CN109857775A - A kind of mass historical data method for digging of power distribution network Dispatching Control System - Google Patents
A kind of mass historical data method for digging of power distribution network Dispatching Control System Download PDFInfo
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
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Abstract
The present invention relates to a kind of mass historical data method for digging of power distribution network Dispatching Control System, technical characterstic is: the following steps are included: step 1, to historical data carry out sliding-model control；Step 2 carries out data mining to section historical data；Step 3, the frequent item set for excavating distribution data library；Step 4, the Strong association rule for excavating all kinds of historical datas；Step 5 checks whether Strong association rule has strong correlation based on promotion degree algorithm；Step 6 establishes all kinds of Strong association rules in power distribution network historical data；Step 7 judges whether the Strong association rule established is reasonable；Step 8 checks Historic Section database with Strong association rule；Step 9 calculates electrical power distribution automatization system evaluation result；Step 10, data mining are completed, and execution logic is exited.The present invention can reduce influence of the inconsistent data record for distribution postitallation evaluation in historical data base, promote the level of power distribution automation.
Description
Technical field
The invention belongs to power system automatic field technical fields, are related to data prediction, frequent item set mining, association
The technologies such as Rules Filtering, especially a kind of power distribution network Dispatching Control System mass historical data method for digging.
Background technique
Currently, detection work and the control work of actual motion of the domestic each grid company for electrical power distribution automatization system
More lack, even blank, the problem of for being encountered in electrical power distribution automatization system operational process mostly based on qualitative analysis, still
Without the quantitative evaluating method for electrical power distribution automatization system relevant device and operating status, distribution management level rests on lower
It is horizontal.
Continuous improvement with power departments at different levels to the attention degree of power distribution network Dispatching Control System operation control, higher level
Power grid supervision department counts drug in some provinces key area distribution automation main station system historical data, monitors every distribution
Automation index.However, being communicated or other equipment and distribution secondary circuit failure, defect and run unit are artificially modified
Influence, power distribution network historical data accuracy is unable to get guarantee.Existing index and evaluation method are based only on original go through
The progress of history data calculates simple, extensively, can not be provided to higher level's power grid supervision department it is a set of it is scientific, objective, effectively comment
Estimate system.Therefore reliable data information how is filtered out from power distribution network mass data so that electric power authorities carry out science
Control undoubtedly becomes distribution automation system thorny problem urgently to be resolved.
Summary of the invention
It is an object of the invention to overcome the deficiencies in the prior art, propose that a kind of design is reasonable, accurate and reliable and time saving province
The mass historical data method for digging of the power distribution network Dispatching Control System of power.
The present invention solves its realistic problem and adopts the following technical solutions to achieve:
A kind of power distribution network Dispatching Control System mass historical data method for digging, comprising the following steps:
Step 1 is based on distribution section historical data base, reads historical data, and carry out sliding-model control to historical data；
Step 2, based on support-confidence level mode Apriori mining algorithm, data digging is carried out to section historical data
Pick；
Step 3, the frequent item set that distribution data library is excavated according to the support threshold of setting；
Step 4, the Strong association rule that all kinds of historical datas are excavated according to the confidence threshold value of setting；
Step 5, based on promotion degree algorithm check Strong association rule whether have strong correlation, if there is strong correlation, then into
Enter step 6, otherwise rejects the weak Strong association rule of each Historic Section promotion degree；
Step 6, based on the logical consistency between historical data, establish all kinds of strong associations rule in power distribution network historical data
Then；
Step 7 judges whether the Strong association rule established is reasonable, if rationally, entering step 8, otherwise needs to set again
The numerical value for determining support, confidence level and promotion degree, is re-introduced into step 2.
Step 8 checks Historic Section database with Strong association rule, is closed according to distribution network system postitallation evaluation core index
Key input, intelligent decision simultaneously count corresponding power distribution network historical record.
Step 9, in conjunction with electrical power distribution automatization system core evaluation index formula, calculate electrical power distribution automatization system evaluation result.
Step 10, data mining are completed, and execution logic is exited.
