CN109118385A - Urban distribution network status data modeling method and system towards big data - Google Patents
Urban distribution network status data modeling method and system towards big data Download PDFInfo
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- CN109118385A CN109118385A CN201810919448.1A CN201810919448A CN109118385A CN 109118385 A CN109118385 A CN 109118385A CN 201810919448 A CN201810919448 A CN 201810919448A CN 109118385 A CN109118385 A CN 109118385A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The present invention provides the urban distribution network status data modeling method towards big data, includes the following steps: that the first step, parameter monitoring module are monitored the parameter of grid equipment；Second step, the data that data analysis module detects the first step are analyzed, and judge whether to break down；Third step, the fault data that data transmission blocks detect second step are sent to client；4th step, fault identification module judge whether to warning note according to the fault data of third step；5th step, positioning failure module carry out fault location according to the data in the 4th step；6th step, system recovery module repair the failure that the 5th step positions.The beneficial effects of the present invention are: predicting the sudden collapse of grid equipment, make and giving warning in advance, improve the stability of system, the adapter tube function to later period other equipment is provided.
Description
Technical field
The present invention relates to electric system to repair field, more specifically to a kind of urban distribution network state towards big data
Data Modeling Method and system.
Background technique
Electric system at present is higher and higher dependent on the level of informatization, while the promotion of the information-based efficiency to electric system
It performs meritorious deeds never to be obliterated, provides sound assurance for the leap of electric system.Traditional mode is gradually substituted by informationization.Client's ditch
It is logical to be also transferred to information system level gradually.
With the rapid development of informationization technology, the equal large-scale use of grid equipment, the network equipment in every field, generally
Think that grid equipment collapse is a very difficult project, grid equipment collapse is often paroxysmal and it is difficult to predict can not
Accomplish effectively to give warning in advance, it is necessary to be controlled to improve the stability of system.
Domestic correlative study does not have while to equipment, power supply, the management of environment, behavior auditing at present, does not have
Standby development interface, provides the adapter tube function to later period other equipment.
Summary of the invention
The present invention overcomes deficiency in the prior art, provides a kind of urban distribution network status data towards big data and build
Mould method and system.
The purpose of the present invention is achieved by following technical proposals.
A kind of urban distribution network status data modeling method towards big data, characterized by the following steps:
The first step, parameter monitoring module are monitored the parameter of grid equipment；
Second step, data analysis module analyze the data that the first step detects, judge whether to break down；
The fault data that the second step detects is sent to client by third step, data transmission blocks；
4th step, fault identification module judge whether to warning note according to the fault data of the third step；
5th step, positioning failure module carry out fault location according to the data in the 4th step；
6th step, system recovery module repair the failure that the 5th step positions.
Further, the parameter monitoring module is connect, for the ginseng to monitoring object unit with the monitoring object unit
Number carries out real-time monitoring.
Further, the data analysis module is connected with the parameter monitoring module by data/address bus, for described
The data of parameter monitoring module collection are handled and are analyzed, and grid equipment fault data is obtained.
Further, the positioning failure module connects the data analysis module by data/address bus, comprising based on nerve
The expert system of network technology, according to treated data using the expert system based on nerual network technique to grid equipment,
The model of load capacity and health degree under network equipment normal condition is recognized.
Further, the data transmission blocks, the grid equipment fault data that the data analysis module is obtained are sent
To the client.
Further, grid equipment is transformer, breaker, disconnecting link, relay, mutual inductor.
Further, the parameter is monitored its temperature, temperature, humidity, voltage and current according to grid equipment difference.
Further, including client, parameter monitoring module, data analysis module, data transmission blocks, fault identification mould
Block, positioning failure module and system recovery module；
The parameter monitoring module is connect with the monitoring object unit, for being monitored to monitoring object, monitors number
Its temperature, temperature, humidity, voltage and current are monitored according to according to grid equipment difference；
The data analysis module is connected with the parameter monitoring module by data/address bus, for supervising to the parameter
The data for surveying module collection are handled and are analyzed, and grid equipment fault data is obtained；
The positioning failure module connects the data analysis module by data/address bus, comprising being based on neural network skill
The expert system of art, according to treated, data utilize the expert system based on nerual network technique to set grid equipment, network
The model of load capacity and health degree under standby normal condition is recognized；
The grid equipment fault data that the data analysis module obtains is sent to the visitor by the data transmission blocks
Family end and the fault identification module；
The fault identification module failure is connected with the system recovery module, repairs to failure.
