CN107784393A - A kind of the defects of transmission line of electricity Forecasting Methodology and device - Google Patents
A kind of the defects of transmission line of electricity Forecasting Methodology and device Download PDFInfo
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- CN107784393A CN107784393A CN201711027293.2A CN201711027293A CN107784393A CN 107784393 A CN107784393 A CN 107784393A CN 201711027293 A CN201711027293 A CN 201711027293A CN 107784393 A CN107784393 A CN 107784393A
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- 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
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- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- 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
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
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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
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- 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
Abstract
The invention belongs to the security fields of transmission line of electricity.The invention provides Forecasting Methodology the defects of a kind of transmission line of electricity and device.Methods described includes：Obtain Production MIS data, power transmission and transformation equipment state monitoring system and the weather information data of transmission line of electricity, wherein, Production MIS packet parameter containing transmission line structure, transmission line of electricity defect historical data, line channel mima type microrelief data, power transmission and transformation equipment state monitoring system includes microclimate online monitoring data, microclimate monitoring historical data, icing online monitoring data, icing monitoring historical data, and weather information data includes weather forecast data, meteorological historical data, meteorological live data；According to Production MIS data, power transmission and transformation equipment state monitoring system and weather information data, using machine learning algorithm, the specified defect information of forecasting of transmission line of electricity is generated.The present invention can be predicted effectively the defects of may being occurred by meteorological effect on transmission line of electricity, to realize Electrical Safety.
Description
Technical field
The present invention relates to the security technology area of transmission line of electricity, more particularly to the defects of a kind of transmission line of electricity Forecasting Methodology and
Device.
Background technology
In recent years, extreme disasters weather occurrence frequency is increased, and meteorological disaster causes damage to people's production and living to be had year by year
The trend of increase.And as the power industry of the necessary support such as the development of the national economy, people's production and living, due to meteorological disaster and
The influence of its secondary disaster, cause the serious electric power accident such as shaft tower damage, circuit breaking to happen occasionally, pole is caused to Electrical Safety
It is big to influence.
Therefore, transmission line of electricity defect is effectively predicted, is the important means for avoiding electric power accident from occurring.
It should be noted that the introduction to technical background above be intended merely to it is convenient technical scheme is carried out it is clear,
Complete explanation, and facilitate the understanding of those skilled in the art and illustrate.Can not merely because these schemes the present invention
Background section is set forth and thinks that above-mentioned technical proposal is known to those skilled in the art.
The content of the invention
The defects of a kind of transmission line of electricity is provided Forecasting Methodology of the invention and device, to realize what transmission line of electricity may occur
Defect is effectively predicted.
In order to achieve the above object, Forecasting Methodology the defects of a kind of transmission line of electricity of offer of the embodiment of the present invention, including：
Obtain Production MIS data, power transmission and transformation equipment state monitoring system and the weather information of transmission line of electricity
Data, wherein, Production MIS packet parameter containing transmission line structure, transmission line of electricity defect historical data,
Line channel mima type microrelief data, the power transmission and transformation equipment state monitoring system includes microclimate online monitoring data, microclimate is supervised
Historical data, icing online monitoring data, icing monitoring historical data are surveyed, the weather information data includes weather forecast number
According to, meteorological historical data, meteorological live data；
According to Production MIS data, power transmission and transformation equipment state monitoring system and weather information data, utilize
Machine learning parser, generate the specified defect information of forecasting of the transmission line of electricity.
In order to achieve the above object, the embodiment of the present invention also provide a kind of the defects of transmission line of electricity prediction meanss, including：
Acquisition module, for obtaining Production MIS data, the power transmission and transformation equipment state monitoring system of transmission line of electricity
System and weather information data, wherein, Production MIS packet parameter containing transmission line structure, transmission line of electricity
Defect historical data, line channel mima type microrelief data, the power transmission and transformation equipment state monitoring system are monitored on-line including microclimate
Data, microclimate monitoring historical data, icing online monitoring data, icing monitoring historical data, the weather information data bag
Containing meteorological forecast data, meteorological historical data, meteorological live data；
Analysis module, for according to Production MIS data, power transmission and transformation equipment state monitoring system and meteorology
Information data, using machine learning parser, generate the specified defect information of forecasting of the transmission line of electricity.
