CN114236448A - Metering device troubleshooting system based on big data - Google Patents
Metering device troubleshooting system based on big data Download PDFInfo
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- 238000013024 troubleshooting Methods 0.000 title claims description 13
- 238000012544 monitoring process Methods 0.000 claims abstract description 25
- 238000012423 maintenance Methods 0.000 claims abstract description 22
- 238000012545 processing Methods 0.000 claims abstract description 17
- 238000007619 statistical method Methods 0.000 claims abstract description 12
- 238000003745 diagnosis Methods 0.000 claims abstract description 10
- 230000000007 visual effect Effects 0.000 claims abstract description 9
- 238000004458 analytical method Methods 0.000 claims abstract description 5
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- 238000013528 artificial neural network Methods 0.000 claims description 3
- 238000007477 logistic regression Methods 0.000 claims description 3
- 238000003672 processing method Methods 0.000 claims description 3
- 230000000306 recurrent effect Effects 0.000 claims description 3
- 238000007405 data analysis Methods 0.000 abstract description 6
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Abstract
The invention provides a metering device fault maintenance system based on big data, which relates to the technical field of metering device fault maintenance, and comprises a data acquisition module, an operation monitoring module, a fault processing module, a statistical query module and a visual display module; the data acquisition module is used for accessing a cloud platform server of a power grid company to acquire data; the operation monitoring module is used for analyzing and mining data to realize fault on-line monitoring and intelligent diagnosis and analysis of various metering devices; the fault processing module is used for alarming faults of the metering device and automatically generating a fault processing work order; the statistical query module is used for performing statistical analysis and query on the fault condition of the metering device; the visual display module is used for visually displaying the results of the statistical analysis and the query; the accuracy and timeliness of fault monitoring of the metering device are improved by means of big data analysis, and the existing maintenance mode of the metering device is optimized.
Description
Technical Field
The invention belongs to the technical field of metering device fault maintenance, and particularly relates to a metering device fault maintenance system based on big data.
Background
Along with the development of the intelligent power grid, the working requirements on the online monitoring system of the metering device and the synchronous line loss control are continuously improved, and higher requirements are provided for the accuracy and timeliness of fault monitoring and maintenance of the metering device in the transformer substation.
The current metering device fault monitoring is mainly based on monitoring abnormal data in an electricity utilization acquisition system, the data source is single, judgment of the metering accuracy of the electric energy metering device is difficult to achieve, in the actual work, workers need to manually inquire the metering acquisition data of the electricity utilization acquisition system and data in other systems such as a scheduling system and the like for contrastive analysis, time and labor are wasted, faults often can be monitored after several days, the real-time performance is poor, the operation state evaluation of the metering device, the rapid study and judgment of the metering faults and the like are difficult to achieve, and the requirement of management lean is not met.
In view of this, the present invention provides a metering device troubleshooting system based on big data, which is very necessary to solve the deficiencies of the prior art.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a metering device fault maintenance system based on big data, which improves the accuracy and timeliness of the fault monitoring of the metering device by using a big data analysis means, establishes an automatic fault real-time alarm mechanism, optimizes the maintenance mode of the existing metering device and ensures the safe and stable operation of the metering device.
In order to achieve the purpose, the invention provides the following technical scheme:
a big data-based metering device fault maintenance system comprises a data acquisition module, an operation monitoring module, a fault processing module, a statistical query module and a visual display module;
the data acquisition module is used for accessing a cloud platform server of a power grid company to acquire data;
the operation monitoring module is used for analyzing and mining data to realize fault on-line monitoring and intelligent diagnosis and analysis of various metering devices;
the fault processing module is used for alarming faults of the metering device and automatically generating a fault processing work order;
the statistical query module is used for performing statistical analysis and query on the fault condition of the metering device;
and the visual display module is used for visually displaying the results of the statistical analysis and the query.
Preferably, the system further comprises a file management module, wherein the file management module is used for managing basic information of the metering device, corresponding relation between the metering device and a line and topological information of the transformer substation and providing a data base.
Preferably, the system further includes a distributed storage module, where the distributed storage module is provided with a plurality of storage nodes for storing the data acquired by the data acquisition module.
