CN110245163A - A kind of Operation of Electric Systems hidden troubles removing method - Google Patents
A kind of Operation of Electric Systems hidden troubles removing method Download PDFInfo
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- CN110245163A CN110245163A CN201910045792.7A CN201910045792A CN110245163A CN 110245163 A CN110245163 A CN 110245163A CN 201910045792 A CN201910045792 A CN 201910045792A CN 110245163 A CN110245163 A CN 110245163A
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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
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
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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
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
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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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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2216/00—Indexing scheme relating to additional aspects of information retrieval not explicitly covered by G06F16/00 and subgroups
- G06F2216/03—Data mining
Abstract
The invention discloses a kind of Operation of Electric Systems hidden troubles removing methods, comprise the following steps: step 1: data acquisition；Step 2: data prediction step 3: incipient fault data storage and calculating；Step 4: data analysis and excavation；Step 5: Analysis of Potential result visualization is shown.Using this method, by carrying out statistic of classification to hidden danger information, the incidence relation that various dimensions show hidden danger and hidden trouble of equipment, season and distinct device put into operation between the information such as the time limit builds information visualization platform, facilitates directly monitoring and the management of equipment；It is intuitive to show the incidence relations such as hidden danger and season, environment, historical information is combed, the keynote message for instructing tour personnel to need to make an inspection tour in Various Seasonal, varying environment improves and makes an inspection tour efficiency；It is analyzed by accident case, shows accident common denominator data, possible consequence etc. instructs hidden troubles removing control to find rule.
Description
Technical field
The present invention relates to field of power systems, specifically, being related to a kind of Operation of Electric Systems hidden troubles removing method.
Background technique
With economic rapid development, electric power networks construction speed is also getting faster, but with the extensive covering of power grid,
The difficult point that safety hazards investigation administers maintenance work is also increasingly prominent, using new approach or mode to safety hazards
Investigation is administered extremely urgent.
There are following difficulty needs to capture for safe operation of power system at present:
(1) daily tour heavy workload, emphasis do not protrude, and the low efficiency that scents a hidden danger, and rely on personal experience；Many power supplies at present
Company mainly by periodical inspection, crossing construction, there are failures and maintenance prerun etc. means at scene scents a hidden danger, wherein leading to
Cross the main means that the daily tour discovery security risk of bottom-up employee is current security risk discovery.But with economical fast
Speed development, Chinese electric power networks construction speed are also getting faster, and there has also been biggish promotion, topological structure of electric for voltage class
It is increasingly complicated, it makes an inspection tour workload and is continuously increased, it is opposite for the energy of individual equipment (route) investment to reduce, meanwhile, because lacking
The key area there may be hidden danger can not be accurately positioned in weary effective information guiding, make an inspection tour focus and do not protrude, and find
Hidden danger low efficiency relies on personal experience；
(2) the information associations degree such as hidden danger and season, hidden trouble of equipment, equipment state evaluation is low；With the extensive covering of power grid, especially
It is electric line long-term work in natural environment, by the test of various natural calamities, especially in some mountain areas, height
The ground such as original, hidden troubles removing control just seem more difficult；
(3) accident case analysis over the years is low with hidden troubles removing improvement combination degree, and common denominator data can not intuitively be shown；Accident over the years
Analysis of cases work has been carried out for many years, but is not very significantly, more often for the help of hidden troubles removing control
It is that scene has occurred and that the investigation processing carried out after the event for influencing operation of power networks.
Summary of the invention
Mesh of the invention is to solve electric system inspection heavy workload, hidden troubles removing difficulty, prevention mechanism shortcoming, lack letter
Breathization shows the problem of poor platform, proposes a kind of Operation of Electric Systems hidden troubles removing method, by utilizing big data technology to hidden
The statistical analysis of trouble effectively can accurately scent a hidden danger and the putting equipment in service time limit, accident (event), equipment deficiency, factory
The incidence relation of the information such as family, running environment, season improves hidden troubles removing governance efficiency.
To realize the above-mentioned technical purpose, a kind of technical solution provided by the invention is a kind of Operation of Electric Systems hidden danger row
Checking method includes the following steps:
Step 1: data acquisition；
Step 2: data prediction；
Step 3: incipient fault data storage and calculating；
Step 4: data analysis and excavation；
Step 5: Analysis of Potential result visualization is shown.
