CN109241169A - The multi-source heterogeneous data fusion geo-database integration method of power distribution network operation information - Google Patents
The multi-source heterogeneous data fusion geo-database integration method of power distribution network operation information Download PDFInfo
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Abstract
The present invention relates to domain of data fusion, specifically disclose a kind of multi-source heterogeneous data fusion geo-database integration method of power distribution network operation information, specifically include: accessing the different service sub-systems of power grid according to demand to obtain target data set；Classify to target data set；Sorted target data is concentrated, according to the interrelated building topological analysis engine between distribution network and network original part；Meet based on topological analysis engine, in selection target data set in same time section, while voltage is consistent, electric current and power meet the data set of KCL law, rejects ungratified data set；It constructs data fusion model based on regularization residual error search method and rejects and merged after topological analysis treated bad data that target data concentrates.This method completes the fusion of multi-source heterogeneous data, solves extraction, the promotion of the integrated and quality of data of the multi-source heterogeneous operation information of distribution, realizes that the composite depth that intersects to power distribution network historical data and near-realtime data is analyzed.
Description
Technical field
The invention belongs to domain of data fusion, in particular to a kind of multi-source heterogeneous data fusion data of power distribution network operation information
Library integrated approach.
Background technique
As the whole world starts the research boom of big data analysis technology and artificial intelligence technology, power distribution network informatization is met
Driving force newly is carried out.Data are the bases of all advanced analysis application, and by different systemic origins, the number of different data structure
It is the premise for realizing big data analysis according to organically blend.
Distribution system as connection transmission system and terminal user tie, have point it is more, wire length, wide, structure is complicated,
The basic characteristics such as management link intersection.During power distribution network produces O&M, it is related to the asset management system, metering automation system
The dozens ofs operation system module such as system, dispatch automated system, each data among systems are independent of one another, and there are data barriers to ask
Topic.
In addition, with the deep development that smart grid is built, the major field of each operation system of power distribution network is different, construction when
Between it is different, framework is different, produce a large amount of metric data, business form data, account information data etc. in the process of running
Various structures, source are complicated, the different multi-source heterogeneous data of time scale disunity, space scale.According to statistics, a middle isotactic
Mould power distribution network will generate the data of TB up to a hundred every year, these data are mutually indepedent in respective operation system, not be able to achieve effectively
Fusion, data efficiency fail adequately to be excavated and played.To the power distribution network from different business systems, different types of structure
Production run data carry out effective integration, are the premises for realizing big data analysis, are to go deep into phase between excavation power distribution network operation data
The basis of pass relationship, to being pushed further into, distribution management upgrades, data-driven production is of great significance.
Summary of the invention
The purpose of the present invention is to provide a kind of multi-source heterogeneous data fusion geo-database integration method of power distribution network operation information,
To overcome, existing processing workload data is big, defect of low efficiency.
To achieve the above object, the present invention provides a kind of multi-source heterogeneous data fusion data base sets of power distribution network operation information
At method, specifically include:
S1 accesses the different service sub-systems of power grid according to demand to obtain target data set to form multi-source data
Collection；
S2 classifies to the target data set；
S3 concentrates the sorted target data, according to the interrelated structure between distribution network and network original part
Build topological analysis engine；
S4 is based on topological analysis engine, selects the target data to concentrate and meets in same time section, while voltage one
Cause, electric current and power meet the data set of KCL law, reject ungratified data set；
S5 constructs data fusion model based on regularization residual error search method and rejects through topological analysis treated number of targets
It is merged later according to the bad data of concentration.
Preferably, in above-mentioned technical proposal, if finding data exception, return step S4 after step S5 fusion.
Preferably, in above-mentioned technical proposal, step S2 is specifically included: to the target data set by voltage class, equipment
Type, acquisition measure type and classify.
Preferably, in above-mentioned technical proposal, the voltage class is divided into: 35kV, 20kV, 10kV；The device type is pressed
Different service sub-systems are divided into: transformer, switchgear, route；The acquisition measures type and is divided into: quantity of state and analog quantity,
Real time data and non-real-time data.
Preferably, in above-mentioned technical proposal, step S4 is specifically included: according to mutual between distribution network and network original part
Association building topological analysis engine specifically includes:
The foundation of topological analysis engine relies on following principle:
1) according to network connection relation, dependence is established to breaker, the switch in network；
2) the complementary information of network element: position of the switch information, the relationship for acquiring measurement；
3) network element is in the dependence of data, relied on including interim under different running method and fixed dependence,
Dependence, the dependence with historical data of the position of the switch and acquisition measurement；
4) alarm event of the network element in network operations information, SOE event, the information dependence for protecting system.