Preferably, the data acquisition module comprises a first data acquisition unit and a second data acquisition unit;
the first data acquisition unit is used for acquiring electric parameter data in the electricity utilization acquisition system;
the second data acquisition unit is used for acquiring the electrical parameter data in the dispatching cloud system;
the data acquisition module is accessed to a cloud platform server of a power grid company through an API (application program interface) interface to acquire electric parameter data in the power utilization acquisition system and the dispatching cloud system, and information barriers among different systems are broken.
Preferably, the operation monitoring module comprises a preprocessing unit, a fault judging unit, a fault marking unit and a fault diagnosis unit;
the preprocessing unit is used for preprocessing the acquired data;
the fault judging unit is used for setting a logistic regression algorithm to analyze the data and judging whether the metering device has a fault or not;
the fault marking unit is used for marking the metering device with faults and acquiring log data and hardware operation data of the metering device;
and the fault diagnosis unit is used for setting a recurrent neural network algorithm to analyze the log data and the hardware operation data and judging the fault type and the fault position of the metering device.
The operation monitoring module carries out fault judgment, fault type classification and fault positioning through big data analysis, and timeliness and accuracy of maintenance of the metering device are improved.
Preferably, the preprocessing the acquired data comprises:
processing noise in the data by a binning method;
replacing the detected abnormal value by a linear interpolation processing method;
detecting local outliers in the data, filtering and deleting the local outliers, calculating local outlier factors of all the points based on a KNN local LOF algorithm, judging whether the points are abnormal points according to the local outlier factors, and filtering and deleting the abnormal points.
Standard, clean and continuous high-quality data are obtained through pretreatment, and subsequent data mining and analysis are facilitated.
Preferably, the fault handling work order records fault type, fault location and work order generation time. The staff can select suitable maintenance workman to overhaul according to the trouble type and the trouble position of fault handling work order record, improves metering device fault handling efficiency.
Preferably, the statistical query module comprises a first statistical unit, a second statistical unit and a third statistical unit;
the first statistical unit is used for counting the fault types of the metering device, calculating and analyzing the occurrence probability of each fault type and sequencing; and obtaining the occurrence probability of each fault type of the metering device through statistical analysis, wherein the fault type with higher probability is the major point of maintenance and should be focused.
The second statistical unit is used for counting the fault positions of the metering device, calculating and analyzing the probability of faults at each fault position and sequencing; and obtaining the fault probability of each fault position of the metering device through statistical analysis, wherein the fault position with higher probability is the major point of maintenance and should be focused.
The third statistical unit is used for counting manufacturers of the metering devices, calculating and analyzing the failure rate of the metering devices of each manufacturer and sequencing the failure rate; and counting the failure rate of the metering devices of each manufacturer, and comparing and evaluating the metering devices of each manufacturer.
The invention has the advantages that the electric parameter data in the electricity collection system and the dispatching cloud system are comprehensively used, and the information barrier among different systems is broken; the fault type is accurately analyzed through a big data means, the fault position is positioned and a fault alarm is timely sent out, the maintenance work order is automatically generated, the fault maintenance mode is changed from original manual passive inspection into the active real-time monitoring of the existing system, the fault maintenance period is effectively shortened, and the timeliness and the accuracy of maintenance of the metering device are improved.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
Fig. 1 is a schematic block diagram of a big data based metering device troubleshooting system provided by an embodiment of the present invention.
The method comprises the steps of 1-a data acquisition module, 2-an operation monitoring module, 3-a fault processing module, 4-a statistical query module and 5-a visual display module.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, 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 invention.
As shown in fig. 1, an embodiment of the present invention provides a big data based metering device troubleshooting system, which includes a data acquisition module 1, an operation monitoring module 2, a fault processing module 3, a statistical query module 4, and a visual display module 5;
the data acquisition module 1 is used for accessing a cloud platform server of a power grid company to acquire data;
the data acquisition module 1 comprises a first data acquisition unit and a second data acquisition unit;
the first data acquisition unit is used for acquiring electric parameter data in the electricity utilization acquisition system;
the second data acquisition unit is used for acquiring the electrical parameter data in the dispatching cloud system;
the data acquisition module 1 is accessed to a cloud platform server of a power grid company through an API (application program interface) interface to acquire electric parameter data in the power utilization acquisition system and the dispatching cloud system, and breaks the information barriers among different systems.
