CN108009940A - Same period line loss exception analysis method and system based on Tableau - Google Patents
Same period line loss exception analysis method and system based on Tableau Download PDFInfo
- Publication number
- CN108009940A CN108009940A CN201711101746.1A CN201711101746A CN108009940A CN 108009940 A CN108009940 A CN 108009940A CN 201711101746 A CN201711101746 A CN 201711101746A CN 108009940 A CN108009940 A CN 108009940A
- Authority
- CN
- China
- Prior art keywords
- line loss
- analysis
- data
- tableau
- same period
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 102
- 230000002159 abnormal effect Effects 0.000 claims abstract description 51
- 230000000007 visual effect Effects 0.000 claims abstract description 36
- 238000000034 method Methods 0.000 claims abstract description 13
- 238000007405 data analysis Methods 0.000 claims abstract description 6
- 238000005065 mining Methods 0.000 claims abstract description 4
- 238000009826 distribution Methods 0.000 claims description 25
- 238000013461 design Methods 0.000 claims description 12
- 238000012795 verification Methods 0.000 claims description 9
- 238000012800 visualization Methods 0.000 claims description 9
- 238000012216 screening Methods 0.000 claims description 7
- 238000007418 data mining Methods 0.000 claims description 6
- 238000007621 cluster analysis Methods 0.000 claims description 5
- 230000008859 change Effects 0.000 claims description 4
- 238000010586 diagram Methods 0.000 claims description 2
- 238000012384 transportation and delivery Methods 0.000 claims description 2
- 239000013589 supplement Substances 0.000 description 6
- 238000007726 management method Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 1
- 230000003796 beauty Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000013079 data visualisation Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 239000004744 fabric Substances 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000013439 planning Methods 0.000 description 1
- 239000000047 product Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Classifications
-
- 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—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a kind of same period line loss exception analysis method and system based on Tableau.The present invention is inputted by data of grid line loss relevant rudimentary data message, use Tableau visual analyzing softwares, big data analysis is carried out using clustering method, analyze abnormal cause, the abnormity point position is positioned, across comparison the line loss situation of commensurate and is not analyzed, so that auxiliary problem (AP) is found, problem rule is looked for, optimizes Controlling line loss flow and raising efficiency.The present invention passes through the line loss Outlier mining to power grid basic information data, look for line loss abnormal problem node, mode is visualized using the big data based on Tableau, the visual analyzing of transverse direction and longitudinal direction is carried out to same period quartile damage situation, auxiliary reference is provided to lift the management level of grid line loss.
Description
Technical field
The invention belongs to grid line loss anomaly analysis technical field, specifically a kind of same period line based on Tableau
Damage exception analysis method and system.
Background technology
Line loss per unit index is used for the economy for examining Operation of Electric Systems, concentrated expression Study on Power Grid Planning, production run, link
Loss and the main economic technical indicator of management level.Same period line loss management analysis proposes higher to the management of line loss lean
It is required that test between the infrastructure device management of power grid, intelligent electric meter coverage condition, collection successful instance and each operation system
Penetrate through promptness, the validity of interconnection.
At present, traditional line loss anomaly analysis mainly carries out four points to data such as marketing and metering systems to critical point electricity etc.
Line loss statistical analysis, and the number that has six big operation systems and three large platforms, different system and platform associated with same period line loss
It is different according to library structure, and the perforation correctness of data transfer, incidence relation needs the coordinated of multi-disciplinary department, line loss is abnormal
Analysis difficulty, and analytical cycle is longer.
Record in database can be divided into a series of significant subsets, that is, cluster.Cluster analysis is a kind of changeable
Statistical technique is measured, is one of main means of data mining analysis.It is that one group of data is divided into according to similitude and otherness
Several classifications, it is as big as possible the purpose is to belong to the similitude between same category of data, it is different classes of in data between
Similitude it is as small as possible.
Quick BI is to take light weight modeling, the method for N number of view, does not build secondary instrument, data are repeatedly kicked into can directly carry out
Analysis, and business personnel can adjust the dimension of analysis and the calculation of measurement in real time, and flexibility is significantly greatly increased, really does
Arrive and data session.Big data visualizes, and just refers to structure or non-structural data conversion into appropriate Visual Chart, then
The information that would fit snugly within data directly shows in face of people.
