CN111367970A - Low-voltage diagnosis system based on data acquired by intelligent electric meter - Google Patents
Low-voltage diagnosis system based on data acquired by intelligent electric meter Download PDFInfo
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Abstract
The invention discloses a low-voltage diagnosis system based on data acquired by an intelligent electric meter, which comprises a data acquisition module, a big data center, a data analysis module and a data diagnosis module, wherein the big data center comprises a headquarter big data center and a network province company big data center. The design has data acquisition module, big data center, data analysis module and data diagnosis module, and data acquisition module is smart electric meter, data acquisition module and big data center electric connection, big data center and data analysis module electric connection, and data acquisition module, data analysis module all and data diagnosis module electric connection. The data analysis module realizes the fault prejudgment and carries out prejudgment processing on faults which may occur in the future, and the data diagnosis module is divided into diagnosis support data, intelligent diagnosis data, operation and maintenance learning data and diagnosis application data in a data framework, can intelligently diagnose low-voltage causes and then further calculates the probability of the low-voltage causes.
Description
Technical Field
The invention relates to the field of intelligent electric meters, in particular to a low-voltage diagnosis system based on real acquisition data of an intelligent electric meter.
Background
Voltage is an important index for measuring the quality of electric energy. Various electric equipment is designed and manufactured according to rated voltage, the equipment can achieve the best effect when the equipment is operated under the rated voltage, and the voltage is in adverse effect on users when the voltage is excessively deviated from the rated voltage. Common electric equipment of a power system comprises an asynchronous motor, various electric heating equipment, an illuminating lamp, household appliances increasing in recent years and the like.
When the voltage is too low, the efficiency of the electric equipment is reduced, and the quality and the efficiency of production are influenced; when the voltage is too high, the life of the electric device will be shortened. The voltage deviation is too large, which, in addition to affecting the normal operation of the user, also has an adverse effect on the power system itself. The power loss and the energy loss in the network are increased due to the reduction of the voltage, and the stability of the operation of the power system can be endangered due to the low voltage; when the voltage is too high, insulation of various electrical devices may be damaged.
Disclosure of Invention
In order to solve the above mentioned drawbacks in the background art, the present invention provides a low voltage diagnosis system based on data acquired by a smart meter, wherein the system is designed with a data acquisition module, a big data center, a data analysis module and a data diagnosis module, the data acquisition module is the smart meter, the data acquisition module is electrically connected with the big data center, the big data center is electrically connected with the data analysis module, and the data acquisition module and the data analysis module are electrically connected with the data diagnosis module. The data analysis module realizes the fault prejudgment and carries out prejudgment processing on faults which may occur in the future, and the data diagnosis module is divided into diagnosis support data, intelligent diagnosis data, operation and maintenance learning data and diagnosis application data in a data framework, can intelligently diagnose low-voltage causes and then further calculates the probability of the low-voltage causes.
The purpose of the invention can be realized by the following technical scheme:
a low-voltage diagnosis system based on data acquired by an intelligent electric meter comprises a data acquisition module, a big data center, a data analysis module and a data diagnosis module, wherein the big data center comprises a headquarter big data center and a network province company big data center.
The data acquisition module is smart electric meter, data acquisition module and big data center electric connection, big data center and data analysis module electric connection, and data acquisition module, data analysis module all and data diagnosis module electric connection.
Further, the data analysis module realizes fault pre-judgment and pre-judges faults which may occur in the future.
Furthermore, the data diagnosis module data architecture is divided into diagnosis support data, intelligent diagnosis data, operation and maintenance learning data and diagnosis application data.
Further, the headquarter big data center comprises headquarter fault diagnosis data, a national network company fault diagnosis knowledge learning base and a national network company fault diagnosis management base.
Further, the grid province company big data comprises grid province fault diagnosis data, an intelligent diagnosis module and various data.
Further, the plurality of data includes fault data, collected data, ledger data, and handheld data.
Further, the intelligent diagnosis module comprises fault feature data, diagnosis model data, diagnosis data, knowledge data and statistical analysis data.
The invention has the beneficial effects that:
the intelligent electric meter is provided with a data acquisition module, a big data center, a data analysis module and a data diagnosis module, wherein the data acquisition module is an intelligent electric meter and is electrically connected with the big data center, the big data center is electrically connected with the data analysis module, and the data acquisition module and the data analysis module are electrically connected with the data diagnosis module. The data analysis module realizes the fault prejudgment and carries out prejudgment processing on faults which may occur in the future, and the data diagnosis module is divided into diagnosis support data, intelligent diagnosis data, operation and maintenance learning data and diagnosis application data in a data framework, can intelligently diagnose low-voltage causes and then further calculates the probability of the low-voltage causes.
