abnormal operation days of the electric energy meter, and the output characteristics comprise the batch prediction fault rate of each calculation period and the corresponding batch prediction fault equipment details.
Further, the batch fault determination conditions in the step 5 include fault hidden dangers, determination conclusions and determination bases, wherein the fault hidden dangers include appearance faults, metering faults and equipment faults.
Further, the intelligent electric energy meter operation batch fault disposal scheme in the step 6 comprises an intelligent electric energy meter fault batch equipment list and an electric energy meter batch fault replacement plan;
the intelligent electric energy meter fault batch equipment list comprises an operation batch number, a bar code, an equipment code, a communication mode, a chip manufacturer, an electric energy meter specification, a station area number, a station area name and a power supply unit;
the electric energy meter batch fault replacement plan comprises a planned year and month, a wiring mode, a planned quantity, a finished quantity, a maker, a making date, a making unit and equipment specifications.
An in-transit intelligent electric energy meter batch early warning system is characterized by comprising an operation batch dividing module, an intelligent electric energy meter data processing module, a first judging module, a batch fault early warning studying and judging module, a second judging module and a batch fault early warning handling module;
the operation batch dividing module is used for dividing all the intelligent electric energy meters in operation into operation batches according to a batch dividing rule;
the intelligent electric energy meter data processing module is used for extracting and counting verification data and operation data of the intelligent electric energy meter according to the operation batches of the intelligent electric energy meter, and calculating the number of equipment in the operation batches, the geographic distribution, the verification time and the installation time;
the first judgment module is used for calculating a historical fault rate and a field inspection misalignment rate according to a statistical period to serve as batch fault early warning research and judgment triggering conditions, if the historical fault rate and the field inspection misalignment rate reach or exceed a set threshold value, the batch fault early warning research and judgment module is started, and if the historical fault rate and the field inspection misalignment rate do not reach the set threshold value, the batch fault early warning research and judgment module returns to the intelligent electric energy meter data processing module;
the batch fault early warning research and judgment module is used for calculating each research and judgment item of the running batch, constructing a batch fault early warning model, calculating a bathtub curve and an inflection point interval of the running batch, and outputting a batch fault judgment result;
the second judging module is used for judging whether the running batch meets the batch fault judging condition or not, and if the running batch meets the batch fault judging condition, the running batch enters the batch fault early warning processing module; if not, returning to the intelligent electric energy meter data processing module;
the batch fault early warning processing module is used for generating an intelligent electric energy meter operation batch fault processing scheme according to the batch fault judgment result.
Further, the operation batches divided by the operation batch dividing module are electric energy meter batches with the same supplier number, specification, arrival year, installation year, management unit, platform area number and rate type under one bidding batch;
the verification and inspection data processed by the intelligent electric energy meter data processing module comprise equipment quantity, arrival year, verification time and fault quantity; the operation data comprises installation time, field inspection quantity and inspection out-of-tolerance quantity;
the historical fault rate in the first judging module is equal to the quantity of the batch historical fault table/the total quantity of the batch equipment multiplied by 100 percent; the field inspection misalignment rate is equal to the number of unqualified tables for field inspection/total number of batch equipment multiplied by 100%.
Further, the study items in the batch fault early warning study module comprise detailed first inspection error normal distribution curves, fault dismantling numbers, sorting detection unqualified numbers, sorting detection qualified numbers, field inspection unqualified numbers, operation error calculation result normal distribution curves, historical early warning numbers and grade evaluation result distribution of all equipment in the operation batch; the input characteristics of the batch fault early warning model comprise the running time of the electric energy meter, the model, the manufacturing unit, the chip manufacturer, the trip mode of the card meter, the voltage, the wiring mode, the overload multiple, the metering abnormal event, the acquisition abnormal event and the first acquisition abnormal running days of the electric energy meter; the output characteristics of the batch fault early warning model comprise the batch prediction fault rate of each calculation period and the corresponding batch prediction fault equipment details, wherein each calculation period is defaulted to be a month;
the batch fault judgment conditions in the second judgment module comprise fault hidden dangers, judgment conclusions and judgment bases, wherein the fault hidden dangers comprise appearance faults, metering faults and equipment faults;
the intelligent electric energy meter operation batch fault handling scheme in the batch fault early warning handling module comprises an intelligent electric energy meter fault batch equipment list and an electric energy meter batch fault replacement plan; the intelligent electric energy meter fault batch equipment list comprises an operation batch number, a bar code, an equipment code, a communication mode, a chip manufacturer, an electric energy meter specification, a station area number, a station area name and a power supply unit; the electric energy meter batch fault replacement plan comprises a planned year and month, a wiring mode, a planned quantity, a finished quantity, a maker, a making date, a making unit and equipment specifications.