Tableau is that a business intelligence for being positioned at data visualization agile development and realizing shows instrument, for reality
Now interaction, visual analysis and instrument board application, so that help enterprise that data are rapidly appreciated and understood, it is continuous to tackle
The market environment of change and challenge.Tableau product advantages：It is easy to use, light left-hand seat；It is very fast using memory calculating basis
Efficiently；The beautiful interaction of picture view, can drill through to bottom；Nearly all categorical data source is connected on data acquisition；Easily
Conveniently realize data fusion；Managed during installation, maintenance, use simple and convenient；Published method is various, multi-faceted authority control
System.So as to can reach, a non-professional BI staff can also complete a specialty in a short period of time, accurate, beauty is real
Report and analysis work.
The content of the invention
The object of the present invention is to provide a kind of same period line loss exception analysis method based on Tableau, it applies Tableau
Visual analyzing software and use clustering method carry out anomaly statistics analysis to power grid same period line loss, look for problem rule,
To optimize Controlling line loss flow and raising efficiency.
For this reason, the present invention adopts the following technical scheme that：Same period line loss exception analysis method based on Tableau, its with
Grid line loss relevant rudimentary data message inputs for data, using Tableau visual analyzing softwares, using clustering method
Big data analysis is carried out, analyzes abnormal cause, positions the abnormity point position, across comparison the line loss situation of commensurate and is not divided
Analysis, so that auxiliary problem (AP) is found, looks for problem rule, optimizes Controlling line loss flow and raising efficiency.
Cluster analysis has been widely used in the power domain of big data, passes through the business number damaged to power grid same period quartile
According to statistical analysis, comprehensive various clustering methods, carry out horizontal and vertical data mining, find line loss data exception,
Trouble node is looked for, improves the line loss quality of data, optimizes Controlling line loss flow and raising efficiency.
As the supplement of above-mentioned technical proposal, above-mentioned same period line loss exception analysis method, including step：
Using clustering method, the cluster of different dimensions is carried out to grid line loss relevant rudimentary data message, it is found that line loss is different
Chang Wenti；
By the analysis result of line loss abnormal problem, abnormal problem is excavated in longitudinal direction, positions abnormal nodes；
According to the analysis result of line loss abnormal problem, design line loss anomaly analysis visualizes scheme；
Scheme is visualized according to line loss anomaly analysis, the analysis result of line loss abnormal problem is verified.
As the supplement of above-mentioned technical proposal, above-mentioned same period line loss exception analysis method, including step：
1）Same period line loss based on Tableau builds/connects grid line loss relevant rudimentary information database；
2）Connect data source：Distribution line loss detail list in power grid basic information database is connected to Tableau Desktop
In software, and basic adjustment is carried out to the data source of connection, data are ready by analysis demand；
3）Data analysis：Using multidimensional clustering analysis method, corresponding visualization point is established using Tableau desktop utilities
Worksheet is analysed, distribution line loss anomaly analysis is carried out to the data source after adjustment；
4）Multidimensional clustering is analyzed：The Main Analysis dimension of multidimensional clustering analysis is determined, using data screening condition, to each distribution line
The different dimensions of loss rate carry out comprehensive analysis, and the abnormal conditions in mining data, position abnormal line loss per unit Factor minute and layout, find
Anomaly regularity, excavates producing cause；
5）According to distribution line loss data mining analysis as a result, design line loss anomaly analysis visual presentation scheme, determine point
Analyse flow, visualization view selection and view linkage rule.
Above-mentioned same period line loss exception analysis method, further includes step：
6）According to the scheme of visual presentation, visual analyzing instrument board is configured using Tableau Desktop, and to analysis result
Carry out result verification.
Above-mentioned same period line loss exception analysis method, further includes step：
7）The distribution line loss visual analyzing result made in Tableau Desktop is published in Tableau Server,
And corresponding data and view authority are distributed to Tableau Server accounts according to the role of user.
As the supplement of above-mentioned technical proposal, step 2）In, the basis adjustment mainly includes setting starting SQL, adjusts
Entire data field type and setting data source screening washer adjustment data area.
As the supplement of above-mentioned technical proposal, step 4）In, the Main Analysis dimension includes distribution delivery, load
Amount, date, region, public specially change accounting and three-phase imbalance rate.