Drawings
The invention will be further described with reference to the accompanying drawings.
FIG. 1 is an overall block diagram of the present invention;
FIG. 2 is a schematic diagram of a large data center of the present invention;
FIG. 3 is a schematic diagram of a headquarters data center according to the present invention;
FIG. 4 is a schematic view of a large data center of a network province company of the present invention;
FIG. 5 is a schematic diagram of the data integration of the present invention with a power usage collection system;
FIG. 6 is a schematic diagram of a test service of the present invention;
FIG. 7 is a schematic diagram of the data integration of the marketing system of the present invention;
FIG. 8 is a schematic diagram of the data integration with the operation and maintenance closed-loop module according to the present invention;
FIG. 9 is a schematic diagram of the data integration with the scheduling system of the present invention;
FIG. 10 is a schematic diagram of the present invention with operator platform data integration;
FIG. 11 is a schematic diagram of the data integration with a weather website according to the present invention.
Detailed Description
The technical solutions 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.
A low-voltage diagnosis system based on data acquired by an intelligent electric meter is shown in figure 1 and comprises a data acquisition module, a big data center, a data analysis module and a data diagnosis module, wherein the big data center comprises a headquarter big data center and a network province company big data center.
The data acquisition module is smart electric meter, data acquisition module and big data center electric connection, big data center and data analysis module electric connection, and data acquisition module, data analysis module all and data diagnosis module electric connection.
The data analysis module realizes the fault prejudgment and carries out prejudgment processing on the faults which may occur in the future.
The data diagnosis module data architecture is divided into diagnosis support data, intelligent diagnosis data, operation and maintenance learning data and diagnosis application data.
As shown in fig. 2 and 3, the headquarters big data center includes headquarters fault diagnosis data, a national network company fault diagnosis knowledge learning base, and a national network company fault diagnosis management base.
As shown in fig. 2 and 4, the grid province company big data includes grid province fault diagnosis data, a smart diagnosis module and various data.
The plurality of data includes fault data, collected data, ledger data, and hand held data. The intelligent diagnosis module comprises fault characteristic data, diagnosis model data, diagnosis data, knowledge data and statistical analysis data.
As shown in fig. 5 and 6, the integration with the electricity consumption information collection master station:
the data of the acquisition master station mainly comprises the following steps: the system comprises an organization domain, a power grid domain, user files, collection files, measurement point files, relationships among files, meter reading results (freezing, current and curve), parameter information, event records, abnormal data, main station operation conditions and the like, and is shown in fig. 5.
In the backward area of the prepositive technology, the prepositive reconstruction is adopted, a real-time stream data processing scheme is used, data are converged through kafka, real-time data processing is carried out through Flink, and persistence is carried out in distributed big data storage.
Aiming at the master station system of the oracle architecture, the data of the database are synchronously acquired in real time in an OGG mode, and the comprehensive sharing of the data is realized.
In the aspect of application integration, an acquisition master station service calling mode is adopted for integration. For the architecture which does not support real-time data synchronization, data are acquired in a T +1 mode, the data are extracted in the morning in a sqoop mode, and analysis and diagnosis are carried out on the next day.
For the integration of state data and instantaneous data (such as data of on-off state, current clock of electric meter, current clock of terminal, instantaneous quantity, current code, etc.), the integration can be obtained in the form of WebService service, the fault intelligent diagnosis module calls WebService service of the electricity consumption acquisition module, the electricity consumption acquisition system acquires corresponding data messages of the field terminal and the electric meter through internal communication service, and returns to the fault diagnosis module after analyzing the data messages into structured data, as shown in FIG. 6.
As shown in fig. 7, integration with marketing services application system:
the data acquisition required from the marketing system mainly includes: organization domain, grid domain, user profile, collection profile, measurement point profile, inter-profile relationship, profile change record, parameter configuration information, etc., as shown in fig. 7.
The integration mode is a mode of combining distributed quasi-real-time acquisition and Oracle synchronization aiming at data types with large data volume, long synchronization time consumption and high resource consumption, such as files and file change records.
As shown in fig. 8, integration with the operation and maintenance closed-loop module:
the operation and maintenance work order record, the work order type and other information need to be acquired from the acquisition operation and maintenance closed-loop module, as shown in fig. 8.
The integration mode is that stock work orders are obtained in batch in an OGG synchronous mode, and incremental work orders can be obtained in real time in a WebService interface mode according to time, work order types and file object types.
