CN107092993A - Reclosing success rate association analysis method based on Disasters Type and line information - Google Patents
Reclosing success rate association analysis method based on Disasters Type and line information Download PDFInfo
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- CN107092993A CN107092993A CN201710413224.9A CN201710413224A CN107092993A CN 107092993 A CN107092993 A CN 107092993A CN 201710413224 A CN201710413224 A CN 201710413224A CN 107092993 A CN107092993 A CN 107092993A
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- 238000012097 association analysis method Methods 0.000 title claims abstract description 12
- 238000012098 association analyses Methods 0.000 claims abstract description 11
- 239000000284 extract Substances 0.000 claims abstract description 6
- APTZNLHMIGJTEW-UHFFFAOYSA-N pyraflufen-ethyl Chemical compound C1=C(Cl)C(OCC(=O)OCC)=CC(C=2C(=C(OC(F)F)N(C)N=2)Cl)=C1F APTZNLHMIGJTEW-UHFFFAOYSA-N 0.000 claims description 9
- 241000607479 Yersinia pestis Species 0.000 claims description 6
- 230000006378 damage Effects 0.000 claims description 4
- 230000005540 biological transmission Effects 0.000 abstract description 8
- 230000005611 electricity Effects 0.000 abstract description 8
- 230000002045 lasting effect Effects 0.000 abstract description 2
- 238000005457 optimization Methods 0.000 abstract description 2
- 238000000034 method Methods 0.000 abstract 1
- 230000001052 transient effect Effects 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000009412 basement excavation Methods 0.000 description 1
- 239000012141 concentrate Substances 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
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- 238000012544 monitoring process Methods 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—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
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02H—EMERGENCY PROTECTIVE CIRCUIT ARRANGEMENTS
- H02H3/00—Emergency protective circuit arrangements for automatic disconnection directly responsive to an undesired change from normal electric working condition with or without subsequent reconnection ; integrated protection
- H02H3/02—Details
- H02H3/06—Details with automatic reconnection
- H02H3/066—Reconnection being a consequence of eliminating the fault which caused disconnection
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Abstract
The invention discloses a kind of reclosing success rate association analysis method based on Disasters Type and line information, including：S1, acquisition line tripping information, extract Disasters Type and line parameter circuit value；S2, combined circuit reclosing success situation, complete the input of original association analysis data set；S3, setting support threshold, frequent item set is searched for using Apriori algorithm；S4, to overlap situation as consequent, filter out meet require Strong association rule.This method can extract the Strong association rule related to reclosing situation in existing line tripping data basis, available for the optimization of transmission line of electricity reclosing strategy, further lifting power network safety operation level and lasting reliable power supply ability.
Description
Technical field
Prevented and reduced natural disasters field, more particularly to a kind of reclosing based on Disasters Type and line information the invention belongs to power network
Success rate association analysis method.
Background technology
The transmission line of electricity of outdoor operation is influenceed tight by disasters such as filth, icing, thunderbolt, windage yaw, bird pest, external force destructions
Weight, it is easy to trigger tripping fault.Jumped according to 330kV under being had jurisdiction over to State Grid Corporation of China in 2015 and above transmission line of electricity
The statistical result of lock data understand, cause line fault trip major casualty type for thunderbolt, ice trouble, bird pest, windburn and outside
Power is destroyed.Wherein, thunderbolt is the first cause for causing transmission line of electricity to trip, and accounts for the 40.68% of the tripping operation total number of records；Ice trouble,
The probability that bird pest, external force destruction cause line fault to trip is approached, and about 16%；Windburn accounts for 9.64%.
Transmission line of electricity is often transient fault because of the trip accident that thunderbolt, strong wind, birds electric discharge trigger, and accounts for power system
Institute faulty 60~90%, can be by reclosing device fast recovery of power supply.Reclosing circuit when improving transient fault is supplied
The continuity of electricity, serve significant role in terms of the stability of system operation, but current reclosing technology can't be distinguished
Transient fault and long-time failure.Blindly reclosing may be such that circuit coincides with long-time failure, to power system
Enormous impact will be caused, triggers more serious power-off event.
