CN107862459A - Metering equipment state evaluation method and system based on big data - Google Patents
Metering equipment state evaluation method and system based on big data Download PDFInfo
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Abstract
The invention discloses a metering equipment state evaluation method and system based on big data. The state evaluation method of some metering devices at present mainly aims at a single device. The invention comprises the following steps: integrating and storing mass measurement related data; establishing an algorithm analysis model based on big metering data, wherein the algorithm analysis model comprises establishing a strong association rule fault index and establishing a fault interval frequent set algorithm analysis mode based on massive historical data; and (3) evaluating the state of the metering equipment: and based on the fault interval frequent set algorithm analysis mode, performing fault equipment state evaluation on the master station of each voltage class and the transformers of each type in a visual analysis mode. The invention fully considers the current situation of real-time and historical data of the current mass metering equipment, and can improve the state evaluation level of the metering equipment based on mass data by using a big data method.
Description
Technical field
The present invention relates to measuring equipment state estimation field, specifically a kind of measuring equipment state based on big data
Appraisal procedure and system.
Background technology
State Grid Corporation of China actively develops metering system lean management work, there is metering production scheduling production platform at present
Comprehensive life-cycle management is carried out to metering device, effectively improves metering accuracy, but the monitoring to measuring equipment and
State estimation is not yet carried out, and the metering fault thus triggered can not be solved effectively.With the input increased to measuring equipment, pass through
The continuous data that harvester extracts is more and more, truly realizes the concept of metering big data.How by big number
According to means analysis and to assess measuring equipment be the emphasis studied at present.
Currently the state estimation of measuring equipment is mainly adopted by power information acquisition system realization to electric energy meter data
Collection and processing, and in power information acquisition system main website by comparing, statistical analysis technique means, to the fortune of measuring equipment
Row operating mode is diagnosed and analyzed, and judges whether measuring equipment is in normal operating condition, realizes decision-making function.Intelligence is examined
It is all kinds of that disconnected data source includes electric energy measurement data, operating condition data and the logout in electric energy meter and acquisition terminal etc.
Data.By the analysis to electric energy meter, the Production conditions of acquisition terminal Various types of data, incidence relation, with reference to related service application
Single equipment analytical model is established in demand, research.Single equipment analytical model can only be analyzed current from individual equipment angle
The running status of equipment.The research mode and conclusion of single equipment analysis are single, can not meet the analysis demand of profound level.
The content of the invention
Technical problem solved by the invention is the defects of overcoming above-mentioned prior art to exist, there is provided one kind is based on big data
Measuring equipment state evaluating method, its utilize magnanimity measuring equipment historical data, to lift the state estimation of measuring equipment
It is horizontal.
Therefore, the present invention adopts the following technical scheme that：A kind of measuring equipment state evaluating method based on big data, its
Including step：
1) magnanimity metering related data is integrated and stored；
2) the Algorithm Analysis model based on metering big data is established, including establishes Strong association rule fault indices and establishes base
In the frequent set based algorithm analytical model of the fault section of mass historical data；
3) measuring equipment state estimation：Based on the frequent set based algorithm analytical model of fault section, by visual analyzing side
Formula carries out default device state assessment to the main website of each voltage class and all types of transformers.
The present invention fully take into account current magnanimity measuring equipment in real time and historical data present situation, big data can be utilized
State estimation of the method lifting based on measuring equipment under mass data is horizontal.
As the supplement of above-mentioned technical proposal, in step 1), the content of Data Integration is as follows：Based on existing data fusion
Pattern and basis, by mass data center extraction continuous data, data access efficiency and the quality of data can be effectively lifted,
Mitigate the workload of data mining to a certain extent；
The data access at mass data center mainly by ETL related tools carry out data docking, by source with
Service logic and Data Integration period assignment of the destination in data transmission procedure, data are enabled into destination
Before, carry out unified continuous data and integrate and clean.
As the supplement of above-mentioned technical proposal, magnanimity measures the collection of related data, mainly from power information acquisition system,
Periodic duty and the taiwan area data of measuring equipment are extracted in sales service system, PMS.