The defects of transmission line of electricity provided in an embodiment of the present invention Forecasting Methodology and device, can using big data analytical technology pair
A variety of effective informations that transmission line of electricity can be influenceed carry out mining analysis, and can effectively predict may by meteorological effect on transmission line of electricity
The defects of generation, to realize Electrical Safety.
With reference to following explanation and accompanying drawing, only certain exemplary embodiments of this invention is disclose in detail, specifies the original of the present invention
Reason can be in a manner of adopted.It should be understood that embodiments of the present invention are not so limited in scope.In appended power
In the range of the spirit and terms that profit requires, embodiments of the present invention include many changes, modifications and are equal.
The feature for describing and/or showing for a kind of embodiment can be in a manner of same or similar one or more
Used in individual other embodiment, it is combined with the feature in other embodiment, or substitute the feature in other embodiment.
It should be emphasized that term "comprises/comprising" refers to the presence of feature, one integral piece, step or component when being used herein, but simultaneously
It is not excluded for the presence or additional of one or more further features, one integral piece, step or component.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
There is the required accompanying drawing used in technology description to be briefly described, it should be apparent that, drawings in the following description are only this
Some embodiments of invention, for those skilled in the art, without having to pay creative labor, can be with root
Other accompanying drawings are obtained according to these accompanying drawings.
The realization principle figure of the defects of Fig. 1 is the transmission line of electricity of present invention Forecasting Methodology；
The process chart of the defects of Fig. 2 is the transmission line of electricity of embodiment of the present invention Forecasting Methodology；
Fig. 3 is the specific implementation flow chart of the step S102 in embodiment illustrated in fig. 2；
Fig. 4 is the schematic flow sheet that Apriori algorithm finds frequent mode；
The structural representation of the defects of Fig. 5 is the transmission line of electricity of embodiment of the present invention prediction meanss；
Fig. 6 is the structural representation of the analysis module in embodiment illustrated in fig. 5.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation describes, it is clear that described embodiment is only part of the embodiment of the present invention, rather than whole embodiments.It is based on
Embodiment in the present invention, those of ordinary skill in the art are obtained every other under the premise of creative work is not made
Embodiment, belong to the scope of protection of the invention.
Art technology technical staff knows, embodiments of the present invention can be implemented as a kind of system, device, equipment,
Method or computer program product.Therefore, the disclosure can be implemented as following form, i.e.,：It is complete hardware, complete soft
Part (including firmware, resident software, microcode etc.), or the form that hardware and software combines.
Below with reference to the principle and spirit of some representative embodiments of the present invention, in detail the explaination present invention.
It has been born in the session of artificial intelligence international conference the 11st held in by the end of August, 1989 in the U.S. data mining
Concept.Nineteen ninety-five, in american computer annual meeting, the implication of data mining is specify that, i.e., extract potential rule by data and have
The process of knowledge information.Into after 21 century, due to the acceleration of IT application process, data volume is in explosive growth, magnanimity
The appearance of data, the concept of " big data " is allowed to arise at the historic moment.Often imply in intelligent electrical network mass data various useful
Information, rely solely on traditional data base querying search mechanism and statistical method be difficult to obtain these information, there is an urgent need to
Pending data automatically, intelligently can be converted into valuable information, so as to be reached for the purpose of decision service, i.e.,
" big data " technology and application.Big data analytical technology, which can be excavated adequately and reasonably, may influence a variety of effective of transmission line of electricity
Information carries out mining analysis, and new idea and method is provided for being widely popularized for intelligent grid.
The realization principle of the present invention is as shown in figure 1, by collecting Production MIS data (including transmission line of electricity knot
Structure parameter, transmission line of electricity defect historical data), meteorological data (including weather forecast data, meteorological historical data), utilize machine
Algorithm (including cluster analysis and association analysis) is analysed in depth in study, the defects of a kind of transmission line of electricity based on machine learning of structure
Forecast model, realize some specified defects for finding transmission line of electricity in advance.