Moreover, the specific steps of the step 3 include:
(1) support threshold is set, Apriori algorithm is based on, historical data base is scanned, and calculate each attribute
The number that item occurs；Then according to the support threshold of setting andGet frequent 1 item collection L1；Wherein, X, Y table
Aspect collection, I indicate total transaction set；
(2) by previous frequently (k-1) item collection Lk-1It is attached with itself, judges two frequent item set Lk-1Before (k-
2) whether a item is identical, if identical and only (k-1) a difference, by two frequent item set Lk-1It merges, obtains
Preliminary candidate k item collection C 'k；
(3) based on preliminary candidate k item collection C 'k, the k-1 item collection of wherein non-frequent episode is rejected, final candidate k are obtained
Collect Ck；
(4) Historic Section database is scanned, candidate's k item collection C is calculatedkIn each attribute set support, will be less than
The set entry of support threshold is rejected, and frequent k item collection L is obtainedk；
(5) step (2)-step (4) is repeated, until obtaining maximum frequent itemsets；
(6) all frequent item sets are obtained.
Moreover, the step 4 method particularly includes:
According to the confidence threshold value of setting, by Confidence (X → Y)=P (Y | X)=P (X ∪ Y)/P (X), calculate
The confidence level of transaction item excavates the Strong association rule of all kinds of historical datas；Wherein, X, Y indicate item collection.
Moreover, whether the check Strong association rule of the step 5 has strong correlation method particularly includes:
Check whether Strong association rule has strong correlation based on promotion degree algorithmic formula Lift (X → Y)=P (Y | X)/P (Y)
Property；Wherein, X, Y indicate item collection.
The advantages of the present invention:
1, the present invention is using each based on support-confidence level-promotion degree frame mining algorithm scanning magnanimity distribution network
Historic Section data, noise record that Automatic sieve debugging misses, jamming pattern, it is established that the strong association rule between all kinds of historical records
Then, and according to promotion degree check whether Strong association rule has strong dependence again, thus establish meet it is distribution network automated
Action logic, the power distribution automation network history data correlation rule that has global consistency, and science, accurate is formed accordingly
Power distribution network Dispatching Control System run core index system.And then inconsistent data record pair in historical data base can be reduced
In the influence of distribution postitallation evaluation, the level of power distribution automation is promoted.
2, the present invention is measurement using classics Apriori association algorithm and with degree of being promoted, it is intended to which it is a set of effective to construct
The data mining system for power distribution network feature.From the historical data of power distribution network magnanimity, frequent item set is selected, utilizes intelligence
Algorithm sets up the Strong association rule for meeting distribution network automated action logic, having consistency, according to this rule to power distribution network
Network historical data base is calibrated.For establish objective, reasonable distribution automation system index system provide it is reliable and accurate
Data digging method provides basic data to establish the distribution automation system index system based on statistical significance.
3, a kind of historical data method for digging based on support confidence level promotion degree provided by the invention, can be to original
Database is discarded the dross and selected the essential, and is eliminated the false and retained the true；The noise data due to distribution network systems defect or artificially manufactured record can be intelligently excluded,
It effectively sets up between all kinds of historical datas of distribution network meeting distribution network automated action logic, having global consistency
Strong association rule, comb out between all kinds of automation historical datas of distribution network clearly related law；Strong pass based on foundation
Connection rule can be used for instructing region distribution control centre and work with electricity operation department day-to-day operation, management, maintenance, defect elimination
It carries out, saves human and material resources and time cost；Improve distribution automation operation control metrics evaluation system science with can
It is also each upper management department to matching while day-to-day operation, the management work for being conducive to region distribution control centre by property
The control of electric system provides more scientific tool.It is simultaneously to establish objective, reasonable distribution automation system index body
System provides the theoretical foundation of reliable and accurate database mining method, process and science；Distribution automation system is referred to
Mark system is from simple, extensive horizontal promotion to the level for really having statistical significance, so that electrical power distribution automatization system correlation is set
Standby and operating status can be preferably quantitatively evaluated.
4, the present invention is the improvement carried out under conditions of existing network deployment, and it is convenient to improve, at low cost.
Detailed description of the invention
Fig. 1 is process flow diagram of the invention.
Specific embodiment
The embodiment of the present invention is described in further detail below in conjunction with attached drawing:
A kind of power distribution network Dispatching Control System mass historical data method for digging, as shown in Figure 1, comprising the following steps:
Step 1 is based on distribution section historical data base, reads historical data, and carry out sliding-model control to historical data,
So as to subsequent carry out data mining；
Step 2, based on support-confidence level-promotion degree mode Apriori mining algorithm, to section historical data into
Row data mining；
Step 3, the frequent item set that distribution data library is excavated according to the support threshold of setting；
The specific steps of the step 3 include:
(1) support threshold is set, Apriori algorithm is based on, historical data base is scanned, and calculate each attribute
The number that item occurs；Then according to the support threshold of setting andGet frequent 1 item collection L1；Wherein, X, Y table
Aspect collection, I indicate total transaction set；
(2) by previous frequently (k-1) item collection Lk-1It is attached with itself, judges two frequent item set Lk-1Before (k-
2) whether a item is identical, if identical and only (k-1) a difference, by two frequent item set Lk-1It merges, obtains
Preliminary candidate k item collection C 'k；
(3) based on preliminary candidate k item collection C 'k, the k-1 item collection of wherein non-frequent episode is rejected, final candidate k are obtained
Collect Ck；
(4) Historic Section database is scanned, candidate's k item collection C is calculatedkIn each attribute set support, will be less than
The set entry of support threshold is rejected, and frequent k item collection L is obtainedk；
(5) step (2)-step (4) is repeated, until obtaining maximum frequent itemsets；
(6) all frequent item sets are obtained.