The content of the invention
The present invention is intended to provide a kind of reclosing success rate association analysis method based on Disasters Type and line information, leads to
The Strong association rule related to reclosing success rate excavated is crossed, optimizes circuit re-switching strategy, so as to improve the weight of power network
Closing success rate.
The technical solution adopted for the present invention to solve the technical problems is：
A kind of reclosing success rate association analysis method based on Disasters Type and line information of present invention offer, including with
Lower step：
S1, acquisition line tripping information, extract Disasters Type and line parameter circuit value；
S2, combined circuit reclosing success situation, complete the input of original association analysis data set；
S3, setting support threshold, frequent item set is searched for using Apriori algorithm；
S4, to overlap situation as consequent, filter out meet require Strong association rule.
Further, the Disasters Type in step S1 of the invention is thunderbolt, ice trouble, bird pest, windburn and external force destruction, line
Road parameter is scoping unit, voltage type, voltage class and the line name of circuit.
Further, the attribute of original analysis data collection is " scoping unit-voltage type-electricity in step S2 of the invention
Press grade-line name-Disasters Type-coincidence situation ".
Further, the support set in step S3 of the invention before using Apriori algorithm search frequent item set
Threshold value is 0.02.
Further, the condition for being used to screen Strong association rule in step S4 of the invention is that confidence level is not less than 0.6, carried
Liter degree is more than 1.
The present invention utilizes the existing tripping operation information of power network, extracts the common Disasters Type and Yi Fa for triggering electric network fault tripping operation
The line characteristics parameter of raw fault trip, and the two strong association between reclosing success rate is excavated using Apriori algorithm
Rule, the transmission line of electricity reclosing policy optimization that may be based on power network tripping fault anticipation provides foundation, further lifts power network
Safe and stable operation level and lasting reliable power supply ability.
Brief description of the drawings
Fig. 1 is the flow chart of the reclosing success rate association analysis method of the invention based on Disasters Type and line information；
Fig. 2 is the use Apriori algorithm search frequent item set flow chart of the embodiment of the present invention.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, it is right below in conjunction with drawings and Examples
The present invention is further elaborated.It should be appreciated that specific embodiment described herein is only to explain the present invention, not
For limiting the present invention.
As shown in figure 1, in one particular embodiment of the present invention, Disasters Type and circuit letter are extracted in Tripping data
Breath, and the Strong association rule excavation and analysis with trip condition are completed using Apriori algorithm, specifically include following steps：
S1, the EMS tripping operation information for obtaining from line arrangement department circuit automatically, are combed by thunderbolt, ice trouble, bird pest, windburn
The trip accident triggered is destroyed with external force, and extracts scoping unit, voltage type, voltage class and the line of circuit in tripping operation record
Road title；
S2, combined circuit reclosing success situation (including two kinds of successful reclosing and unsuccessful reclosing), according to " administration is single
The form of position-voltage type-voltage class-line name-Disasters Type-coincidence situation " completes original association analysis data set
Input；
S3, setting support threshold=0.02, are scanned, reservation meets support threshold to 1 item collection that data are concentrated
Frequent 1 item collection, and new data set is constituted by frequent 1 item collection, then complete the search of frequent 2 item collection.So repeat, using by
Frequent k+1 item collections are explored in the data set that is constituted from frequent k item collections of alternative manner of layer search, until in the absence of meeting support
Untill the frequent item set that threshold value=0.2 is required, specific flow is as shown in Figure 2；
Wherein, the support of a certain k item collections represents the frequent journey that the k item collections occur in original association analysis data set
The number of times that degree, i.e. the k item collections occur and the ratio of the original association analysis data set total number of records.S4, using overlap situation as rule
Consequent, filters out the Strong association rule that confidence level is more than 1 not less than 0.6, lifting degree；