The step 1 is by transferring hidden danger relevant information all over 10 years in management database, the hidden danger letter
Breath includes: electric power related system historical data, hidden danger parameter, hidden trouble of equipment basic parameter, device location information and equipment
Operating status description information.
Data prediction by being cleaned, being converted to the big data obtained in step 1, duplicate removal, merging and screening, it is right
Historical data carries out validity differentiation, retains advantageous analysis partial data to management database.
The storage of data passes through following steps and realizes: firstly, imported by network interface, by way of export from Power SCADA
Related data is extracted in more data platforms such as system, power information acquisition system, PMS system, OMS system, storage is arrived traditional
In relevant database；Then, data are extracted from relevant database by Sqoop tool, storage is distributed to Hadoop
File system.
Traditional relevant database mainly stores hidden danger basic parameter, and the hidden danger information includes: hidden danger
Reason, hidden danger source, the result data for finding date, hidden danger performance data and data analysis mining；The distributed text of Hadoop
Part system mainly stores magnanimity, complicated hidden danger content and hidden danger overhaul data；Number is handled to a large amount of, complicated hidden danger content and hidden danger
According to calculating mainly rely on Mahout components of data analysis with analysis and complete；Mass data is calculated through distributed computing framework
Afterwards, it obtains a result, and result is write direct into relevant database for service application layer access.
Data analysis passes through the data analysis mining technology logarithms such as clustering, association analysis, decision tree analysis with excavation
According to being modeled, relevant Data Analysis Model includes Clustering Model, relation analysis model and control measures decision tree mould
Type provides support to the analysis application of data using data analysis mining model, obtains all kinds of hidden danger and the putting equipment in service time limit, thing
Therefore the incidence relations such as violating the regulations, manufacturer, running environment and season, for assisting discovering device family hidden danger and to each
Class equipment generates the analysis prediction of hidden danger.
The relation analysis model includes: equipment safety hidden danger relation analysis model and hidden trouble of equipment association analysis mould
Type
The equipment safety hidden danger relation analysis model is by the association analysis algorithm of Mahout to hidden danger classification and all kinds of parameters
It is associated the incidence relation analyzed and found out between all kinds of hidden danger and each parameter.
It completes on the incidence relation between all kinds of hidden danger and each hidden danger parameter, the hidden trouble of equipment relation analysis model root
Clustering is carried out to hidden danger by the K-means clustering algorithm of Mahout according to characteristics such as the particular content of hidden danger, processing modes,
Simultaneously by equipment associate device hidden danger information, the incidence relation between hidden danger and hidden danger and event is studied, is hidden troubles removing
It administers and data supporting is provided.
Based on data mining results, hidden danger and equipment deficiency, hidden danger and season and hidden danger and accident case over the years are analyzed
Between incidence relation, utilize computer graphics and image processing techniques exploitation visualize interface.
Beneficial effects of the present invention: 1, by carrying out statistic of classification to hidden danger information, various dimensions show that hidden danger and equipment are hidden
Suffer from, the incidence relation that season and distinct device put into operation between the information such as the time limit, builds information visualization platform, facilitate equipment
Directly monitoring and management；2, the incidence relations such as hidden danger and season, environment are intuitively shown, historical information is combed, instructs tour personnel
The keynote message for needing to make an inspection tour in Various Seasonal, varying environment improves and makes an inspection tour efficiency；3, it is analyzed, is shown by accident case
Accident common denominator data, possible consequence etc. instruct hidden troubles removing control to find rule.
Detailed description of the invention
Fig. 1 is a kind of flow chart of Operation of Electric Systems hidden troubles removing method of the invention.
Specific embodiment
It is right with reference to the accompanying drawings and examples for the purpose of the present invention, technical solution and advantage is more clearly understood
The present invention is described in further detail, it should be appreciated that the specific embodiments described herein are only one kind of the invention
Most preferred embodiment, only to explain the present invention, and the scope of protection of the present invention is not limited, and those of ordinary skill in the art are not having
Every other embodiment obtained under the premise of creative work is made, shall fall within the protection scope of the present invention.
Embodiment: as shown in Figure 1, a kind of Operation of Electric Systems hidden troubles removing method, includes the following steps:
Step 1: data acquisition；
Step 2: data prediction；
Step 3: incipient fault data storage and calculating；
Step 4: data analysis and excavation；
Step 5: Analysis of Potential result visualization is shown.