As the supplement of above-mentioned technical proposal, in step 1), the content of data storage is as follows：The number according to needed for grid structure
According to being divided into static data and two classifications of dynamic data；First, the static data based on facility information (mainly has distribution transforming letter
Breath, line information, on-pole switch information, transformer information etc.), static data information updating frequency is low, and digital independent amount is small, is
This partial data is uniformly stored in the DataNode databases of single node by this, is easy to digital independent；It is second, negative to run
Dynamic data based on lotus, this partial data change update frequency is high, real time data update frequency up to 15 minutes, 10 minutes, 1
Minute even second level, it is individually deposited for this, maximizes the reading performance of raising data.
As the supplement of above-mentioned technical proposal, in step 2), the content for establishing Strong association rule fault indices is：Establish meter
The important fault impact index analysis system of equipment (predominantly transformer) is measured, passes through the relation between important fault impact index
Deployment analysis, establish the Strong association rule of these index occurrence of equipment failures.
As the supplement of above-mentioned technical proposal, in step 2), the content of the frequent set based algorithm analytical model of fault section is such as
Under：Intelligent Recognition is carried out by computer, on the basis of Strong association rule fault indices are established, referred to for each measuring equipment
Mark the judgement in section caused by parameter carries out primary fault.
As the supplement of above-mentioned technical proposal, in step 2), the judgment step in section caused by carrying out primary fault is as follows：It is first
First, the fault indices data of reality are judged, when judging each index is in what interval range, the number of generating state failure
Compare more, the time dimension of statistics is specific to the time；Then, according to the scope of each index judged, each is measured
Equipment index (per day magnitude of voltage, per day current value, rated current no-load voltage ratio, rated capacity, the time limit that puts into operation etc.) demarcation interval,
In interval range one to two sections arranged below, in one to two sections of interval range arrangement above.
It is a further object of the present invention to provide a kind of measuring equipment status assessing system based on big data, it includes：
Data Integration and memory cell：For integrating and storing magnanimity metering related data；
Algorithm Analysis unit：Establish Strong association rule fault indices and establish the fault section frequency based on mass historical data
Numerous set based algorithm analytical model；
Measuring equipment state evaluation unit：Based on the frequent set based algorithm analytical model of fault section, pass through visual analyzing
Mode carries out default device state assessment to the main website of each voltage class and all types of transformers.
It is the device have the advantages that as follows：Storage mode of the introducing that the present invention innovates based on distributed type assemblies, compared with
Original memory technology is compared, and storage and the read-write efficiency of data are drastically increased in terms of big data；Dug by big data
Parser model is dug, the reasonable analysis to measuring equipment mass data and utilization is realized, completes to comment measuring equipment state
Estimate and early warning.
Brief description of the drawings
Fig. 1 is the schematic flow sheet of appraisal procedure of the present invention.
Embodiment
The present invention is expanded on further with reference to embodiment.
Embodiment 1
The present embodiment provides a kind of measuring equipment state evaluating method based on big data, as shown in figure 1, it includes step
Suddenly：
1) magnanimity metering related data is integrated and stored；
2) the Algorithm Analysis model based on metering big data is established, including establishes Strong association rule fault indices and establishes base
In the frequent set based algorithm analytical model of the fault section of mass historical data；
3) measuring equipment state estimation：Based on the frequent set based algorithm analytical model of fault section, by visual analyzing side
Formula carries out default device state assessment to the main website of each voltage class and all types of transformers.
The present invention fully take into account current magnanimity measuring equipment in real time and historical data present situation, big data can be utilized
State estimation of the method lifting based on measuring equipment under mass data is horizontal.
In step 1), the content of Data Integration is as follows：Based on existing data fusion pattern and basis, pass through mass data
Center extraction continuous data, data access efficiency and the quality of data can be effectively lifted, mitigate data to a certain extent and open
The workload of hair；
The data access at mass data center mainly by ETL related tools carry out data docking, by source with
Service logic and Data Integration period assignment of the destination in data transmission procedure, data are enabled into destination
Before, carry out unified continuous data and integrate and clean.