The invention achieves the following beneficial effects:
1) the traditional sampling verification mode is replaced by online fault early warning, so that the workload of verification personnel is reduced;
2) the batch fault early warning conclusion is obtained through the batch fault early warning system, and compared with the original forced alternation mode, the batch fault early warning conclusion avoids resource waste and saves investment;
3) the method realizes the on-line monitoring, accurate study and judgment and state replacement of the intelligent electric energy meter, and ensures social resource conservation and efficient utilization on the premise of ensuring accurate and reliable metering of the intelligent electric energy meter.
Drawings
FIG. 1 is a process of batch fault early warning of an in-service intelligent electric energy meter;
FIG. 2 is a schematic diagram of a batch fault warning functional physical architecture;
FIG. 3 is an error normal distribution curve of the intelligent electric energy meter;
FIG. 4 is a schematic diagram of a batch fault early warning model.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
An in-service intelligent electric energy meter batch fault early warning method is shown in fig. 1 and comprises the following steps:
step 1: dividing all the intelligent electric energy meters in operation into operation batches according to batch division rules;
the running batch refers to the batch of the electric energy meters with the same supplier number, specification, arrival year, installation year, management unit, platform area number and rate type under one bidding batch, namely the running batch of the intelligent electric energy meters.
Supplier number, specification, arrival year, installation year and management unit are optional rules; the number of the platform area and the type of the rate are optional rules.
Step 2: and extracting and counting the verification and inspection data and the operation data of the intelligent electric energy meter according to the operation batches of the intelligent electric energy meter, and calculating the number of equipment in the operation batches, the geographic distribution, the verification time, the installation time and the like.
The verification test data comprises the number of equipment, the year of arrival, verification time and the number of faults; the operation data comprises installation time, field inspection quantity and inspection out-of-tolerance quantity.
And step 3: calculating a historical fault rate and a field inspection misalignment rate according to a statistical period to serve as batch fault early warning research and judgment triggering conditions, and entering a step 4 of batch fault early warning research and judgment link if a set threshold is reached or exceeded; if not, returning to the step 2, and continuing to extract and calculate;
wherein, the historical fault rate is equal to the quantity of the batch historical fault table/the total quantity of the batch equipment multiplied by 100 percent; the historical failure rate threshold defaults to 8%, and can be modified.
The field inspection misalignment rate is equal to the number of unqualified tables for field inspection/the total number of batch equipment multiplied by 100 percent; the field test misalignment rate threshold is modified by default to 5%.
And 4, step 4: calculating each judgment item of the running batch, constructing a batch fault early warning model, calculating a bathtub curve and an inflection point interval of the running batch, and outputting a batch fault judgment result;
the operating batch judging items comprise first inspection error normal distribution curves, fault dismantling numbers, sorting and detecting unqualified numbers, sorting and detecting qualified numbers, field inspection unqualified numbers, operating error calculation result normal distribution curves, historical early warning numbers and grade evaluation result distribution of all equipment details under the operating batch.
The input characteristics comprise the running time of the electric energy meter, the model, the manufacturing unit, the chip manufacturer, the trip mode of the card meter, the voltage, the wiring mode, the overload multiple, the metering abnormal event, the acquisition abnormal event and the first acquisition abnormal operation days of the electric energy meter.
The output characteristics include the batch predicted failure rate and corresponding batch predicted failure device details for each calculation cycle, wherein each calculation cycle defaults to a month.
And 5: judging whether the running batch meets batch fault judgment conditions, and if so, entering a step 6 of batch fault early warning handling; if not, returning to the step 2.
The batch fault judgment conditions comprise fault hidden dangers, judgment conclusions and judgment bases. The fault hidden danger comprises appearance faults, metering faults and equipment faults.