Data prediction by being cleaned, being converted to the big data obtained in step 1, duplicate removal, merging and screening, it is right
Historical data carries out validity differentiation, retains advantageous analysis partial data to management database, the big data includes ten
All hidden danger relevant informations over year, the hidden danger information includes: electric power related system historical data, is set hidden danger parameter
Standby hidden danger basic parameter, device location information and equipment running status description information.
The storage of data passes through following steps and realizes: firstly, imported by network interface, by way of export from Power SCADA
Related data is extracted in more data platforms such as system, power information acquisition system, PMS system, OMS system, storage is arrived traditional
In relevant database；Then, data are extracted from relevant database by Sqoop tool, storage is distributed to Hadoop
File system.
Traditional relevant database mainly stores hidden danger basic parameter, such as hidden danger reason, hidden danger source, discovery
The result data on date, hidden danger performance data and data analysis mining；Hadoop distributed file system mainly store magnanimity,
Complicated hidden danger content and hidden danger overhaul data；It is main to the calculating and analysis of a large amount of, complicated hidden danger content and hidden danger processing data
Mahout components of data analysis is relied on to complete；Mass data is obtained a result after distributed computing framework calculates, and will knot
Fruit writes direct relevant database for service application layer access.
Data analysis passes through the data analysis mining technology logarithms such as clustering, association analysis, decision tree analysis with excavation
According to being modeled, relevant Data Analysis Model includes Clustering Model, relation analysis model and control measures decision tree mould
Type provides support to the analysis application of data using data analysis mining model, obtains all kinds of hidden danger and the putting equipment in service time limit, thing
Therefore the incidence relations such as violating the regulations, manufacturer, running environment and season, for assisting discovering device family hidden danger and to each
Class equipment generates the analysis prediction of hidden danger.
The relation analysis model includes: equipment safety hidden danger relation analysis model and hidden trouble of equipment association analysis mould
Type；
The equipment safety hidden danger relation analysis model is by the association analysis algorithm of Mahout to hidden danger classification and all kinds of parameters
It is associated the incidence relation analyzed and found out between all kinds of hidden danger and each parameter.
On the basis of completing the incidence relation between all kinds of hidden danger and each hidden danger parameter, the hidden trouble of equipment association analysis mould
Type clusters hidden danger by the K-means clustering algorithm of Mahout according to characteristics such as particular content, the processing modes of hidden danger
Analysis, while by equipment associate device hidden danger information, the incidence relation between hidden danger and hidden danger, event is studied, is arranged for hidden danger
It looks into improvement and data supporting is provided.Pass through the modeling to information, the associated weights being associated between accident and each event, thus to accident
Malfunction elimination, prediction, classification judgement and measure is instructed to provide data supporting, establish an accurate efficient malfunction elimination with
Repair mechanism.
Based on data mining results, analyze between hidden danger and equipment deficiency, hidden danger and season, hidden danger and accident case over the years
Incidence relation, utilize computer graphics and image processing techniques exploitation visualize interface.It can be with by administration interface
The operating status of dynamic monitoring equipment can provide in time malfunction elimination and arrange for fault point and fault type accuracy of judgement
Impose and implement the man-machine system of accident responsibility, it is ensured that power system security is efficiently run.
The specific embodiment of the above is a kind of preferable implementation of Operation of Electric Systems hidden troubles removing method of the present invention
Mode limits specific implementation range of the invention not with this, and the scope of the present invention includes being not limited to present embodiment,
Equivalence changes made by all shape, structures according to the present invention are within the scope of the invention.
Claims (10)
1. a kind of Operation of Electric Systems hidden troubles removing method, characterized by the following steps:
Step 1: data acquisition；
Step 2: data prediction；
Step 3: incipient fault data storage and calculating；
Step 4: data analysis and excavation；
Step 5: Analysis of Potential result visualization is shown.
2. a kind of Operation of Electric Systems hidden troubles removing method according to claim 1, it is characterised in that: the step 1
By transferring hidden danger relevant information all over 10 years in management database, the hidden danger information includes: electric power phase relation
System historical data, hidden danger parameter, hidden trouble of equipment basic parameter, device location information and equipment running status description information.
3. a kind of Operation of Electric Systems hidden troubles removing method according to claim 1, it is characterised in that: the step 2
Data prediction by being cleaned, being converted to the big data obtained in step 1, duplicate removal, merging and screening, to historical data
Validity differentiation is carried out, retains advantageous analysis partial data to management database.