Magnanimity measures the collection of related data, is mainly extracted from power information acquisition system, sales service system, PMS
Go out periodic duty and the taiwan area data of measuring equipment.
In step 1), the content of data storage is as follows：The data according to needed for grid structure, are divided into static data and dynamic number
According to two classifications；When based on facility information static data (mainly have distribution transforming information, line information, on-pole switch information,
Transformer information etc.), static data information updating frequency is low, and digital independent amount is small, is uniformly stored in this partial data for this
In the DataNode databases of single node, it is easy to digital independent；Second, the dynamic data based on operating load, this part number
High according to change update frequency, real time data update frequency individually deposits it for this up to 15 minutes, 10 minutes, 1 minute or even second level
Put, maximize the reading performance for improving data.
In step 2), the content for establishing Strong association rule fault indices is：Establish measuring equipment (predominantly transformer)
Important fault impact index analysis system, by the relation deployment analysis between important fault impact index, establish these indexs
The Strong association rule of occurrence of equipment failure.
In step 2), the content of the frequent set based algorithm analytical model of fault section is as follows：Intelligent knowledge is carried out by computer
Not, on the basis of Strong association rule fault indices are established, for caused by each measuring equipment index parameter progress primary fault
The judgement in section.
In step 2), the judgment step in section caused by carrying out primary fault is as follows：First, to the fault indices data of reality
Judged, when judging each index is in what interval range, the number of generating state failure is relatively more, the time dimension tool of statistics
Body is to the time；Then, according to the scope of each index judged, by each measuring equipment index (per day magnitude of voltage, day
Average current value, rated current no-load voltage ratio, rated capacity, the time limit that puts into operation etc.) demarcation interval, in interval range arranged below one to two
Individual section, in one to two sections of interval range arrangement above.Specific algorithm is as follows：
Algorithm produces frequent 1 item collection L by scanning first1, in K (K>1) before secondary scanning, first with K-1 scanning
As a result (i.e. frequent K-1 item collections Lk-1) and frequent episode search function produce K items candidate's frequent item set Ck, it is then determined that CkIn
The support values of each single item element, followed by frequent 2 item collection L2, until there is some k value to cause LkFor sky, then algorithm terminates.
Such as transformer section explanation in following table 10kV main websites：
Explanation:(1) according to actual conditions, in above table, I1 to I6 occurs in each affairs and only occurred once；I7
Occur in each affairs and only occur once to I12；I13 to I17 occurs in each affairs and only occurred once；I18 is extremely
I22 occurs in each affairs and only occurred once；I23 will judge according to whether actual metered equipment breaks down.Can be with
By above actual conditions, in calculating process, the constraint of condition is carried out to algorithm, so as to reduce the complexity of calculating.Database
In each transaction packet contain 4 items or 5 items.(2) set of some Strong association rules finally occurs, because what is discussed is
The relation of faulty equipment and other specification, final Strong association rule collection is screened, retain and occurred in implications with equipment
The Strong association rule of failure conclusion, such as：Retain Strong association ruleThis Rule Expression：
Every 15 minutes average current values 700-800A, every 15 minutes average voltage levels in 4000-5000V, rated current
No-load voltage ratio [2,3), rated capacity (150,200], device fails, this five simultaneous probability of item be 30% (support
Degree).
The present invention is established based on the state estimation algorithm under measuring equipment big data, according to different main website types, is passed through
The state estimation of all types of transformers is realized to the different application of algorithm.Analyzed by Data Integration from source to destination-
Data supporting-algorithm application-the Visual Implementation based on big data is measured, completes the cluster mining analysis to measuring equipment,
Breach the bottleneck of conventional single analysis.
Embodiment 2
The present embodiment provides a kind of measuring equipment status assessing system based on big data, and it includes：
Data Integration and memory cell：For integrating and storing magnanimity metering related data；
Algorithm Analysis unit：Establish Strong association rule fault indices and establish the fault section frequency based on mass historical data
Numerous set based algorithm analytical model；
Measuring equipment state evaluation unit：Based on the frequent set based algorithm analytical model of fault section, pass through visual analyzing
Mode carries out default device state assessment to the main website of each voltage class and all types of transformers.