Step 6: and generating an intelligent electric energy meter operation batch fault disposal scheme according to the batch fault judgment result.
The intelligent electric energy meter operation batch fault disposal scheme comprises an intelligent electric energy meter fault batch equipment list and an electric energy meter batch fault replacement plan.
The intelligent electric energy meter fault batch equipment list comprises an operation batch number, a bar code, an equipment code, a communication mode, a chip manufacturer, an electric energy meter specification, a station area number, a station area name and a power supply unit.
The electric energy meter batch fault replacement plan comprises a planned year and month, a wiring mode, a planned quantity, a finished quantity, a maker, a making date, a making unit and equipment specifications.
An in-transit intelligent electric energy meter batch early warning system comprises an in-transit batch dividing module, an intelligent electric energy meter data processing module, a first judging module, a batch fault early warning studying and judging module, a second judging module and a batch fault early warning processing module;
the operation batch dividing module is used for dividing all the intelligent electric energy meters in operation into operation batches according to a batch dividing rule;
the intelligent electric energy meter data processing module is used for extracting and counting verification data and operation data of the intelligent electric energy meter according to the operation batches of the intelligent electric energy meter, and calculating the number of equipment in the operation batches, the geographic distribution, the verification time and the installation time;
the first judgment module is used for calculating a historical fault rate and a field inspection misalignment rate according to a statistical period to serve as batch fault early warning research and judgment triggering conditions, if the historical fault rate and the field inspection misalignment rate reach or exceed a set threshold value, the batch fault early warning research and judgment module is started, and if the historical fault rate and the field inspection misalignment rate do not reach the set threshold value, the batch fault early warning research and judgment module returns to the intelligent electric energy meter data processing module;
the batch fault early warning research and judgment module is used for calculating each research and judgment item of the running batch, constructing a batch fault early warning model, calculating a bathtub curve and an inflection point interval of the running batch, and outputting a batch fault judgment result;
the second judging module is used for judging whether the running batch meets the batch fault judging condition or not, and if the running batch meets the batch fault judging condition, the running batch enters the batch fault early warning processing module; if not, returning to the intelligent electric energy meter data processing module;
the batch fault early warning processing module is used for generating an intelligent electric energy meter operation batch fault processing scheme according to the batch fault judgment result.
The operation batches divided by the operation batch dividing module are electric energy meter batches with the same supplier number, specification, arrival year, installation year, management unit, platform area number and rate type under one bidding batch;
the verification and inspection data processed by the intelligent electric energy meter data processing module comprise equipment quantity, arrival year, verification time and fault quantity; the operation data comprises installation time, field inspection quantity and inspection out-of-tolerance quantity;
the historical fault rate in the first judging module is equal to the quantity of the batch historical fault table/the total quantity of the batch equipment multiplied by 100 percent; the field inspection misalignment rate is equal to the number of unqualified tables for field inspection/total number of batch equipment multiplied by 100%.
The study items in the batch fault early warning study module comprise detailed first inspection error normal distribution curves, fault dismantling number, sorting detection unqualified number, sorting detection qualified number, field inspection unqualified number, operation error calculation result normal distribution curves, historical early warning number and grade evaluation result distribution of all equipment in the operation batch; the input characteristics of the batch fault early warning model comprise the running time of the electric energy meter, the model, the manufacturing unit, the chip manufacturer, the trip mode of the card meter, the voltage, the wiring mode, the overload multiple, the metering abnormal event, the acquisition abnormal event and the first acquisition abnormal running days of the electric energy meter; the output characteristics of the batch fault early warning model comprise the batch prediction fault rate of each calculation period and the corresponding batch prediction fault equipment details, wherein each calculation period is defaulted to be a month;
the batch fault judgment conditions in the second judgment module comprise fault hidden dangers, judgment conclusions and judgment bases, wherein the fault hidden dangers comprise appearance faults, metering faults and equipment faults;
the intelligent electric energy meter operation batch fault handling scheme in the batch fault early warning handling module comprises an intelligent electric energy meter fault batch equipment list and an electric energy meter batch fault replacement plan; the intelligent electric energy meter fault batch equipment list comprises an operation batch number, a bar code, an equipment code, a communication mode, a chip manufacturer, an electric energy meter specification, a station area number, a station area name and a power supply unit; the electric energy meter batch fault replacement plan comprises a planned year and month, a wiring mode, a planned quantity, a finished quantity, a maker, a making date, a making unit and equipment specifications.