The operation monitoring module 2 is used for analyzing and mining data to realize fault on-line monitoring and intelligent diagnosis and analysis of various metering devices;
the operation monitoring module 2 comprises a preprocessing unit, a fault judging unit, a fault marking unit and a fault diagnosis unit;
the preprocessing unit is used for preprocessing the acquired data, and specifically comprises: processing noise in the data by a binning method; replacing the detected abnormal value by a linear interpolation processing method; detecting local outliers in the data, filtering and deleting the local outliers, calculating local outlier factors of all the points based on a KNN local LOF algorithm, judging whether the points are abnormal points according to the local outlier factors, and filtering and deleting the abnormal points. Standard, clean and continuous high-quality data are obtained through pretreatment, and subsequent data mining and analysis are facilitated.
The fault judging unit is used for setting a logistic regression algorithm to analyze the data and judging whether the metering device has a fault or not;
the fault marking unit is used for marking the metering device with faults and acquiring log data and hardware operation data of the metering device;
and the fault diagnosis unit is used for setting a recurrent neural network algorithm to analyze the log data and the hardware operation data and judging the fault type and the fault position of the metering device.
The operation monitoring module 2 carries out fault judgment, fault type classification and fault positioning through big data analysis, and improves the timeliness and the accuracy of maintenance of the metering device.
And the fault processing module 3 is used for alarming faults of the metering device and automatically generating a fault processing work order, and the fault processing work order records the fault type, the fault position and the work order generation time. The staff can select suitable maintenance workman to overhaul according to the trouble type and the trouble position of fault handling work order record, improves metering device fault handling efficiency.
The statistical query module 4 is used for performing statistical analysis and query on the fault condition of the metering device;
the statistical query module 4 comprises a first statistical unit, a second statistical unit and a third statistical unit;
the first statistical unit is used for counting the fault types of the metering device, calculating and analyzing the occurrence probability of each fault type and sequencing; and obtaining the occurrence probability of each fault type of the metering device through statistical analysis, wherein the fault type with higher probability is the major point of maintenance and should be focused.
The second statistical unit is used for counting the fault positions of the metering device, calculating and analyzing the probability of faults at each fault position and sequencing; and obtaining the fault probability of each fault position of the metering device through statistical analysis, wherein the fault position with higher probability is the major point of maintenance and should be focused.
The third statistical unit is used for counting manufacturers of the metering devices, calculating and analyzing the failure rate of the metering devices of each manufacturer and sequencing the failure rate; and counting the failure rate of the metering devices of each manufacturer, and comparing and evaluating the metering devices of each manufacturer.
And the visual display module 5 is used for visually displaying the results of the statistical analysis and the query.
The system also comprises a file management module, wherein the file management module is used for managing basic information of the metering device, corresponding relation between the metering device and a circuit and topological information of the transformer substation and providing a data base.
The system also comprises a distributed storage module, wherein the distributed storage module is provided with a plurality of storage nodes and is used for storing the data acquired by the data acquisition module.
Although the present invention has been described in detail by referring to the drawings in connection with the preferred embodiments, the present invention is not limited thereto. Various equivalent modifications or substitutions can be made on the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and these modifications or substitutions are within the scope of the present invention/any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention.
Claims (8)
1. A big data-based metering device fault maintenance system is characterized by comprising a data acquisition module, an operation monitoring module, a fault processing module, a statistical query module and a visual display module;
the data acquisition module is used for accessing a cloud platform server of a power grid company to acquire data;
the operation monitoring module is used for analyzing and mining data to realize fault on-line monitoring and intelligent diagnosis and analysis of various metering devices;
the fault processing module is used for alarming faults of the metering device and automatically generating a fault processing work order;
the statistical query module is used for performing statistical analysis and query on the fault condition of the metering device;
and the visual display module is used for visually displaying the results of the statistical analysis and the query.
2. The big data based metering device troubleshooting system of claim 1 further comprising a file management module for managing metering device basis information, metering device to line correspondence, substation topology information, providing a data basis.
3. The big data based metering device troubleshooting system of claim 1 further comprising a distributed storage module, said distributed storage module providing a plurality of storage nodes for storing data obtained by the data obtaining module.
4. The big-data based metering device troubleshooting system of claim 1 wherein the data acquisition module comprises a first data acquisition unit and a second data acquisition unit;
the first data acquisition unit is used for acquiring electric parameter data in the electricity utilization acquisition system;
and the second data acquisition unit is used for acquiring the electrical parameter data in the dispatching cloud system.