Wherein, for correlation rule X=>Y, X are that attribute is { scoping unit, voltage class in original association analysis data set
Type, voltage class, line name, Disasters Type } in one or more specific valued combinations, such as { 500kV, thunderbolt } or { Shan
West, exchange, 500kV, windburn } etc., Y is coincidence situation corresponding with specific X values in original association analysis data set, is existed
{ successful reclosing } and { unsuccessful reclosing } two kinds of situations, then the definition of confidence level and lifting degree and calculation formula are as follows：
Confidence level represents that original association analysis data concentrate on and occur occurring Y possibility on the premise of X again, i.e., comprising X
Item collection in include Y probability again.This parameter is the measurement to correlation rule credibility, and numerical value is higher, and Y occurs when X occurs
Possibility it is bigger, i.e., release that Y is more reliable by X, for being likely to occur for Y effective reply can be taken to arrange in advance in this case
Apply.It is denoted as confidence (X=>Y), then have
Confidence (X=>Y)=P (Y/X)=P (XY)/P (X)
Lifting degree represents that the appearance of the X in original association analysis data set occurs the lifting degree of possibility to Y, as exists
Occur Y probability occur simultaneously under X precondition and the ratio between Y probability individually occur.This parameter is used to judge correlation rule
Validity, compensate for support, confidence level parameter can not ensure that X and Y are not separate defects.It is denoted as lift (X=>
Y), then have
Lift (X=>Y)=P (Y/X)/P (Y)=P (XY)/[P (X) P (Y)]
The lower 330kV of State Grid Corporation of China's linchpin in 2015 and above transmission line of electricity tripping operation number are extracted according to step S1
According to for sample, the arrangement of Tripping data is carried out according to data format specified in step S2, according to what is provided in step S3
Apriori algorithm finds out frequent item set (such as Fig. 2), and calculates according to the definition of support the support of each frequent item set
Degree, filter out the frequent item set for meeting support threshold requirement, finally in the frequent item set for meeting support threshold requirement with
Coincidence situation be consequent, using attribute as { scoping unit, voltage type, voltage class, line name, Disasters Type } in one
Plant or a variety of specific valued combinations are regular former piece, constitute shape such as X=>Y different correlation rules, according to correlation rule confidence
The calculation formula of degree and lifting degree calculates the confidence level and lifting degree of each correlation rule, is screened according to the requirement in step S4
Go out Strong association rule.The Strong association rule form excavated according to above-mentioned steps is：
According to the Strong association rule of above-mentioned acquisition, reclosing strategy is adjusted as follows：For can determine whether in advance as thunder
Trip event, generally transient fault caused by sudden and violent or birds, according to the direct reclosing of original protection setting；For judging
The trip event caused by high wind or ice and snow, is often long-time failure, can produce block signal and not start auto recloser；
For can not failure judgement type trip event, continue to use original protection setting and carry out reclosing.
After being adjusted, 2015 Nian Guowang companies are super, the reclosing success rate of UHV transmission line disaster failure is reachable
81.63%, compared to improving 18.16% before.And by thunderbolt, windburn and ice trouble cause the ratio that line fault trips compared with
Greatly, this ratio in 2015 is 67.48%, shows that the reclosing strategy validity proposed is more obvious.
Power network monitoring equipment of the adjustment of above-mentioned reclosing strategy without extra increase correlation, also not by operation of power networks shape
The influence of state, with very strong practicality and operability.
Claims (5)
1. a kind of reclosing success rate association analysis method based on Disasters Type and line information, it is characterised in that including with
Lower step：
S1, acquisition line tripping information, extract Disasters Type and line parameter circuit value；
S2, combined circuit reclosing success situation, complete the input of original association analysis data set；
S3, setting support threshold, frequent item set is searched for using Apriori algorithm；
S4, to overlap situation as consequent, filter out meet require Strong association rule.
2. the reclosing success rate association analysis method according to claim 1 based on Disasters Type and line information, its
It is characterised by, the Disasters Type in step S1 is thunderbolt, ice trouble, bird pest, windburn and external force destruction, line parameter circuit value is the pipe of circuit
Have jurisdiction over unit, voltage type, voltage class and line name.