The process chart of the defects of Fig. 2 is the transmission line of electricity of embodiment of the present invention Forecasting Methodology.As shown in Fig. 2 including：
Step S101, obtain Production MIS data, the power transmission and transformation equipment state monitoring system of transmission line of electricity with
And weather information data, wherein, Production MIS packet parameter containing transmission line structure, transmission line of electricity defect
Historical data, line channel mima type microrelief data, the power transmission and transformation equipment state monitoring system include microclimate online monitoring data,
Microclimate monitoring historical data, icing online monitoring data, icing monitoring historical data, the weather information data include meteorology
Forecast data, meteorological historical data, meteorological live data；
Step S102, according to Production MIS data, power transmission and transformation equipment state monitoring system and weather information
Data, using machine learning parser, generate the specified defect information of forecasting of the transmission line of electricity.
When it is implemented, in step S101, the transmission line structure parameter of acquisition includes：Line account, voltage class,
Circuit division number, wire type, shaft tower property, tower, shaft tower longitude and latitude, span, exhale height, shaft tower material, fixed form, shaft tower
High, these parameters can be obtained directly from the Production MIS of transmission line of electricity.
Transmission line of electricity defect historical data can also directly obtain from Production MIS, and its data mode can be with
It is form, as shown in table 1.These defect historical datas can include enrollment time, defect content, technical reason and duty
Appoint reason etc..Wherein, liability cause is meteorologic factor, including temperature, heavy rain, thunderbolt, strong wind, heavy snow, frost etc.；Defect content
Which kind of defect information that some specific part including transmission line of electricity occurs；Technical reason be defect specific manifestation form, example
Such as self-destruction, crackle, cracking, deformation, bending, damage, displacement.
Table 1
When it is implemented, in step s 102, the machine learning parser includes cluster analysis and association analysis.
Also, according to Production MIS data, power transmission and transformation equipment state monitoring system and weather information data,
Using machine learning parser, the specified defect information of forecasting of the transmission line of electricity is generated, implements step such as Fig. 3 institutes
Show, including：
Step S1021, gone through according to the transmission line of electricity defect historical data and the meteorological historical data, microclimate monitoring
History data, icing monitoring historical data, are associated analysis, obtain the first specified defect information, the first specified defect letter
Breath include in the transmission line of electricity defect historical data by meteorology (heavy rain, thunderstorm, strong wind, heavy snow etc.), microclimate (temperature,
Maximum wind velocity, wind direction etc.) and the transmission line of electricity defect that directly or indirectly triggers of icing.
Step S1022, according to the first specified defect information and transmission line structure parameter, line channel mima type microrelief number
According to, carry out cluster analysis, obtain the second specified defect information, the second specified defect information includes first specified defect
In information by transmission line structure parameter (such as insulator chain model, type, wire type, stockbridge damper position and model etc.)
The transmission line of electricity defect directly or indirectly triggered with line channel mima type microrelief data.
Step S1023, history number is monitored according to the second specified defect information and the meteorological historical data, microclimate
According to, icing monitoring historical data, analysis is associated, calculates and obtains specific meteorological historical information, the specific meteorological history is believed
The defects of breath includes causing to occur by the meteorology of a period of time in the second specified defect information.It is meteorological when historic defects occur
Disaster is the meteorology (temperature, heavy rain, thunderstorm, strong wind, heavy snow etc.) of a period of time, microclimate (temperature, most as main cause
Big wind speed, wind direction etc.) and icing the defects of causing.The reason for causing specified defect (rule)：Meteorological data in a period of time, i.e.,
When meteorological data in a period of time occurs, it is possible to the defects of similar specific can occur.
Step S1024, according to the specific meteorological historical information and the weather forecast data, meteorological live data, micro-
Meteorological online monitoring data, icing online monitoring data, cluster analysis is carried out, calculate and obtain specific Weather Forecast Information, it is described
Specific Weather Forecast Information is included when certain specific meteorological, microclimate and icing occurs, it is possible to the defects of occurring.It that is to say
Say, when weather forecast occurs similar meteorological, it is possible to defect occurs.
Step S1025, according to the second specified defect information and the specific Weather Forecast Information, it is associated point
Analysis, generation can be predicted the specified defect of the specific line unit of transmission line of electricity, the specific line unit of transmission line of electricity it is specific
Defect, which includes working as, occurs certain specific meteorological, microclimate and icing constantly, it is possible to defect occurs, it would be possible to the defects of occurring and institute
State transmission line structure parameter related in transmission line of electricity defect historical data, line channel mima type microrelief data are associated, generation
Specific overhead line structures it is possible that specified defect.