Step 4, the Strong association rule that all kinds of historical datas are excavated according to the confidence threshold value of setting；
The step 4 method particularly includes:
According to the confidence threshold value of setting, by Confidence (X → Y)=P (Y | X)=P (X ∪ Y)/P (X), calculate
The confidence level of transaction item excavates the Strong association rule of all kinds of historical datas.Wherein, X, Y indicate item collection.
Step 5, based on promotion degree algorithm check Strong association rule whether have strong correlation, if there is strong correlation, then into
Enter step 6, otherwise rejects the weak Strong association rule of each Historic Section promotion degree；
Whether the check Strong association rule of the step 5 has strong correlation method particularly includes:
Check whether Strong association rule has strong correlation based on promotion degree algorithmic formula Lift (X → Y)=P (Y | X)/P (Y)
Property；Wherein, X, Y indicate item collection.
Step 6, based on the logical consistency between historical data, establish all kinds of strong associations rule in power distribution network historical data
Then；
Step 7 judges whether the Strong association rule established is reasonable, if rationally, entering step 8, otherwise needs to set again
The numerical value for determining support, confidence level and promotion degree, is re-introduced into step 2.
Step 8 checks Historic Section database with Strong association rule, is closed according to distribution network system postitallation evaluation core index
Key input, intelligent decision simultaneously count corresponding power distribution network historical record.
Step 9, in conjunction with electrical power distribution automatization system core evaluation index formula, calculate electrical power distribution automatization system evaluation result.
Step 10, data mining are completed, and execution logic is exited.
It is emphasized that embodiment of the present invention be it is illustrative, without being restrictive, therefore the present invention includes
It is not limited to embodiment described in specific embodiment, it is all to be obtained according to the technique and scheme of the present invention by those skilled in the art
Other embodiments, also belong to the scope of protection of the invention.
Claims (4)
1. a kind of mass historical data method for digging of power distribution network Dispatching Control System, it is characterised in that: the following steps are included:
Step 1 is based on distribution section historical data base, reads historical data, and carry out sliding-model control to historical data；
Step 2, based on support-confidence level-promotion degree mode Apriori mining algorithm, section historical data is counted
According to excavation；
Step 3, the frequent item set that distribution data library is excavated according to the support threshold of setting；
Step 4, the Strong association rule that all kinds of historical datas are excavated according to the confidence threshold value of setting；
Step 5 checks whether Strong association rule has strong correlation based on promotion degree algorithm, if there is strong correlation, then enters step
Rapid 6, otherwise reject the weak Strong association rule of each Historic Section promotion degree；
Step 6, based on the logical consistency between historical data, establish all kinds of Strong association rules in power distribution network historical data；
Step 7 judges whether the Strong association rule established is reasonable, if rationally, entering step 8, otherwise needs to reset branch
The numerical value of degree of holding, confidence level and promotion degree, is re-introduced into step 2；
Step 8 checks Historic Section database with Strong association rule, defeated according to distribution network system postitallation evaluation core index key
Enter, intelligent decision simultaneously counts corresponding power distribution network historical record；
Step 9, in conjunction with electrical power distribution automatization system core evaluation index formula, calculate electrical power distribution automatization system evaluation result；
Step 10, data mining are completed, and execution logic is exited.