3. the reclosing success rate association analysis method according to claim 1 based on Disasters Type and line information, its
It is characterised by, the attribute of original association analysis data set is " scoping unit-voltage type-voltage class-circuit name in step S2
Title-Disasters Type-coincidence situation ".
4. the reclosing success rate association analysis method according to claim 1 based on Disasters Type and line information, its
It is characterised by, support threshold is 0.02 in step S3.
5. the reclosing success rate association analysis method according to claim 1 based on Disasters Type and line information, its
It is characterised by, the condition for being used to screen Strong association rule in step S4 is that confidence level is more than 1 not less than 0.6, lifting degree.
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| CN201710413224.9A CN107092993A (en) | 2017-06-05 | 2017-06-05 | Reclosing success rate association analysis method based on Disasters Type and line information |
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| CN201710413224.9A CN107092993A (en) | 2017-06-05 | 2017-06-05 | Reclosing success rate association analysis method based on Disasters Type and line information |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109656969A (en) * | 2018-11-16 | 2019-04-19 | 北京奇虎科技有限公司 | Data unusual fluctuation analysis method and device |
| CN109710595A (en) * | 2018-11-30 | 2019-05-03 | 广东工业大学 | The construction method of transmission of electricity corridor bird pest hotspot graph based on limited information |
| CN110390125A (en) * | 2019-05-15 | 2019-10-29 | 南京金蓝智慧城市规划设计有限公司 | Bituminous pavement failure analysis method based on correlation model |
| CN113515560A (en) * | 2021-07-19 | 2021-10-19 | 彩虹无线（北京）新技术有限公司 | Vehicle fault analysis method and device, electronic equipment and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103678943A (en) * | 2013-12-31 | 2014-03-26 | 国家电网公司 | Multi-index fuzzy evaluation method for grid faults caused by disasters |
| CN103871003A (en) * | 2014-03-31 | 2014-06-18 | 国家电网公司 | Power distribution network fault diagnosis method utilizing historical fault data |
| CN105488308B (en) * | 2016-01-20 | 2019-05-24 | 国家电网公司 | A kind of disaster influences the multi-scale generalization analysis method of power grid |
-
2017
- 2017-06-05 CN CN201710413224.9A patent/CN107092993A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103678943A (en) * | 2013-12-31 | 2014-03-26 | 国家电网公司 | Multi-index fuzzy evaluation method for grid faults caused by disasters |
| CN103871003A (en) * | 2014-03-31 | 2014-06-18 | 国家电网公司 | Power distribution network fault diagnosis method utilizing historical fault data |
| CN105488308B (en) * | 2016-01-20 | 2019-05-24 | 国家电网公司 | A kind of disaster influences the multi-scale generalization analysis method of power grid |
Non-Patent Citations (1)
| Title |
|---|
| 卢恩泽等: "基于灾害类型与线路信息的电网重合闸成功率关联分析", 《高压电器》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109656969A (en) * | 2018-11-16 | 2019-04-19 | 北京奇虎科技有限公司 | Data unusual fluctuation analysis method and device |
| CN109710595A (en) * | 2018-11-30 | 2019-05-03 | 广东工业大学 | The construction method of transmission of electricity corridor bird pest hotspot graph based on limited information |
| CN109710595B (en) * | 2018-11-30 | 2022-03-25 | 广东工业大学 | Method for constructing bird damage hotspot diagram of power transmission corridor based on limited information |
| CN110390125A (en) * | 2019-05-15 | 2019-10-29 | 南京金蓝智慧城市规划设计有限公司 | Bituminous pavement failure analysis method based on correlation model |
| CN113515560A (en) * | 2021-07-19 | 2021-10-19 | 彩虹无线（北京）新技术有限公司 | Vehicle fault analysis method and device, electronic equipment and storage medium |
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Application publication date: 20170825 |
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