The general principle and principal character and advantages of the present invention of the present invention has been shown and described above.The technology of the industry
Personnel are it should be appreciated that the present invention is not limited to the above embodiments, and the simply explanation described in above-described embodiment and specification is originally
The principle of invention, without departing from the spirit and scope of the present invention, various changes and modifications of the present invention are possible, these changes
Change and improvement all fall within the protetion scope of the claimed invention.The claimed scope of the invention by appended claims and its
Equivalent thereof.
Claims (8)
1. a kind of measuring equipment state evaluating method based on big data, it is characterised in that including step：
1) magnanimity metering related data is integrated and stored；
2) the Algorithm Analysis model based on metering big data is established, including establishes Strong association rule fault indices and establishes based on sea
Measure the frequent set based algorithm analytical model of fault section of historical data；
3) measuring equipment state estimation：Based on the frequent set based algorithm analytical model of fault section, pass through visual analyzing mode pair
The main website of each voltage class and all types of transformers carry out default device state assessment.
2. measuring equipment state evaluating method according to claim 1, it is characterised in that in step 1), Data Integration
Content is as follows：Based on existing data fusion pattern and basis, pass through mass data center extraction continuous data；
The data access at mass data center mainly carries out data docking by ETL related tools, passes through source and purpose
Service logic and Data Integration period assignment in data transmission procedure are held, enables to data to enter before destination is entered
The unified continuous data of row is integrated and cleaning.
3. measuring equipment state evaluating method according to claim 2, it is characterised in that magnanimity metering related data is adopted
Collection, periodic duty and the taiwan area number of measuring equipment are mainly extracted from power information acquisition system, sales service system, PMS
According to.
4. according to the measuring equipment state evaluating method described in claim any one of 1-3, it is characterised in that in step 1), number
It is as follows according to the content of storage：
The data according to needed for grid structure, it is divided into static data and two classifications of dynamic data；First, based on facility information
Static data, static data information updating frequency is low, and digital independent amount is small, and this partial data is uniformly stored in into single section for this
In the DataNode databases of point, it is easy to digital independent；Second, the dynamic data based on operating load, the change of this partial data
Update frequency is high, individually deposits it for this, maximizes the reading performance for improving data.
5. according to the measuring equipment state evaluating method described in claim any one of 1-3, it is characterised in that in step 2), build
The content of vertical Strong association rule fault indices is：
The important fault impact index analysis system of measuring equipment is established, is deployed by the relation between important fault impact index
Analysis, establish the Strong association rule of these index occurrence of equipment failures.
6. according to the measuring equipment state evaluating method described in claim any one of 1-3, it is characterised in that in step 2), therefore
The content for hindering the frequent set based algorithm analytical model in section is as follows：Intelligent Recognition is carried out by computer, is establishing Strong association rule
On the basis of fault indices, the judgement in section caused by carrying out primary fault for each measuring equipment index parameter.
7. measuring equipment state evaluating method according to claim 6, it is characterised in that in step 2), carry out once event
The judgment step in section caused by barrier is as follows：
First, the fault indices data of reality are judged, when judging each index is in what interval range, generating state failure
Number it is relatively more, the time dimension of statistics is specific to the time；Then, will be each according to the scope of each index judged
Individual measuring equipment index demarcation interval, in interval range one to two sections arranged below, arrived in interval range arrangement above one
Two sections.