2. a kind of mass historical data method for digging of power distribution network Dispatching Control System according to claim 1, feature
Be: the specific steps of the step 3 include:
(1) support threshold is set, Apriori algorithm is based on, historical data base is scanned, and calculate each attribute item and go out
Existing number；Then according to the support threshold of setting andGet frequent 1 item collection L1；Wherein, X, Y table
Aspect collection, I indicate total transaction set；
(2) by previous frequently (k-1) item collection Lk-1It is attached with itself, judges two frequent item set Lk-1Before (k-2) it is a
It is whether identical, if identical and only (k-1) a difference, by two frequent item set Lk-1It merges, obtains preliminary
Candidate k item collection C 'k；
(3) based on preliminary candidate k item collection C 'k, the k-1 item collection of wherein non-frequent episode is rejected, final candidate k item collection C is obtainedk；
(4) Historic Section database is scanned, candidate's k item collection C is calculatedkIn each attribute set support, will be less than support
The set entry of threshold value is rejected, and frequent k item collection L is obtainedk；
(5) step (2)-step (4) is repeated, until obtaining maximum frequent itemsets；
(6) all frequent item sets are obtained.
3. a kind of mass historical data method for digging of power distribution network Dispatching Control System according to claim 1, feature
It is: the step 4 method particularly includes:
According to the confidence threshold value of setting, by Confidence (X → Y)=P (Y | X)=P (X ∪ Y)/P (X), affairs are calculated
The confidence level of item, excavates the Strong association rule of all kinds of historical datas；Wherein, X, Y indicate item collection.
4. a kind of mass historical data method for digging of power distribution network Dispatching Control System according to claim 1, feature
Be: whether the check Strong association rule of the step 5 has strong correlation method particularly includes:
Check whether Strong association rule has strong correlation based on promotion degree algorithmic formula Lift (X → Y)=P (Y | X)/P (Y)；Its
In, X, Y indicate item collection.
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| CN110222094A (en) * | 2019-06-14 | 2019-09-10 | 国网新疆电力有限公司电力科学研究院 | Based on the electric energy meter risk analysis method and system for improving Apriori algorithm |
| CN110244184A (en) * | 2019-07-04 | 2019-09-17 | 国网江苏省电力有限公司 | A kind of distribution line fault observer method for digging, system and the medium of frequent item set |
| CN110334912A (en) * | 2019-06-10 | 2019-10-15 | 国网浙江省电力有限公司嘉兴供电公司 | A kind of distributed generation resource networking contact efficiency assessment method |
| CN110874413A (en) * | 2019-11-14 | 2020-03-10 | 哈尔滨工业大学 | Association rule mining-based method for establishing efficacy evaluation index system of air defense multi-weapon system |
| CN111160750A (en) * | 2019-12-23 | 2020-05-15 | 东南大学 | Distribution network analysis and investment decision method based on association rule mining |
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| CN111666300A (en) * | 2020-04-16 | 2020-09-15 | 广西电网有限责任公司 | Examination and processing method for relay protection fixed value |
| CN112381306A (en) * | 2020-11-20 | 2021-02-19 | 广西电网有限责任公司防城港供电局 | Intelligent operation and maintenance management and control platform for power distribution network |
| CN112700085A (en) * | 2020-12-11 | 2021-04-23 | 华南理工大学 | Association rule based method, system and medium for optimizing steady-state operation parameters of complex system |
| CN113361939A (en) * | 2021-06-15 | 2021-09-07 | 红云红河烟草(集团)有限责任公司 | Dynamic association method and system for quality and equipment management of wrapping machine type |
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| CN110334912A (en) * | 2019-06-10 | 2019-10-15 | 国网浙江省电力有限公司嘉兴供电公司 | A kind of distributed generation resource networking contact efficiency assessment method |
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| CN111160750A (en) * | 2019-12-23 | 2020-05-15 | 东南大学 | Distribution network analysis and investment decision method based on association rule mining |
| CN111159256A (en) * | 2019-12-31 | 2020-05-15 | 贵州电网有限责任公司 | Distribution network information data mining method facing equipment operation and maintenance |
| CN111666300A (en) * | 2020-04-16 | 2020-09-15 | 广西电网有限责任公司 | Examination and processing method for relay protection fixed value |
| CN112381306A (en) * | 2020-11-20 | 2021-02-19 | 广西电网有限责任公司防城港供电局 | Intelligent operation and maintenance management and control platform for power distribution network |
| CN112700085A (en) * | 2020-12-11 | 2021-04-23 | 华南理工大学 | Association rule based method, system and medium for optimizing steady-state operation parameters of complex system |
| CN113361939A (en) * | 2021-06-15 | 2021-09-07 | 红云红河烟草(集团)有限责任公司 | Dynamic association method and system for quality and equipment management of wrapping machine type |
| CN113361939B (en) * | 2021-06-15 | 2022-05-20 | 红云红河烟草(集团)有限责任公司 | Dynamic association method and system for quality and equipment management of wrapping machine type |
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