As the supplement of above-mentioned technical proposal, step 5）In, the data alternative condition is visualization view and sieve
Device is selected, the visualization view is bar chart, line chart, pie chart and scatter diagram.
As the supplement of above-mentioned technical proposal, the grid line loss relevant rudimentary data message includes multi-source equipment files
Information and quartile damage result data.
It is a further object of the present invention to provide a kind of same period line loss exception analysis system based on Tableau, it includes：
Cluster analysis unit：Using clustering method, different dimensions are carried out to grid line loss relevant rudimentary data message and are gathered
Class, finds line loss abnormal problem；
Abnormal nodes positioning unit：By the analysis result of line loss abnormal problem, abnormal problem is excavated in longitudinal direction, positions abnormal section
Point；
The visual design unit：According to the analysis result of line loss abnormal problem, design line loss anomaly analysis visualizes scheme；
Anomaly analysis result verification unit：Scheme is visualized according to line loss anomaly analysis, is matched somebody with somebody using Tableau Desktop
Visual analyzing instrument board is put, and result verification is carried out to the analysis result of line loss abnormal problem；
Visual configuration unit：The distribution line loss visual analyzing result made in Tableau Desktop is published to
In Tableau Server, and corresponding data and view power are distributed Tableau Server accounts according to the role of user
Limit；
As a result output unit：For exporting the distribution line loss visual analyzing result made in Tableau Desktop.
It is the device have the advantages that as follows：The present invention is by digging the line loss abnormal data of power grid basic information data
Pick, looks for line loss abnormal problem node, visualizes mode using the big data based on Tableau, same period quartile is damaged
Situation carry out transverse direction and longitudinal direction visual analyzing, to pinpoint the problems as core, for the purpose of Added Management, using raising efficiency as
As a result, provide auxiliary reference for optimization Controlling line loss flow.
Brief description of the drawings
Fig. 1 is the flow chart of same period line loss exception analysis method of the invention.
Embodiment
With reference to specification drawings and specific embodiments, the invention will be further described.
Embodiment 1
The present embodiment provides a kind of same period line loss exception analysis method based on Tableau, it includes the following steps：
（1）Structure/connection power grid basic information database, predominantly influences the various factors of line loss exception, this sentences distribution and is
Example is abnormal to the extremely high damage data mining of distribution and positioning based on Tableau emphasis.
（2）Connect data source.By the distribution line loss detail list in power grid basic information database（Table set）It is connected to
In Tableau desktop utilities, and basic adjustment is carried out to the data source of connection, it is main to include setting starting SQL, adjustment number
According to field type, data source screening washer adjustment data area etc. is set, data are ready by analysis demand.
（3）Data analysis, it is soft by using Tableau Desktop mainly using analysis methods such as multidimensional clustering analyses
Part establishes corresponding visual analyzing worksheet, and distribution line loss anomaly analysis is carried out to the data after adjustment.
（4）Multidimensional clustering is analyzed：The Main Analysis dimension of multidimensional clustering analysis is determined, using data screening condition, to each
The different dimensions of distribution line loss per unit carry out comprehensive analysis, and the abnormal conditions in mining data, position abnormal line loss per unit Factor minute cloth
Point, note abnormalities rule, excavates producing cause.
（5）According to distribution line loss data mining analysis as a result, design line loss anomaly analysis visual presentation scheme, really
Setting analysis flow, visualization view selection and view linkage rule etc..
（6）According to the scheme of visual presentation, visual analyzing instrument board is configured using Tableau Desktop, and to dividing
Analyse result and carry out result verification, ensure the correctness and availability of abnormal results.
（7）The distribution line loss visual analyzing result made in Tableau Desktop is published to Tableau
In Server, and corresponding data and view authority are distributed to Tableau Server accounts according to the role of user.
Embodiment 2
The present embodiment provides a kind of above-mentioned same period line loss exception analysis method, including step：
Using clustering method, the cluster of different dimensions is carried out to grid line loss relevant rudimentary data message, it is found that line loss is different
Chang Wenti；
By the analysis result of line loss abnormal problem, abnormal problem is excavated in longitudinal direction, positions abnormal nodes；
According to the analysis result of line loss abnormal problem, design line loss anomaly analysis visualizes scheme；
Scheme is visualized according to line loss anomaly analysis, the analysis result of line loss abnormal problem is verified.