As shown in fig. 9, integration with the scheduling system:
it is necessary to acquire a planned blackout record and actual blackout record information corresponding to the planned blackout from the scheduling system, as shown in fig. 9.
The integration mode is that according to the requirement of the diagnosis service, a WebService interface form can be adopted to obtain the diagnosis service in real time according to conditions such as areas, lines, transformer areas, time and the like.
As shown in fig. 10, integration with the operator platform:
the slave operator platform data includes information such as the operating status of the SIM card, the tariff status, the tariff standard, the accumulated traffic, the location, etc., as shown in fig. 10. The integration mode is that the operator platform calls the external integration API service.
As shown in fig. 11, integration with weather website:
the corresponding position, temperature, humidity, wind direction, wind power, weather, time and other environmental information of the power supply area where the fault is located are obtained, as shown in fig. 11. The integration mode is that the weather website integrates API service call to the outside.
When the device is used, the data analysis module realizes fault pre-judgment and pre-judges faults which may occur in the future, the data diagnosis module is divided into diagnosis support data, intelligent diagnosis data, operation and maintenance learning data and diagnosis application data in a data architecture, low-voltage causes can be intelligently diagnosed, and then the probability of the low-voltage causes is further calculated.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.
Claims (7)
1. A low-voltage diagnosis system based on data acquired by an intelligent electric meter is characterized by comprising a data acquisition module, a big data center, a data analysis module and a data diagnosis module, wherein the big data center comprises a headquarter big data center and a network province company big data center;
the data acquisition module is smart electric meter, data acquisition module and big data center electric connection, big data center and data analysis module electric connection, and data acquisition module, data analysis module all and data diagnosis module electric connection.
2. The system of claim 1, wherein the data analysis module is configured to implement fault prediction to predict future faults.
3. The system of claim 1, wherein the data diagnosis module is configured to classify the data into diagnosis support data, intelligent diagnosis data, operation and maintenance learning data, and diagnosis application data.
4. The low-voltage diagnosis system based on the practical data of the intelligent electric meter as claimed in claim 1, wherein the headquarter big data center comprises headquarter fault diagnosis data, a national grid company fault diagnosis knowledge learning base and a national grid company fault diagnosis management base.
5. The system of claim 1, wherein the grid provincial company big data includes grid provincial fault diagnosis data, a smart diagnosis module and various data.
6. The system of claim 5, wherein the plurality of data comprises fault data, collected data, ledger data, and handheld data.
7. The system of claim 5, wherein the intelligent diagnosis module comprises fault feature data, diagnosis model data, diagnosis data, knowledge data and statistical analysis data.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111985561A (en) * | 2020-08-19 | 2020-11-24 | 安徽蓝杰鑫信息科技有限公司 | Fault diagnosis method and system for intelligent electric meter and electronic device |
| CN111985558A (en) * | 2020-08-19 | 2020-11-24 | 安徽蓝杰鑫信息科技有限公司 | Electric energy meter abnormity diagnosis method and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103473710A (en) * | 2013-08-20 | 2013-12-25 | 国家电网公司 | Graded handling method for faults of centralized operation and maintenance systems |
| CN106526425A (en) * | 2016-11-23 | 2017-03-22 | 衢州学院 | Low-voltage diagnosis method based on real collected data of intelligent electric meter |
| CN106646038A (en) * | 2016-11-30 | 2017-05-10 | 衢州学院 | Low-voltage diagnosis system on basis of data acquired by intelligent electric meter in real time |
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- 2020-03-27 CN CN202010230938.8A patent/CN111367970A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103473710A (en) * | 2013-08-20 | 2013-12-25 | 国家电网公司 | Graded handling method for faults of centralized operation and maintenance systems |
| CN106526425A (en) * | 2016-11-23 | 2017-03-22 | 衢州学院 | Low-voltage diagnosis method based on real collected data of intelligent electric meter |
| CN106646038A (en) * | 2016-11-30 | 2017-05-10 | 衢州学院 | Low-voltage diagnosis system on basis of data acquired by intelligent electric meter in real time |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111985561A (en) * | 2020-08-19 | 2020-11-24 | 安徽蓝杰鑫信息科技有限公司 | Fault diagnosis method and system for intelligent electric meter and electronic device |
| CN111985558A (en) * | 2020-08-19 | 2020-11-24 | 安徽蓝杰鑫信息科技有限公司 | Electric energy meter abnormity diagnosis method and system |
| CN111985561B (en) * | 2020-08-19 | 2023-02-21 | 安徽蓝杰鑫信息科技有限公司 | Fault diagnosis method and system for intelligent electric meter and electronic device |
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Application publication date: 20200703 |