In order to verify the usability and stability of the method of the present invention, and to apply the method to the batch replacement of the smart meters, the present embodiment is described by taking the following data as an example. The in-transit intelligent electric energy meter batch fault early warning system provided by the invention is deployed in a provincial metering center, as shown in fig. 2. The method selects a certain province on-line electric energy meter for analysis, firstly divides the on-line intelligent electric energy meter into a plurality of batches according to the batch division rule, and as shown in table 1:
TABLE 1
And then, calculating and analyzing the intelligent electric energy meter in operation by taking the operation batch as a dimension to obtain a first inspection error normal distribution curve, as shown in fig. 3. And extracting electric energy meter data according to the characteristic values input by the batch fault early warning model, wherein the electric energy meter data comprises the operation duration, the model, the manufacturing unit, a chip manufacturer, a card meter tripping mode, voltage, a wiring mode, an overload multiple, a metering abnormal event, an acquisition abnormal event and the first acquisition abnormal electric energy meter operation days. Then, using PCA (principal component analysis) to perform dimensionality reduction on the feature data:
for each feature, the average of the dimensional feature is subtracted from the value of the current feature. For the jth feature of the ith sample, the calculation formula is:
wherein the mean of the ith feature is:
wherein n is the number of samples;
the covariance matrix is:
the size of the covariance matrix is m × m, and m is a characteristic dimension;
the covariance calculation formula is as follows:
cov(x1,x2)=E[(x1-E(x1))(x2-E(x2))]=E(x1x2)-E(x1)E(x2) For weighing the 1 st sample x1With 2 nd sample x2E denotes the desired value;
calculating an eigenvector of the covariance matrix and a corresponding eigenvalue:
and e, taking u as λ u, wherein λ is an eigenvalue and u is an eigenvector.
Finally, training is performed in the batch fault early warning model shown in fig. 4, and a prediction result is obtained and is used as a reference for batch fault study and judgment, as shown in table 2.
The invention establishes a batch fault early warning process of the intelligent electric energy meters and designs a batch fault early warning scheme of the intelligent electric energy meters. The batch state of the intelligent electric energy meters in operation is analyzed and evaluated by adopting a machine learning algorithm through extracting the online monitoring data of the intelligent electric energy meters and disassembling the sorting detection data. According to the embodiment, the model prediction result is combined with the historical occurrence data such as the first inspection error normal distribution curve, the fault dismantling number, the sorting detection unqualified number, the sorting detection qualified number, the field inspection unqualified number, the operation error calculation result normal distribution curve, the historical early warning number and the like to perform comprehensive judgment, and the reliability of batch fault early warning is further improved on the basis that the model prediction is accurate and available.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.

Claims (8)

1. An in-transit intelligent electric energy meter batch early warning method is characterized by comprising the following steps:
step 1: dividing all the intelligent electric energy meters in operation into operation batches according to batch division rules;
step 2: extracting and counting the verification and inspection data and the operation data of the intelligent electric energy meter according to the operation batches of the intelligent electric energy meter, and calculating the number of equipment in the operation batches, the geographic distribution, the verification time and the installation time;
and step 3: calculating a historical fault rate and a field inspection misalignment rate according to a statistical period to serve as a batch fault early warning study and judgment triggering condition, if the historical fault rate and the field inspection misalignment rate reach or exceed a set threshold value, entering a step 4, and if the historical fault rate and the field inspection misalignment rate do not reach the set threshold value, returning to the step 2;
and 4, step 4: calculating each judgment item of the running batch, constructing a batch fault early warning model, calculating a bathtub curve and an inflection point interval of the running batch, and outputting a batch fault judgment result;
the judging items comprise first inspection error normal distribution curves, fault dismantling numbers, sorting detection unqualified numbers, sorting detection qualified numbers, field inspection unqualified numbers, operation error calculation result normal distribution curves, historical early warning numbers and grade evaluation result distribution of all equipment details under the operation batch;
the input characteristics of the batch fault early warning model comprise the running time of the electric energy meter, the model, the manufacturing unit, the chip manufacturer, the trip mode of the card meter, the voltage, the wiring mode, the overload multiple, the metering abnormal event, the acquisition abnormal event and the first acquisition abnormal operation days of the electric energy meter, and the output characteristics comprise the batch prediction fault rate of each calculation period and the corresponding batch prediction fault equipment details;
and 5: judging whether the running batch meets the batch fault judgment condition, and if so, entering the step 6; if not, returning to the step 2;
step 6: and generating an intelligent electric energy meter operation batch fault disposal scheme according to the batch fault judgment result.