Embodiment 3
The present embodiment provides a kind of same period line loss exception analysis system based on Tableau, it includes：
Cluster analysis unit：Using clustering method, different dimensions are carried out to grid line loss relevant rudimentary data message and are gathered
Class, finds line loss abnormal problem；
Abnormal nodes positioning unit：By the analysis result of line loss abnormal problem, abnormal problem is excavated in longitudinal direction, positions abnormal nodes；
The visual design unit：According to the analysis result of line loss abnormal problem, design line loss anomaly analysis visualizes scheme；
Anomaly analysis result verification unit：Scheme is visualized according to line loss anomaly analysis, is matched somebody with somebody using Tableau Desktop
Visual analyzing instrument board is put, and result verification is carried out to the analysis result of line loss abnormal problem；
Visual configuration unit：The distribution line loss visual analyzing result made in Tableau Desktop is published to
In Tableau Server, and corresponding data and view authority are distributed to Tableau Server accounts according to the role of user；
As a result output unit：For exporting the distribution line loss visual analyzing result made in Tableau Desktop.
The basic principles, main features and the advantages of the invention have 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 above embodiments and description only describe this
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 (10)
1. the same period line loss exception analysis method based on Tableau, it is characterised in that
Inputted by data of grid line loss relevant rudimentary data message, using Tableau visual analyzing softwares, using cluster point
Analysis method carry out big data analysis, analyze abnormal cause, position the abnormity point position, across comparison not commensurate line loss situation simultaneously
Analyzed, so that auxiliary problem (AP) is found, look for problem rule, optimize Controlling line loss flow and raising efficiency.
2. same period line loss exception analysis method according to claim 1, it is characterised in that including step：
Using clustering method, the cluster of different dimensions is carried out to grid line loss relevant rudimentary data message, it is found that line loss is different
Chang Wenti；
By the analysis result of line loss abnormal problem, abnormal problem is excavated in longitudinal direction, positions abnormal nodes；
According to the analysis result of line loss abnormal problem, design line loss anomaly analysis visualizes scheme；
Scheme is visualized according to line loss anomaly analysis, the analysis result of line loss abnormal problem is verified.
3. same period line loss exception analysis method according to claim 1, it is characterised in that including step：
1）Structure/connection grid line loss relevant rudimentary information database；
2）Connect data source：Distribution line loss detail list in power grid basic information database is connected to Tableau Desktop
In software, and basic adjustment is carried out to the data source of connection, data are ready by analysis demand；
3）Data analysis：Using multidimensional clustering analysis method, corresponding visualization point is established using Tableau desktop utilities
Worksheet is analysed, distribution line loss anomaly analysis is carried out to the data source after adjustment；
4）Multidimensional clustering is analyzed：The Main Analysis dimension of multidimensional clustering analysis is determined, using data screening condition, to each distribution line
The different dimensions of loss rate carry out comprehensive analysis, and the abnormal conditions in mining data, position abnormal line loss per unit Factor minute and layout, find
Anomaly regularity, excavates producing cause；
5）According to distribution line loss data mining analysis as a result, design line loss anomaly analysis visual presentation scheme, determine point
Analyse flow, visualization view selection and view linkage rule.
4. same period line loss exception analysis method according to claim 3, it is characterised in that further include step：
6）According to the scheme of visual presentation, visual analyzing instrument board is configured using Tableau Desktop, and to analysis result
Carry out result verification.
5. same period line loss exception analysis method according to claim 4, it is characterised in that further include step：
7）The distribution line loss visual analyzing result made in Tableau Desktop is published in Tableau Server,
And corresponding data and view authority are distributed to Tableau Server accounts according to the role of user.
6. same period line loss exception analysis method according to claim 3, it is characterised in that step 2）In, the basis
Adjustment mainly includes setting starting SQL, adjustment data field type and sets data source screening washer adjustment data area.
7. same period line loss exception analysis method according to claim 3, it is characterised in that step 4）In, described is main
Analyzing dimension includes distribution delivery, load, date, region, public specially change accounting and three-phase imbalance rate.
8. same period line loss exception analysis method according to claim 3, it is characterised in that step 5）In, the data
Alternative condition is visualization view and screening washer, and the visualization view is bar chart, line chart, pie chart and scatter diagram.