In the embodiment of the present invention, cluster analysis uses k-Means clustering methods more ripe at present.Come for the present invention
Say, carry out hierarchical clustering firstly the need of by initial data using cluster analysis, all defect can be divided into 4-8 classes by initial analysis,
It is 4,5,6,7,8 to choose k respectively, and by calculating overall profile coefficient, carries out the comparison of Clustering Effect.Obtained by experience
Know, as k=6, i.e., cluster result is ideal when defect mode being divided into 6 class.
Also, physical background and expertise with reference to the defects of transmission line of electricity, six common classes can be gone out with summary and induction and are lacked
The pattern of falling into, i.e. ice trouble, thunderbolt, windburn, pollution flashover, external force is destroyed and bird pest, and case data F1, F2 the defects of obtain, F3,
F4, F5...... are represented.
Defect case data is clustered using k-Means, its result see the table below shown in 2,6 class defects, 21 defects
Case data.
Table 2
| Sequence number | Defect title | Defect case |
| Defect 1 | Ice trouble | F4,F6,F11,F12,F19 |
| Defect 2 | Thunderbolt | F3,F10,F13,F22 |
| Defect 3 | Windburn | F7,F9,F15,F16,F18 |
| Defect 4 | Pollution flashover | F2,F20 |
| Defect 5 | External force is destroyed | F1,F5,F8,F17 |
| Defect 6 | Bird pest | F14,F21 |
In order to carry out the diagnosis of defect mode, the dependency relation between each quantity of state and each defect mode is further considered,
When i.e. certain quantity of state occurs abnormal, the possibility of the situation of certain defect mode occurs.Wherein, quantity of state includes specific weather information
Data and overhead line structures structural parameters etc..
After coefficient correlation of each state parameter with each defect mode of equipment is tried to achieve, examining for defect mode diagnosis can be obtained
Disconnected matrix R, it is as follows：
Diagnostic matrix R is defined as i-th kind of defect mode BDiIn j-th of state parameter VjUnder coefficient correlation be Rij.Its
In, i ∈ [1, m], common m kinds defect mode；J ∈ [1, n], common n kinds state parameter.
Calculating coefficient RijWhen, calculated herein using Pearson came relative coefficient.Coefficient correlation is with two
Variable by product moment method with based on the deviation of the average value of each independent variable, being calculated, being multiplied by two deviations, use it
Accumulate to reflect degree of correlation between two variables.The span of Pearson correlation coefficient is -1 to 1.When coefficient correlation is 1, meaning
Taste two linear variable displacement correlations, is that all data points all fall in straight line in the functional arrangement on two variables
On, and the value of one of variable increases and increased with the value of another variable.When the value of coefficient correlation is -1, this is still meaned
All data points all to fall on straight line, the value of one of variable increases and reduced with the value of another variable.If two changes
It is 0 that amount, which tries to achieve coefficient correlation, then shows do not have significant linear relationship between the two variables.
After diagnostic matrix R is tried to achieve by the above method, defect mode can be diagnosed by following formula：
F=RU；
Wherein,Case data is fallen into for follow-up breakthrough, includes the state of each state parameter
Deterioration is horizontal；For defect mode diagnostic result vector, each element in vector
Value can characterize the subjection degree of the defect case under each defect mode., can when finally making a definite diagnosis most probable defect mode
The defects of selecting subjection degree maximum (i.e. numerical value is maximum) pattern, as final result.
By cluster analysis, six kinds of defect modes of transmission line of electricity, i.e. defect mode number m=6 have been excavated；Key parameters
15 altogether, state parameter number n=15.
In the embodiment of the present invention, the available association rules mining algorithm of association analysis has Apriori algorithm, FP- to increase calculation
Method, Eclat algorithms etc..Wherein Apriori algorithm is the association rules mining algorithm proposed earliest, and current ten big data is dug
One of algorithm is dug, the algorithm of other association rule minings is all that Apriori operational efficiency is directed on the basis of Apriori algorithm
The problems such as not high, is improved what is drawn, and many data mining algorithms have all borrowed Apriori thought at present.