5. The big-data based metering device troubleshooting system of claim 1 wherein said operational monitoring module comprises a preprocessing unit, a fault determination unit, a fault marking unit, and a fault diagnosis unit;
the preprocessing unit is used for preprocessing the acquired data;
the fault judging unit is used for setting a logistic regression algorithm to analyze the data and judging whether the metering device has a fault or not;
the fault marking unit is used for marking the metering device with faults and acquiring log data and hardware operation data of the metering device;
and the fault diagnosis unit is used for setting a recurrent neural network algorithm to analyze the log data and the hardware operation data and judging the fault type and the fault position of the metering device.
6. The big-data based metering device troubleshooting system of claim 5 wherein the preprocessing the acquired data comprises:
processing noise in the data by a binning method;
replacing the detected abnormal value by a linear interpolation processing method;
detecting local outliers in the data, filtering and deleting the local outliers, calculating local outlier factors of all the points based on a KNN local LOF algorithm, judging whether the points are abnormal points according to the local outlier factors, and filtering and deleting the abnormal points.
7. The big-data based metering device troubleshooting system of claim 5 wherein the troubleshooting work order records a type of failure, a location of the failure, and a time of work order generation.
8. The big-data based metering device troubleshooting system of claim 5 wherein said statistical query module comprises a first statistical unit, a second statistical unit, and a third statistical unit;
the first statistical unit is used for counting the fault types of the metering device, calculating and analyzing the occurrence probability of each fault type and sequencing;
the second statistical unit is used for counting the fault positions of the metering device, calculating and analyzing the probability of faults at each fault position and sequencing;
and the third statistical unit is used for counting manufacturers of the metering devices, calculating and analyzing the failure rate of the metering devices of each manufacturer and sequencing the failure rate.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116842827A (en) * | 2023-06-13 | 2023-10-03 | 中国人民解放军陆军工程大学 | Electromagnetic performance boundary model construction method for unmanned aerial vehicle flight control system |
Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106199494A (en) * | 2016-07-25 | 2016-12-07 | 国网上海市电力公司 | A kind of intelligent diagnosis system based on metering device fault |
| CN107146018A (en) * | 2017-05-04 | 2017-09-08 | 北京许继电气有限公司 | Data Management Analysis method and system based on electric network state monitoring system |
| CN107862459A (en) * | 2017-11-10 | 2018-03-30 | 国网浙江省电力公司电力科学研究院 | Metering equipment state evaluation method and system based on big data |
| KR101856543B1 (en) * | 2018-02-26 | 2018-05-11 | 주식회사 리앙커뮤니케이션즈 | Failure prediction system based on artificial intelligence |
| CN108199922A (en) * | 2018-01-11 | 2018-06-22 | 承德石油高等专科学校 | A kind of system and method for diagnosing and repairing for the network equipment and server failure |
| CN108336725A (en) * | 2016-12-16 | 2018-07-27 | 泰豪软件股份有限公司 | The management of dispatching of power netwoks monitoring of tools and intelligent analysis system |
| KR101922031B1 (en) * | 2017-09-28 | 2018-11-27 | 주식회사 스타코프 | Watt-hour method and watt-hour meter f unctioning analysis of realtime power pattern, arc information extraction and arc fault alert on the basis of bigdata |
| CN109446184A (en) * | 2018-08-28 | 2019-03-08 | 湖南大唐先科技有限公司 | Power generation big data preprocess method and system based on big data analysis platform |
| CN109492783A (en) * | 2018-11-14 | 2019-03-19 | 中国电力科学研究院有限公司 | A kind of Application of Power Metering Instruments failure risk prediction technique based on GBDT |
| CN109816161A (en) * | 2019-01-14 | 2019-05-28 | 中国电力科学研究院有限公司 | A kind of power distribution network operation computer-aided decision support System and its application method |