9. according to claim 1-3 any one of them same period line loss exception analysis methods, it is characterised in that the grid line
Damaging relevant rudimentary data message includes multi-source device File Information and quartile damage result data.
10. the same period line loss exception analysis system based on Tableau, it is characterised in that including：
Cluster analysis unit：Using clustering method, different dimensions are carried out to grid line loss relevant rudimentary data message and are gathered
Class, finds line loss abnormal problem；
Abnormal nodes positioning unit：By the analysis result of line loss abnormal problem, abnormal problem is excavated in longitudinal direction, positions abnormal section
Point；
The visual design unit：According to the analysis result of line loss abnormal problem, design line loss anomaly analysis visualizes scheme；
Anomaly analysis result verification unit：Scheme is visualized according to line loss anomaly analysis, is matched somebody with somebody using Tableau Desktop
Visual analyzing instrument board is put, and result verification is carried out to the analysis result of line loss abnormal problem；
Visual configuration unit：The distribution line loss visual analyzing result made in Tableau Desktop is published to
In Tableau Server, and corresponding data and view power are distributed Tableau Server accounts according to the role of user
Limit；
As a result output unit：For exporting the distribution line loss visual analyzing result made in Tableau Desktop.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711101746.1A CN108009940A (en) | 2017-11-10 | 2017-11-10 | Same period line loss exception analysis method and system based on Tableau |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711101746.1A CN108009940A (en) | 2017-11-10 | 2017-11-10 | Same period line loss exception analysis method and system based on Tableau |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108009940A true CN108009940A (en) | 2018-05-08 |
Family
ID=62051550
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711101746.1A Pending CN108009940A (en) | 2017-11-10 | 2017-11-10 | Same period line loss exception analysis method and system based on Tableau |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108009940A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109857784A (en) * | 2019-02-12 | 2019-06-07 | 吉林师范大学 | A kind of big data statistical analysis system |
| CN109885051A (en) * | 2019-02-21 | 2019-06-14 | 彭劲松 | A kind of ecological environment health quality appraisal procedure |
| CN109902133A (en) * | 2019-03-06 | 2019-06-18 | 北京中电普华信息技术有限公司 | Multi-source data correction processing method and system based on power grid arbitrary segmentation area |
| CN110212523A (en) * | 2019-06-06 | 2019-09-06 | 国网北京市电力公司 | The detection method of the mode that supplies power with double circuit in line loss power supply relationship |
| CN110597792A (en) * | 2019-06-24 | 2019-12-20 | 国网甘肃省电力公司电力科学研究院 | Multistage redundant data fusion method and device based on synchronous line loss data fusion |
| CN110633269A (en) * | 2019-08-02 | 2019-12-31 | 国家电网公司华东分部 | Device parameter data standardization method based on Tableau |
| CN112116152A (en) * | 2020-09-17 | 2020-12-22 | 北京中恒博瑞数字电力科技有限公司 | Line loss double-rate optimization method and system based on synchronous data |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100681538B1 (en) * | 2005-10-27 | 2007-02-09 | 한국전력공사 | System for monitoring real-time power consumption |
| CN102969711A (en) * | 2012-11-22 | 2013-03-13 | 安徽省电力公司黄山供电公司 | Line loss control system |
| CN103258256A (en) * | 2013-04-12 | 2013-08-21 | 国家电网公司 | Cable line condition monitoring multi-dimensional visual management platform |
| CN104751369A (en) * | 2013-12-25 | 2015-07-01 | 南京理工大学 | Improved line loss analysis method for K-means clustering |
| CN106102161A (en) * | 2016-05-30 | 2016-11-09 | 天津大学 | Based on the data-optimized indoor orientation method of focusing solutions analysis |
| CN106447209A (en) * | 2016-10-11 | 2017-02-22 | 上海博辕信息技术服务有限公司 | Determination method and device for evaluation factor of power distribution network project |
-
2017
- 2017-11-10 CN CN201711101746.1A patent/CN108009940A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100681538B1 (en) * | 2005-10-27 | 2007-02-09 | 한국전력공사 | System for monitoring real-time power consumption |
| CN102969711A (en) * | 2012-11-22 | 2013-03-13 | 安徽省电力公司黄山供电公司 | Line loss control system |