A kind of 8. measuring equipment status assessing system based on big data, it is characterised in that including：
Data Integration and memory cell：For integrating and storing magnanimity metering related data；
Algorithm Analysis unit：Establish Strong association rule fault indices and establish the fault section Frequent Set based on mass historical data
Hop algorithm analytical model；
Measuring equipment state evaluation unit：Based on the frequent set based algorithm analytical model of fault section, pass through visual analyzing mode
Main website and all types of transformers to each voltage class carry out default device state assessment.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109191011A (en) * | 2018-10-12 | 2019-01-11 | 中国电力科学研究院有限公司 | A kind of DC bushing state evaluating method and device based on Apriori algorithm |
| CN111817265A (en) * | 2020-06-29 | 2020-10-23 | 北京智芯微电子科技有限公司 | Low-voltage transformer area power distribution protection method and system |
| CN114236448A (en) * | 2021-11-23 | 2022-03-25 | 国网山东省电力公司日照供电公司 | Metering device troubleshooting system based on big data |
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| CN104679828A (en) * | 2015-01-19 | 2015-06-03 | 云南电力调度控制中心 | Rules-based intelligent system for grid fault diagnosis |
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| CN109191011A (en) * | 2018-10-12 | 2019-01-11 | 中国电力科学研究院有限公司 | A kind of DC bushing state evaluating method and device based on Apriori algorithm |
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Address after: The eight district of Hangzhou city in Zhejiang province 310014 Huadian Zhaohui under No. 1 Lane Patentee after: STATE GRID ZHEJIANG ELECTRIC POWER COMPANY LIMITED ELECTRIC POWER Research Institute Patentee after: State Grid Zhejiang Electric Power Co., Ltd. Huzhou Power Supply Co. Patentee after: JIAXING POWER SUPPLY COMPANY OF STATE GRID ZHEJIANG ELECTRIC POWER Co.,Ltd. Patentee after: STATE GRID CORPORATION OF CHINA Patentee after: STATE GRID INFORMATION & TELECOMMUNICATION GROUP Co.,Ltd. Patentee after: FUJIAN YIRONG INFORMATION TECHNOLOGY Co.,Ltd. Address before: The eight district of Hangzhou city in Zhejiang province 310014 Huadian Zhaohui under No. 1 Lane Patentee before: ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID ZHEJIANG ELECTRIC POWER Co. Patentee before: HUZHOU POWER SUPPLY COMPANY OF STATE GRID ZHEJIANG ELECTRIC POWER Co. Patentee before: JIAXING POWER SUPPLY COMPANY OF STATE GRID ZHEJIANG ELECTRIC POWER Co. Patentee before: State Grid Corporation of China Patentee before: STATE GRID INFORMATION & TELECOMMUNICATION GROUP Co.,Ltd. Patentee before: FUJIAN YIRONG INFORMATION TECHNOLOGY Co.,Ltd. |
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| TR01 | Transfer of patent right |
Effective date of registration: 20220510 Address after: 311100 Building 5, 138 Yunlian Road, Yuhang District, Hangzhou City, Zhejiang Province Patentee after: Marketing service center of State Grid Zhejiang Electric Power Co.,Ltd. Patentee after: State Grid Zhejiang Electric Power Co., Ltd. Huzhou Power Supply Co. Patentee after: JIAXING POWER SUPPLY COMPANY OF STATE GRID ZHEJIANG ELECTRIC POWER Co.,Ltd. Patentee after: STATE GRID CORPORATION OF CHINA Patentee after: STATE GRID INFORMATION & TELECOMMUNICATION GROUP Co.,Ltd. Patentee after: FUJIAN YIRONG INFORMATION TECHNOLOGY Co.,Ltd. Address before: The eight district of Hangzhou city in Zhejiang province 310014 Huadian Zhaohui under No. 1 Lane Patentee before: STATE GRID ZHEJIANG ELECTRIC POWER COMPANY LIMITED ELECTRIC POWER Research Institute Patentee before: State Grid Zhejiang Electric Power Co., Ltd. Huzhou Power Supply Co. Patentee before: JIAXING POWER SUPPLY COMPANY OF STATE GRID ZHEJIANG ELECTRIC POWER Co.,Ltd. Patentee before: STATE GRID CORPORATION OF CHINA Patentee before: STATE GRID INFORMATION & TELECOMMUNICATION GROUP Co.,Ltd. Patentee before: FUJIAN YIRONG INFORMATION TECHNOLOGY Co.,Ltd. |