The invention has the benefit that the present invention is monitored grid equipment parameter, neural network is based on using a set of
The expert system of technology recognizes the model of grid equipment, the load capacity under network equipment normal condition and health degree, diagnoses through length
Grid equipment under a certain load level after phase operation, the network equipment health degree whether matched with desired value, to be set to power grid
Standby sudden collapse is predicted, is made and being given warning in advance, improves the stability of system, be provided simultaneously with development interface, provide to rear
The adapter tube function of phase other equipment.
Detailed description of the invention
Fig. 1 is invention system flow chart.
Specific embodiment
Below by specific embodiment, further description of the technical solution of the present invention.
A kind of urban distribution network status data modeling method towards big data, characterized by the following steps:
The first step, parameter monitoring module are monitored the parameter of grid equipment；
Second step, data analysis module analyze the data that the first step detects, judge whether to break down；
The fault data that the second step detects is sent to client by third step, data transmission blocks；
4th step, fault identification module judge whether to warning note according to the fault data of the third step；
5th step, positioning failure module carry out fault location according to the data in the 4th step；
6th step, system recovery module repair the failure that the 5th step positions.
Further, the parameter monitoring module is connect, for the ginseng to monitoring object unit with the monitoring object unit
Number carries out real-time monitoring.
Further, the data analysis module is connected with the parameter monitoring module by data/address bus, for described
The data of parameter monitoring module collection are handled and are analyzed, and grid equipment fault data is obtained.
Further, the positioning failure module connects the data analysis module by data/address bus, comprising based on nerve
The expert system of network technology, according to treated data using the expert system based on nerual network technique to grid equipment,
The model of load capacity and health degree under network equipment normal condition is recognized.
Further, the data transmission blocks, the grid equipment fault data that the data analysis module is obtained are sent
To the client.
Further, grid equipment is transformer, breaker, disconnecting link, relay, mutual inductor.
Further, the parameter is monitored its temperature, temperature, humidity, voltage and current according to grid equipment difference.
Further, including client, parameter monitoring module, data analysis module, data transmission blocks, fault identification mould
Block, positioning failure module and system recovery module；
The parameter monitoring module is connect with the monitoring object unit, for being monitored to monitoring object, monitors number
Its temperature, temperature, humidity, voltage and current are monitored according to according to grid equipment difference；
The data analysis module is connected with the parameter monitoring module by data/address bus, for supervising to the parameter
The data for surveying module collection are handled and are analyzed, and grid equipment fault data is obtained；
The positioning failure module connects the data analysis module by data/address bus, comprising being based on neural network skill
The expert system of art, according to treated, data utilize the expert system based on nerual network technique to set grid equipment, network
The model of load capacity and health degree under standby normal condition is recognized；
The grid equipment fault data that the data analysis module obtains is sent to the visitor by the data transmission blocks
Family end and the fault identification module；
The fault identification module failure is connected with the system recovery module, repairs to failure.
One embodiment of the present invention has been described in detail above, but the content is only preferable implementation of the invention
Example, should not be considered as limiting the scope of the invention.It is all according to all the changes and improvements made by the present patent application range
Deng should still be within the scope of the patent of the present invention.
Claims (8)
1. a kind of urban distribution network status data modeling method towards big data, characterized by the following steps:
The first step, parameter monitoring module are monitored the parameter of grid equipment；
Second step, data analysis module analyze the data that the first step detects, judge whether to break down；
The fault data that the second step detects is sent to client by third step, data transmission blocks；
4th step, fault identification module judge whether to warning note according to the fault data of the third step；
5th step, positioning failure module carry out fault location according to the data in the 4th step；
6th step, system recovery module repair the failure that the 5th step positions.
2. the urban distribution network status data modeling method according to claim 1 towards big data, it is characterised in that: described
Parameter monitoring module is connect with the monitoring object unit, carries out real-time monitoring for the parameter to monitoring object unit.