Apriori algorithm is that R.Agrawal and R.Strikant propose to be used to excavate mass market transaction data in 1994
The initiative algorithm of boolean association rule in storehouse.The algorithm is a kind of successively search (BFS) algorithm, be make use of
" if a predicate collection right and wrong are frequently, its any superset is also non-frequent to the antimonotone characteristic that predicate collection is closed downwards
".
For correlation rule, general type X=>Y implication, it can be understood as " if X, Y ".
If the storehouse to be excavated of correlation rule is D, it is affairs T intersection, if there is n affairs, D={ T1,T2,…,Tn,
For each affairs, then it is made up of m item, T={ I1,I2,…,Im}。
For item collection X, the definition of support (Support) is：
And for X=>Y correlation rule, its support are：
The support of description reflects two item collections of X, Y while the probability occurred.The support of the support and Frequent Set
It is equal.In formula, Sup represents support, and co represents collective number.Similarly, for X=>Y correlation rule, its confidence level
(Confidence) it is：
The situation of the confidence level reflection of description is, if including X in item collection, to include Y probability simultaneously.For using pass
For the user for joining rule, user can go to excavate support and confidence level by defining the threshold value of minimum support and confidence level
Simultaneously higher correlation rule.In formula, Con represents confidence level, and Sup represents support.
Most important also most basic problem is to find all frequent modes in association rule mining, be following present
Apriori algorithm finds the flow of frequent mode, and wherein size is that the set of k frequent predicate set is designated as Fk, its Candidate Set collection
Conjunction is designated as Ck, FkAnd CkAll include a support attribute field.
1) database is scanned first, is calculated the support of each predicate and is determined frequently predicate, so as to obtain frequency
The set F of numerous 1- predicates collection1；
2) thereafter each time before ergodic data storehouse, frequent (k-1)-predicate for utilizing a preceding ergodic data storehouse to obtain
The set F of collectionk-1For seed set, new, potential frequently k- predicate collection, i.e. candidate k- are generated using predicate collection generating function
Predicate collection, form set Ck；
3) C is determined at ergodic data storehousekIn each candidate's predicate collection support, at the end of each scan database
Those predicate collection for meeting minimum support condition are obtained, that is, determine frequent k- predicates collection, and then them is turned into next time time
The seed gone through；Repeat 2) with 3) process until new frequent predicate set can not be found.
During Apriori algorithm finds frequent mode, most crucial step is exactly connection and secateurs.Except the first step
Hereafter the simple probability for calculating predicate appearance each obtains frequent k- predicates collection set to determine beyond frequent 1- predicates collection
Step all includes：
(1) to the set F of acquired frequently (k-1)-predicate collectionk-1Generation candidate's k- predicates are attached with its own
The set C of collectionk(k≥2)。
(2) ergodic data storehouse calculates CkIn each candidate's predicate collection support and secateurs is carried out by minimum support,
Obtain the set F of frequent k- predicates collectionk。
Generation for each candidate's k- predicate collection, the step of similarly including connection and secateurs：
(1) connect：Apriori assumes that the predicate of affairs and predicate concentration is arranged according to lexicographic order, for appointing
Anticipate two frequent (k-1)-predicate collection f1And f2For, if the preceding k-2 predicate of the two predicate collection be all identical and kth -1
Predicate is different, then f1And f2It is attachable.By connecting f1And f2A k- predicate collection c is generated, the k- predicates collection includes f1
And f2In all predicates, and arranged according to lexicographic order, the k- predicate collection c are put into set C as candidate's k- predicate collectionkIn.
(2) secateurs：For set CkIn each k- predicates collection c, if the k- predicate collection, which includes, is not present in Fk-1In
(k-1)-subset, then by c from candidate collection CkMiddle deletion.
Fig. 4 has demonstrated the overall process that frequent predicate set searching is carried out using Apriori algorithm with an example, wherein minimum
Support is 2 (support is expressed in a manner of counting).
Being found out in database D can further generate what is included in the database on the basis of all frequent predicate sets
Correlation rule.According to min confidence condition, association rule of those confidence levels not less than the min confidence that user specifies are found out
Then, the task of association rule mining is so far completed.Next, it can be carried out according to resulting correlation rule and specific industry
Concrete analysis, therefrom obtains implicit association knowledge.