Compared with prior art, the multi-source heterogeneous data fusion geo-database integration method of power distribution network operation information of the present invention,
The distribution operation data provided using data-interface realizes the topological analysis of distribution network system, removes the data of mistake, then benefit
With the internal logic relationship of traditional Kirchhoff's theorem and each service sub-system, data fusion model is established, it is different to complete multi-source
The fusion of structure data solves the extraction of the multi-source heterogeneous operation information of distribution, the integrated and quality of data is promoted, and realizes to power distribution network
Historical data intersects composite depth analysis with near-realtime data.
Detailed description of the invention
Fig. 1 is the multi-source heterogeneous data fusion geo-database integration method flow diagram of power distribution network operation information according to the present invention.
Fig. 2 is the frame for the data process&analysis that spark is combined with MongoDB.
Specific embodiment
With reference to the accompanying drawing, specific embodiments of the present invention will be described in detail, it is to be understood that guarantor of the invention
Shield range is not limited by the specific implementation.
As shown in Figure 1, a kind of multi-source heterogeneous data fusion geo-database integration method of power distribution network operation information, specifically includes:
S1, using the frame of the big data processing and analysis of spark, MongoDB combination on technological layer, in service layer
Access the data base set of the different service sub-systems of power grid according to demand to obtain one or more target data sets to be formed
Multi-source data collection.
S2 classifies to target data set.Voltage class, device type are pressed to target data set, acquisition measures type
Classify.Voltage class is divided into: 35kV, 20kV, 10kV；Device type is divided by different service sub-systems: transformer is opened
Close cabinet, route；Acquisition measures type and is divided into: quantity of state and analog quantity, real time data and non-real-time data.
S3 concentrates sorted target data, opens up according to the interrelated building between distribution network and network original part
Flutter analysis engine.
The foundation of topological analysis engine relies on following principle:
1) according to network connection relation, dependence is established to breaker, the switch in network；
2) the complementary information of network element: position of the switch information, the relationship for acquiring measurement；
3) network element is in the dependence of data, relied on including interim under different running method and fixed dependence,
Dependence, the dependence with historical data of the position of the switch and acquisition measurement；
4) alarm event of the network element in network operations information, SOE event, the information dependence for protecting system.
S4 is met in selection target data set in same time section, while voltage based on the analysis of topological analysis engine
Unanimously, electric current and power meet the data set of KCL law, reject ungratified data set.
Fusion Model is established to multi-source heterogeneous data.Historical data, steady state data to mutual redundancy, take different power
Weight coefficient is calculated, and can also be designed a model according to the geometry characteristic distributions of priori data or these physical quantitys, using flat
The method of mean value is merged.
The data fusion model for meeting KCL law based on S4 building, establishes data filtering matching rule, from a large amount of history
According to model partition, fusion in data, steady state data, fused data set is through S5 step-by-step analysis.
S5 constructs data fusion model based on regularization residual error search method and rejects through topological analysis treated the mesh
It is merged after bad data in mark data set, if finding data exception after step S5 fusion, return step S4 can be into
Row repeatedly recycles.
Using regularization residual error search method, classified according to the difference in data set between actual value and estimated value, according to
The measurement error model of building retains valid data, rejects invalid data.
S6 carries out the quality of data by manual intervention or machine learning algorithm and gos deep into mining analysis, obtains to step (4)
It is same when discontinuity surface data, carry out step S4 according to different weight coefficient and arrive step S5 data fusion, further promotion
Data reliability and accuracy.
Using machine learning algorithms such as mathematics method, neural network, clusterings, with a kind of special data come general
The prediction model of change understands the behavior of system by lot of examples for machine learning algorithm, when machine algorithm and new type number
When according to occurring together, system will generate similar prediction, defeated by input data, mode, machine learning algorithm, deduction
Out, S4 to S5 process data is subjected to analytic learning.
Example
(1) asset management system, dispatch automated system are connected by standard interface agreement.
(2) fault message, equipment operating data, equipment state number are grabbed from dispatch automated system, the asset management system
According to the multi-source heterogeneous data of composition.
(3) data mining rule, extract equipment voltage type table, power plant and substation's information table, facility information table, measuring point remote signalling are established
Information table, remote control operation warning watch, telemetering sample definition table, SOE Event Log Table.