3. the urban distribution network status data modeling method according to claim 1 towards big data, it is characterised in that: described
Data analysis module is connected, for the parameter monitoring module collection by data/address bus with the parameter monitoring module
Data are handled and are analyzed, and grid equipment fault data is obtained.
4. the urban distribution network status data modeling method according to claim 1 towards big data, it is characterised in that: described
Positioning failure module connects the data analysis module by data/address bus, comprising the expert system based on nerual network technique,
According to treated, data utilize the expert system based on nerual network technique under grid equipment, network equipment normal condition
The model of load capacity and health degree is recognized.
5. the urban distribution network status data modeling method according to claim 1 towards big data, it is characterised in that: described
The grid equipment fault data that the data analysis module obtains is sent to the client by data transmission blocks.
6. the urban distribution network status data modeling method according to claim 1 towards big data, it is characterised in that: power grid
Equipment is transformer, breaker, disconnecting link, relay, mutual inductor.
7. the urban distribution network status data modeling method according to claim 1 towards big data, it is characterised in that: described
Parameter is monitored its temperature, temperature, humidity, voltage and current according to grid equipment difference.
8. a kind of system applied in method of claim 1, it is characterised in that: including client, parameter monitoring module, number
According to analysis module, data transmission blocks, fault identification module, positioning failure module and system recovery module；
The parameter monitoring module is connect with the monitoring object unit, for being monitored to monitoring object, monitoring data according to
Its temperature, temperature, humidity, voltage and current are monitored according to grid equipment difference；
The data analysis module is connected with the parameter monitoring module by data/address bus, for the parameter monitoring mould
The data that block is collected are handled and are analyzed, and grid equipment fault data is obtained；
The positioning failure module connects the data analysis module by data/address bus, comprising based on nerual network technique
Expert system, according to treated data using the expert system based on nerual network technique to grid equipment, the network equipment just
The model of load capacity and health degree under normal state is recognized；
The grid equipment fault data that the data analysis module obtains is sent to the client by the data transmission blocks
With the fault identification module；
The fault identification module failure is connected with the system recovery module, repairs to failure.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201810919448.1A CN109118385A (en) | 2018-08-10 | 2018-08-10 | Urban distribution network status data modeling method and system towards big data |
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| Application Number | Priority Date | Filing Date | Title |
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| CN201810919448.1A CN109118385A (en) | 2018-08-10 | 2018-08-10 | Urban distribution network status data modeling method and system towards big data |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109884475A (en) * | 2019-04-02 | 2019-06-14 | 云南电网有限责任公司大理供电局 | A kind of electric network fault detection method, device, system and storage medium |
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| CN104410686A (en) * | 2014-11-25 | 2015-03-11 | 江苏省电力公司扬州供电公司 | Bank power grid intelligent monitoring system |
| CN104793607A (en) * | 2015-04-20 | 2015-07-22 | 国家电网公司 | Online fault diagnosis, health analysis and failure prediction system and online fault diagnosis, health analysis and failure prediction method for servers |
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2018
- 2018-08-10 CN CN201810919448.1A patent/CN109118385A/en active Pending
Patent Citations (4)
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| WO2001033513A1 (en) * | 1999-10-28 | 2001-05-10 | General Electric Company | Method and system for remotely managing communication of data used for predicting malfunctions in a plurality of machines |
| JP2001133089A (en) * | 1999-11-09 | 2001-05-18 | Fuji Electric Co Ltd | Store control system, failure diagnosis method, computer- readable record medium recording program for executing failure diagnosis method using computer |
| CN104410686A (en) * | 2014-11-25 | 2015-03-11 | 江苏省电力公司扬州供电公司 | Bank power grid intelligent monitoring system |
| CN104793607A (en) * | 2015-04-20 | 2015-07-22 | 国家电网公司 | Online fault diagnosis, health analysis and failure prediction system and online fault diagnosis, health analysis and failure prediction method for servers |
Non-Patent Citations (1)
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| CN109884475A (en) * | 2019-04-02 | 2019-06-14 | 云南电网有限责任公司大理供电局 | A kind of electric network fault detection method, device, system and storage medium |
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