4. a kind of Operation of Electric Systems hidden troubles removing method according to claim 1, it is characterised in that: the step 3
The storage of middle data passes through following steps and realizes: firstly, imported by network interface, by way of export from Power SCADA system, use
Related data is extracted in more data platforms such as power utilization information collection system, PMS system, OMS system, traditional relationship type number is arrived in storage
According in library；Then, data are extracted from relevant database by Sqoop tool, Hadoop distributed file system is arrived in storage.
5. a kind of Operation of Electric Systems hidden troubles removing method according to claim 4, it is characterised in that: traditional pass
It is that type database mainly stores hidden danger basic parameter, the hidden danger basic parameter includes hidden danger reason, hidden danger source, discovery day
The result data of phase, hidden danger performance data and data analysis mining；Hadoop distributed file system mainly stores magnanimity, answers
Miscellaneous hidden danger content and hidden danger overhaul data；To a large amount of, complicated hidden danger content and hidden danger processing data calculating and analysis mainly according to
It holds in the palm and is completed in Mahout components of data analysis；Mass data is obtained a result after distributed computing framework calculates, and by result
Relevant database is write direct for service application layer access.
6. a kind of Operation of Electric Systems hidden troubles removing method according to claim 1, it is characterised in that: the step 4
Middle data analysis carries out data by data analysis mining technologies such as clustering, association analysis, decision tree analysis with excavation
Modeling, relevant Data Analysis Model include Clustering Model, relation analysis model and control measures decision-tree model, are used
Data analysis mining model provides support to the analysis application of data, obtains all kinds of hidden danger and the putting equipment in service time limit, accident, disobeys
The incidence relations such as chapter, manufacturer, running environment and season, for assisting discovering device family hidden danger and to various kinds of equipment
Generate the analysis prediction of hidden danger.
7. a kind of Operation of Electric Systems hidden troubles removing method according to claim 6, it is characterised in that: the association point
Analysis model includes: equipment safety hidden danger relation analysis model and hidden trouble of equipment relation analysis model.
8. a kind of Operation of Electric Systems hidden troubles removing method according to claim 7, it is characterised in that: the equipment safety
Hidden danger relation analysis model is associated analysis with all kinds of parameters to hidden danger classification by the association analysis algorithm of Mahout and finds out
Incidence relation between all kinds of hidden danger and each parameter.
9. a kind of Operation of Electric Systems hidden troubles removing method according to claim 7, it is characterised in that: complete all kinds of hidden danger
On incidence relation between each hidden danger parameter, the hidden trouble of equipment relation analysis model is according to the particular content of hidden danger, place
The characteristics such as reason mode carry out clustering to hidden danger by the K-means clustering algorithm of Mahout, while being set by equipment association
Standby hidden danger information, studies the incidence relation between hidden danger and hidden danger, event, provides data supporting for hidden troubles removing improvement.
10. a kind of Operation of Electric Systems hidden troubles removing method according to claim 1, it is characterised in that: the step 5
In, data mining results are based on, the pass between hidden danger and equipment deficiency, hidden danger and season, hidden danger and accident case over the years is analyzed
Connection relationship visualizes interface using computer graphics and image processing techniques exploitation.
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Cited By (3)
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| CN111046019A (en) * | 2019-11-22 | 2020-04-21 | 北京网聘咨询有限公司 | Database potential safety hazard troubleshooting method and device |
| CN111428895A (en) * | 2020-03-27 | 2020-07-17 | 安徽数升数据科技有限公司 | Intelligent ammeter fault diagnosis support center |
| CN113742508A (en) * | 2021-07-30 | 2021-12-03 | 国网河南省电力公司信息通信公司 | Graphic data mining method for monitoring mass information on line by power equipment |
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Effective date of registration: 20191220 Address after: 313000 No. 777 Fenghuang Road, Zhejiang, Huzhou Applicant after: State Grid Zhejiang Electric Power Co., Ltd. Huzhou Power Supply Co. Applicant after: STATE GRID ZHEJIANG DEQING COUNTY POWER SUPPLY Co.,Ltd. Applicant after: DEQING XINDIAN ELECTRIC POWER CONSTRUCTION CO.,LTD. Address before: 313200 Zhejiang city of Huzhou province Deqing County ZTE Wukang Road No. 9 Applicant before: STATE GRID ZHEJIANG DEQING COUNTY POWER SUPPLY Co.,Ltd. Applicant before: DEQING XINDIAN ELECTRIC POWER CONSTRUCTION CO.,LTD. |
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Application publication date: 20190917 |