2. The in-transit intelligent electric energy meter batch early warning method according to claim 1, wherein the operation batch in the step 1 is as follows:
under one bidding batch, the batches of the electric energy meters with the same supplier number, specification, arrival year, installation year, management unit, platform area number and rate type;
the supplier number, specification, arrival year, installation year and management unit are optional rules; the number of the platform area and the type of the rate are optional rules.
3. The in-transit intelligent electric energy meter batch early warning method as claimed in claim 1, wherein the verification test data in step 2 comprises equipment number, year of arrival, verification time and fault number; the operation data comprises installation time, field inspection quantity and inspection out-of-tolerance quantity.
4. The in-transit intelligent electric energy meter batch early warning method according to claim 1, wherein in step 3:
historical failure rate = number of batch historical failure tables/total number of batch devices × 100%;
field inspection misalignment = number of field inspection rejected tables/total number of batch devices × 100%.
5. The in-transit intelligent electric energy meter batch early warning method according to claim 1, wherein the batch fault judgment conditions in the step 5 comprise fault hidden dangers, judgment conclusions and judgment bases, wherein the fault hidden dangers comprise appearance faults, metering faults and equipment faults.
6. The in-transit intelligent electric energy meter batch early warning method according to claim 1, wherein the intelligent electric energy meter operation batch fault handling scheme in step 6 comprises an intelligent electric energy meter fault batch equipment list and an electric energy meter batch fault replacement plan;
the intelligent electric energy meter fault batch equipment list comprises an operation batch number, a bar code, an equipment code, a communication mode, a chip manufacturer, an electric energy meter specification, a station area number, a station area name and a power supply unit;
the electric energy meter batch fault replacement plan comprises a planned year and month, a wiring mode, a planned quantity, a finished quantity, a maker, a making date, a making unit and equipment specifications.
7. An in-transit intelligent electric energy meter batch early warning system is characterized by comprising an operation batch dividing module, an intelligent electric energy meter data processing module, a first judging module, a batch fault early warning studying and judging module, a second judging module and a batch fault early warning handling module;
the operation batch dividing module is used for dividing all the intelligent electric energy meters in operation into operation batches according to a batch dividing rule;
the intelligent electric energy meter data processing module is used for extracting and counting verification data and operation data of the intelligent electric energy meter according to the operation batches of the intelligent electric energy meter, and calculating the number of equipment in the operation batches, the geographic distribution, the verification time and the installation time;
the first judgment module is used for calculating a historical fault rate and a field inspection misalignment rate according to a statistical period to serve as batch fault early warning research and judgment triggering conditions, if the historical fault rate and the field inspection misalignment rate reach or exceed a set threshold value, the batch fault early warning research and judgment module is started, and if the historical fault rate and the field inspection misalignment rate do not reach the set threshold value, the batch fault early warning research and judgment module returns to the intelligent electric energy meter data processing module;
the batch fault early warning research and judgment module is used for calculating each research and judgment item of the running batch, constructing a batch fault early warning model, calculating a bathtub curve and an inflection point interval of the running batch, and outputting a batch fault judgment result;
the second judging module is used for judging whether the running batch meets the batch fault judging condition or not, and if the running batch meets the batch fault judging condition, the running batch enters the batch fault early warning processing module; if not, returning to the intelligent electric energy meter data processing module;
the batch fault early warning processing module is used for generating an intelligent electric energy meter operation batch fault processing scheme according to a batch fault judgment result;
the study items in the batch fault early warning study module comprise detailed first inspection error normal distribution curves, fault dismantling number, sorting detection unqualified number, sorting detection qualified number, field inspection unqualified number, operation error calculation result normal distribution curves, historical early warning number and grade evaluation result distribution of all equipment in the operation batch; the input characteristics of the batch fault early warning model comprise the running time of the electric energy meter, the model, the manufacturing unit, the chip manufacturer, the trip mode of the card meter, the voltage, the wiring mode, the overload multiple, the metering abnormal event, the acquisition abnormal event and the first acquisition abnormal running days of the electric energy meter; the output characteristics of the batch fault early warning model comprise the batch prediction fault rate of each calculation period and the corresponding batch prediction fault equipment details, wherein each calculation period defaults to a month;
the batch fault judgment conditions in the second judgment module comprise fault hidden dangers, judgment conclusions and judgment bases, wherein the fault hidden dangers comprise appearance faults, metering faults and equipment faults;
the intelligent electric energy meter operation batch fault handling scheme in the batch fault early warning handling module comprises an intelligent electric energy meter fault batch equipment list and an electric energy meter batch fault replacement plan; the intelligent electric energy meter fault batch equipment list comprises an operation batch number, a bar code, an equipment code, a communication mode, a chip manufacturer, an electric energy meter specification, a station area number, a station area name and a power supply unit; the electric energy meter batch fault replacement plan comprises a planned year and month, a wiring mode, a planned quantity, a finished quantity, a maker, a making date, a making unit and equipment specifications.