| CN110046145A (en) * | 2019-04-02 | 2019-07-23 | 国网安徽省电力有限公司 | Expert intelligence Analysis Service platform based on electric flux big data research |
| CN111738462A (en) * | 2020-06-08 | 2020-10-02 | 国网江苏省电力有限公司常州供电分公司 | Fault first-aid repair active service early warning method for electric power metering device |
| CN112560214A (en) * | 2020-12-24 | 2021-03-26 | 国网北京市电力公司 | Transformer substation bus balance fault diagnosis method, system, equipment and storage medium |
| CN112686493A (en) * | 2020-11-24 | 2021-04-20 | 国网新疆电力有限公司营销服务中心(资金集约中心、计量中心) | Method for evaluating running state and replacing of intelligent electric meter in real time by relying on big data |
| CN113125874A (en) * | 2020-01-10 | 2021-07-16 | 国网上海市电力公司 | Intelligent studying and judging method for electric quantity acquisition fault of transformer substation |
| CN113420195A (en) * | 2021-05-28 | 2021-09-21 | 国网河北省电力有限公司营销服务中心 | Method and system for determining fault type of intelligent electric meter |
| CN113659723A (en) * | 2021-08-19 | 2021-11-16 | 国网江苏省电力有限公司泰州供电分公司 | Intelligent user fault studying and judging system and implementation method |
-
2021
- 2021-11-23 CN CN202111396802.5A patent/CN114236448A/en active Pending
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106199494A (en) * | 2016-07-25 | 2016-12-07 | 国网上海市电力公司 | A kind of intelligent diagnosis system based on metering device fault |
| CN108336725A (en) * | 2016-12-16 | 2018-07-27 | 泰豪软件股份有限公司 | The management of dispatching of power netwoks monitoring of tools and intelligent analysis system |
| CN107146018A (en) * | 2017-05-04 | 2017-09-08 | 北京许继电气有限公司 | Data Management Analysis method and system based on electric network state monitoring system |
| KR101922031B1 (en) * | 2017-09-28 | 2018-11-27 | 주식회사 스타코프 | Watt-hour method and watt-hour meter f unctioning analysis of realtime power pattern, arc information extraction and arc fault alert on the basis of bigdata |
| CN107862459A (en) * | 2017-11-10 | 2018-03-30 | 国网浙江省电力公司电力科学研究院 | Metering equipment state evaluation method and system based on big data |
| CN108199922A (en) * | 2018-01-11 | 2018-06-22 | 承德石油高等专科学校 | A kind of system and method for diagnosing and repairing for the network equipment and server failure |
| KR101856543B1 (en) * | 2018-02-26 | 2018-05-11 | 주식회사 리앙커뮤니케이션즈 | Failure prediction system based on artificial intelligence |
| CN109446184A (en) * | 2018-08-28 | 2019-03-08 | 湖南大唐先科技有限公司 | Power generation big data preprocess method and system based on big data analysis platform |
| CN109492783A (en) * | 2018-11-14 | 2019-03-19 | 中国电力科学研究院有限公司 | A kind of Application of Power Metering Instruments failure risk prediction technique based on GBDT |
| CN109816161A (en) * | 2019-01-14 | 2019-05-28 | 中国电力科学研究院有限公司 | A kind of power distribution network operation computer-aided decision support System and its application method |
| CN110046145A (en) * | 2019-04-02 | 2019-07-23 | 国网安徽省电力有限公司 | Expert intelligence Analysis Service platform based on electric flux big data research |
| CN113125874A (en) * | 2020-01-10 | 2021-07-16 | 国网上海市电力公司 | Intelligent studying and judging method for electric quantity acquisition fault of transformer substation |
| CN111738462A (en) * | 2020-06-08 | 2020-10-02 | 国网江苏省电力有限公司常州供电分公司 | Fault first-aid repair active service early warning method for electric power metering device |
| CN112686493A (en) * | 2020-11-24 | 2021-04-20 | 国网新疆电力有限公司营销服务中心(资金集约中心、计量中心) | Method for evaluating running state and replacing of intelligent electric meter in real time by relying on big data |
| CN112560214A (en) * | 2020-12-24 | 2021-03-26 | 国网北京市电力公司 | Transformer substation bus balance fault diagnosis method, system, equipment and storage medium |
| CN113420195A (en) * | 2021-05-28 | 2021-09-21 | 国网河北省电力有限公司营销服务中心 | Method and system for determining fault type of intelligent electric meter |
| CN113659723A (en) * | 2021-08-19 | 2021-11-16 | 国网江苏省电力有限公司泰州供电分公司 | Intelligent user fault studying and judging system and implementation method |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116842827A (en) * | 2023-06-13 | 2023-10-03 | 中国人民解放军陆军工程大学 | Electromagnetic performance boundary model construction method for unmanned aerial vehicle flight control system |
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