| CN103258256A (en) * | 2013-04-12 | 2013-08-21 | 国家电网公司 | Cable line condition monitoring multi-dimensional visual management platform |
| CN104751369A (en) * | 2013-12-25 | 2015-07-01 | 南京理工大学 | Improved line loss analysis method for K-means clustering |
| CN106102161A (en) * | 2016-05-30 | 2016-11-09 | 天津大学 | Based on the data-optimized indoor orientation method of focusing solutions analysis |
| CN106447209A (en) * | 2016-10-11 | 2017-02-22 | 上海博辕信息技术服务有限公司 | Determination method and device for evaluation factor of power distribution network project |
Non-Patent Citations (1)
| Title |
|---|
| 赵凌伟: "浑源县供电公司线损管理系统分析与决策", 《万方数据知识服务平台》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109857784A (en) * | 2019-02-12 | 2019-06-07 | 吉林师范大学 | A kind of big data statistical analysis system |
| CN109885051A (en) * | 2019-02-21 | 2019-06-14 | 彭劲松 | A kind of ecological environment health quality appraisal procedure |
| CN109902133A (en) * | 2019-03-06 | 2019-06-18 | 北京中电普华信息技术有限公司 | Multi-source data correction processing method and system based on power grid arbitrary segmentation area |
| CN110212523A (en) * | 2019-06-06 | 2019-09-06 | 国网北京市电力公司 | The detection method of the mode that supplies power with double circuit in line loss power supply relationship |
| CN110597792A (en) * | 2019-06-24 | 2019-12-20 | 国网甘肃省电力公司电力科学研究院 | Multistage redundant data fusion method and device based on synchronous line loss data fusion |
| CN110633269A (en) * | 2019-08-02 | 2019-12-31 | 国家电网公司华东分部 | Device parameter data standardization method based on Tableau |
| CN112116152A (en) * | 2020-09-17 | 2020-12-22 | 北京中恒博瑞数字电力科技有限公司 | Line loss double-rate optimization method and system based on synchronous data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108009940A (en) | Same period line loss exception analysis method and system based on Tableau | |
| CN110362894B (en) | Distribution line and transformer comprehensive loss reduction and energy saving calculation analysis method and system | |
| CN105574652B (en) | Intelligent power distribution network planning big data management and control system and method | |
| CN107402976A (en) | Power grid multi-source data fusion method and system based on multi-element heterogeneous model | |
| CN105574593B (en) | Track state static detection and control system and method based on cloud computing and big data | |
| CN107680194A (en) | A kind of information system for power enterprise automates cruising inspection system | |
| CN108932589A (en) | A kind of IT application in enterprises project implementation management system | |
| CN204288281U (en) | Industrial enterprise's electric energy management system | |
| CN106210013A (en) | A kind of integrated circuit testing information integration based on high in the clouds analyzes system and method | |
| CN109740872A (en) | The diagnostic method and system of a kind of area's operating status | |
| CN105354680B (en) | GIS-based power grid section auxiliary determination analysis method | |
| CN106228300A (en) | A kind of distributed power source operation management system | |
| CN109858886A (en) | It is a kind of that control success rate promotion analysis method is taken based on integrated study | |
| CN107066500A (en) | A kind of electrical network mass data quality indicator method based on PMS models | |
| CN106126674A (en) | A kind of industry service information analysis report-generating method and system thereof | |
| CN107025288A (en) | Distributed data digging method and system | |
| CN111046519A (en) | Application analysis method of artificial intelligence technology in electric energy meter error diagnosis | |
| CN112598234A (en) | Low-voltage transformer area line loss abnormity analysis method, device and equipment | |
| CN109324978A (en) | A kind of software testing management system of multi-person synergy | |
| CN108694448A (en) | PHM platforms | |
| CN115908046A (en) | Visual power distribution system based on airport terminal building BIM | |
| CN106251094A (en) | A kind of 10kV Business Process System work order unusual fluctuation analytical equipment and the method for analysis | |
| CN109903008B (en) | Visual management and control system and method for subway construction project progress | |
| CN107544895A (en) | Hadoop big datas platform testing system and method | |
| CN116894639B (en) | Multi-information fusion plan management system and method for Internet of things |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180508 |