(4) classified according to voltage class (35kV, 20kV, 10kV), device type (transformer, switchgear, route)
Fusion.
(5) it is based on fused data set, equipment, location information, quantity of state, analog quantity, real time data, historical data is established and closes
It is mapping table, according to the interrelated building topological analysis engine between distribution network and network original part.
(6) by topological analysis processing module, data fusion analysis is carried out, extraction meets KCL law data set.
(7) bad data in fused data is detected and is recognized by regularization residual error search method, extract significant figure
According to.
(8) different structure data are mutually verified by mathematics method, neural network, clustering, further
Concentration judgement, positioning and prevention monitoring are carried out to distribution risk status.
In conclusion the multi-source heterogeneous data fusion geo-database integration method of power distribution network operation information of the present invention, to power train
System generates a large amount of multi-source heterogeneous data and is classified, merged after topological analysis, rejecting bad data, and it is more to solve distribution
The extraction of source isomery operation information, the integrated and quality of data are promoted, and realize the friendship to power distribution network historical data and near-realtime data
Pitch composite depth analysis.
In addition, in order to complete above-mentioned data processing task, being needed a set of since power distribution network operation data is magnanimity
Efficient data processing shelf.Fig. 2 is data processing shelf used in the present invention, and frame uses multiple MongoDB instance numbers
Parallel computation is carried out to data according to library.Spark is the caller of MongoDB, by executing the MapReduce built in MongoDB
Mechanism handles basic data, obtains meeting the data into above-mentioned Data Analysis Model.ML (machine learning) library of Spark includes
The algorithms of many machine learning, perfection support above-mentioned analysis demand.Since the processing mode of spark and MongoDB are present
It is very popular, infrastructure effect is also only served in the present invention, so not doing excessive explanation here.
The aforementioned description to specific exemplary embodiment of the invention is in order to illustrate and illustration purpose.These descriptions
It is not wishing to limit the invention to disclosed precise forms, and it will be apparent that according to the above instruction, can much be changed
And variation.The purpose of selecting and describing the exemplary embodiment is that explaining specific principle of the invention and its actually answering
With so that those skilled in the art can be realized and utilize a variety of different exemplary implementation schemes of the invention and
Various chooses and changes.The scope of the present invention is intended to be limited by claims and its equivalents.
Claims (5)
1. a kind of multi-source heterogeneous data fusion geo-database integration method of power distribution network operation information, which is characterized in that specifically include:
S1, the different service sub-systems for accessing power grid according to demand form multi-source data collection to obtain target data set；
S2 classifies to the target data set；
S3 concentrates the sorted target data, opens up according to the interrelated building between distribution network and network original part
Flutter analysis engine；
S4 is based on topological analysis engine, selects the target data to concentrate and meets in same time section, at the same voltage it is consistent,
Electric current and power meet the data set of KCL law, reject ungratified data set；
S5 constructs data fusion model based on regularization residual error search method and rejects through topological analysis treated the number of targets
It is merged later according to the bad data of concentration.
2. the multi-source heterogeneous data fusion geo-database integration method of power distribution network operation information according to claim 1, feature
It is, if finding data exception, return step S4 after step S5 fusion.
3. the multi-source heterogeneous data fusion geo-database integration method of power distribution network operation information according to claim 1, feature
Be, step S2 is specifically included: to the target data set by voltage class, device type acquires measurement type and classifies.
4. the multi-source heterogeneous data fusion geo-database integration method of power distribution network operation information according to claim 3, feature
It is, the voltage class is divided into: 35KV, 20KV, 10KV；The device type is divided by different service sub-systems: transformation
Device, switchgear, route；The acquisition measures type and is divided into: quantity of state and analog quantity, real time data and non-real-time data.