8. The in-transit intelligent electric energy meter batch early warning system as claimed in claim 7, wherein the operation batch divided by the operation batch dividing module is an electric energy meter batch with the same supplier number, specification, arrival year, installation year, management unit, platform area number and rate type under one bidding batch;
the verification and inspection data processed by the intelligent electric energy meter data processing module comprise equipment quantity, arrival year, verification time and fault quantity; the operation data comprises installation time, field inspection quantity and inspection out-of-tolerance quantity;
the historical fault rate = the number of the batch historical fault tables/the total number of the batch devices × 100% in the first judging module; field inspection misalignment = number of field inspection rejected tables/total number of batch devices × 100%.
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CN111998972B (en) * 2020-09-17 2021-07-16 南方电网科学研究院有限责任公司 Terminal block temperature drastic change alarm function detection method, device, terminal and medium
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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104898084A (en) * 2015-06-19 2015-09-09 国网重庆市电力公司电力科学研究院 Electric energy metering device operation state monitoring method
CN105022019A (en) * 2015-06-23 2015-11-04 国家电网公司 Method of comprehensively estimating reliability of single-phase intelligent electric energy meter
CN205643673U (en) * 2015-12-08 2016-10-12 广州供电局有限公司 Metering device scraps alarm device based on measurement instrument follow -up of quality evaluation system
CN106842101A (en) * 2015-12-03 2017-06-13 中国电力科学研究院 A kind of evaluation method of electric energy meter running status
CN108830437A (en) * 2018-04-09 2018-11-16 国电南瑞科技股份有限公司 A kind of appraisal procedure for intelligent electric energy meter operation
CN109190957A (en) * 2018-08-23 2019-01-11 国网天津市电力公司电力科学研究院 A kind of intelligent electric energy meter O&M replacing options and device based on online overall merit
CN109298374A (en) * 2018-10-29 2019-02-01 中国电力科学研究院有限公司 A kind of electric energy table status on-line evaluation method and system

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104898084A (en) * 2015-06-19 2015-09-09 国网重庆市电力公司电力科学研究院 Electric energy metering device operation state monitoring method
CN105022019A (en) * 2015-06-23 2015-11-04 国家电网公司 Method of comprehensively estimating reliability of single-phase intelligent electric energy meter
CN106842101A (en) * 2015-12-03 2017-06-13 中国电力科学研究院 A kind of evaluation method of electric energy meter running status
CN205643673U (en) * 2015-12-08 2016-10-12 广州供电局有限公司 Metering device scraps alarm device based on measurement instrument follow -up of quality evaluation system
CN108830437A (en) * 2018-04-09 2018-11-16 国电南瑞科技股份有限公司 A kind of appraisal procedure for intelligent electric energy meter operation
CN109190957A (en) * 2018-08-23 2019-01-11 国网天津市电力公司电力科学研究院 A kind of intelligent electric energy meter O&M replacing options and device based on online overall merit
CN109298374A (en) * 2018-10-29 2019-02-01 中国电力科学研究院有限公司 A kind of electric energy table status on-line evaluation method and system

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