5. the multi-source heterogeneous data fusion geo-database integration method of power distribution network operation information according to claim 1, feature
It is, step S4 is specifically included: specific according to the interrelated building topological analysis engine between distribution network and network original part
Include:
The foundation of topological analysis engine relies on following principle:
1) according to network connection relation, dependence is established to breaker, the switch in network；
2) the complementary information of network element: position of the switch information, the relationship for acquiring measurement；
3) network element is in the dependence of data, including the interim dependence for relying on and fixing under different running method, switch
Dependence, the dependence with historical data of position and acquisition measurement；
4) alarm event of the network element in network operations information, SOE event, the information dependence for protecting system.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110188141A (en) * | 2019-05-15 | 2019-08-30 | 南京邮电大学 | Electric power Internet of Things multi-source data fusion method, readable storage medium storing program for executing and terminal |
| CN110543508A (en) * | 2019-08-30 | 2019-12-06 | 海南电网有限责任公司 | multi-source big data fusion access method for power distribution communication network |
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| CN112818052A (en) * | 2021-02-25 | 2021-05-18 | 云南电网有限责任公司电力科学研究院 | Abnormal voltage data detection method and device |
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| CN115001793A (en) * | 2022-05-27 | 2022-09-02 | 北京双湃智安科技有限公司 | Data fusion method for information security multi-source heterogeneous data |
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102940482A (en) * | 2012-11-22 | 2013-02-27 | 中国科学院自动化研究所 | Adaptive tomographic fluorescence imaging (TFI) reconstructing method |
| CN104393593A (en) * | 2014-11-28 | 2015-03-04 | 国家电网公司 | Method based on three-state data effective fusion |
| US20160314165A1 (en) * | 2013-12-16 | 2016-10-27 | International Business Machines Corporation | System and method of integrating time-aware data from multiple sources |
| CN107945053A (en) * | 2017-12-29 | 2018-04-20 | 广州思泰信息技术有限公司 | A kind of multiple source power distribution network data convergence analysis platform and its control method |
-
2018
- 2018-11-16 CN CN201811369141.5A patent/CN109241169A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102940482A (en) * | 2012-11-22 | 2013-02-27 | 中国科学院自动化研究所 | Adaptive tomographic fluorescence imaging (TFI) reconstructing method |
| US20160314165A1 (en) * | 2013-12-16 | 2016-10-27 | International Business Machines Corporation | System and method of integrating time-aware data from multiple sources |
| CN104393593A (en) * | 2014-11-28 | 2015-03-04 | 国家电网公司 | Method based on three-state data effective fusion |
| CN107945053A (en) * | 2017-12-29 | 2018-04-20 | 广州思泰信息技术有限公司 | A kind of multiple source power distribution network data convergence analysis platform and its control method |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110188141A (en) * | 2019-05-15 | 2019-08-30 | 南京邮电大学 | Electric power Internet of Things multi-source data fusion method, readable storage medium storing program for executing and terminal |
| CN110674235A (en) * | 2019-08-13 | 2020-01-10 | 南方电网科学研究院有限责任公司 | Marketing and distribution topology file synchronous management system and method |
| CN110543508A (en) * | 2019-08-30 | 2019-12-06 | 海南电网有限责任公司 | multi-source big data fusion access method for power distribution communication network |
| CN110687398A (en) * | 2019-10-12 | 2020-01-14 | 广西电网有限责任公司电力科学研究院 | Power distribution network fault intelligent search positioning method based on multi-source data fusion |
| CN111597415B (en) * | 2020-05-13 | 2023-05-26 | 云南电网有限责任公司电力科学研究院 | Neural network-based distribution network account data penetration method and device |
| CN111597415A (en) * | 2020-05-13 | 2020-08-28 | 云南电网有限责任公司电力科学研究院 | Neural network-based power distribution network account data communication method and device |
| CN112270337A (en) * | 2020-09-16 | 2021-01-26 | 广州奇化有限公司 | Heterogeneous information acquisition method and device based on feature mapping |
| CN112818052A (en) * | 2021-02-25 | 2021-05-18 | 云南电网有限责任公司电力科学研究院 | Abnormal voltage data detection method and device |
| CN113610123A (en) * | 2021-07-23 | 2021-11-05 | 上海德衡数据科技有限公司 | Multi-source heterogeneous data fusion method and system based on Internet of things |
| CN113610123B (en) * | 2021-07-23 | 2024-01-23 | 上海德衡数据科技有限公司 | Multi-source heterogeneous data fusion method and system based on Internet of things |
| CN115001793A (en) * | 2022-05-27 | 2022-09-02 | 北京双湃智安科技有限公司 | Data fusion method for information security multi-source heterogeneous data |
| CN115378674A (en) * | 2022-08-11 | 2022-11-22 | 国网湖南综合能源服务有限公司 | Application method and system of shared energy storage cloud platform based on block chain technology |
| CN115378674B (en) * | 2022-08-11 | 2023-11-03 | 国网湖南综合能源服务有限公司 | Application method and system of shared energy storage cloud platform based on blockchain technology |
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Application publication date: 20190118 |