In Association Rule Analysis, the determination to confidence level and support is vital, only rational confidence level
The correlation rule of relative value could be preferably excavated with support threshold.Because quantity of state species is various, therefore support
Threshold value should not set excessive, be set as 0.1 in this support, and in order to obtain the correlation rule of higher confidence level,
Confidence is set as 0.8.Thus, it is possible to obtain multiple state parameters and transmission line of electricity difference defect type relevance is most strong
Correlative factor.
In embodiments of the present invention, cluster analysis and association analysis are all to belong to machine learning to analyse in depth algorithm, although
In the embodiment shown in fig. 3, some steps use cluster analysis, and some steps use association analysis, but its essence is all one
Sample, therefore, in whole Forecasting Methodology implementation process, can all use cluster analysis, can also all using association analysis,
That is cluster analysis is identical with association analysis essence, which step which algorithm last failure prediction is had no effect on using in
Result.
It should be noted that although describing the operation of the inventive method with particular order in the accompanying drawings, still, this is not required that
Or imply and must perform these operations according to the particular order, or the operation having to carry out shown in whole could realize the phase
The result of prestige.Additionally or alternatively, it is convenient to omit some steps, multiple steps are merged into a step and performed, and/or will
One step is decomposed into execution of multiple steps.
After the method for exemplary embodiment of the invention is described, next, with reference to figure 5 to the exemplary reality of the present invention
The defects of applying the transmission line of electricity of mode prediction meanss are introduced.The implementation of the device may refer to the implementation of the above method, weight
Multiple part repeats no more.Term " module " used below and " unit ", can realize the software of predetermined function and/or hard
Part.Although module described by following examples is preferably realized with software, hardware, or the combination of software and hardware
Realization and may and be contemplated.
The structural representation of the defects of Fig. 5 is the transmission line of electricity of embodiment of the present invention prediction meanss.As shown in figure 5, including：
Acquisition module 101, for obtaining Production MIS data, the power transmission and transformation equipment state monitoring of transmission line of electricity
System and weather information data, wherein, Production MIS packet parameter containing transmission line structure, power transmission line
Road defect historical data, line channel mima type microrelief data, the power transmission and transformation equipment state monitoring system are supervised online including microclimate
Survey data, microclimate monitoring historical data, icing online monitoring data, icing monitoring historical data, the weather information data
Include weather forecast data, meteorological historical data, meteorological live data；
Analysis module 102, for according to Production MIS data, power transmission and transformation equipment state monitoring system and gas
Image information data, using machine learning parser, generate the specified defect information of forecasting of the transmission line of electricity.
In the present embodiment, the machine learning parser includes cluster analysis and association analysis.
In the present embodiment, the analysis module 102 is used for according to Production MIS data, power transmission and transforming equipment shape
State monitoring system and weather information data, using machine learning parser, the specified defect for generating the transmission line of electricity is pre-
Measurement information, as shown in fig. 6, specifically including：
First specified defect information generating module 1021, for according to the transmission line of electricity defect historical data and the gas
As historical data, microclimate monitoring historical data, icing monitoring historical data, analysis is associated, obtains the first specified defect
Information, the first specified defect information include in the transmission line of electricity defect historical data by meteorological, microclimate and icing
The transmission line of electricity defect directly or indirectly triggered；
Second specified defect information generating module 1022, for according to the first specified defect information and transmission line of electricity knot
Structure parameter, line channel mima type microrelief data, cluster analysis is carried out, obtains the second specified defect information, second specified defect
Information include in the first specified defect information by transmission line structure parameter and line channel mima type microrelief data directly or
The transmission line of electricity defect triggered indirectly；
Specific meteorological historical information generation module 1023, for being gone through according to the second specified defect information and the meteorology
History data, microclimate monitoring historical data, icing monitoring historical data, are associated analysis, calculate and obtain specific meteorological history
Information, the specific meteorological historical information include causing what is occurred by the meteorology of a period of time in the second specified defect information
Defect；
Specific Weather Forecast Information generation module 1024, for pre- according to the specific meteorological historical information and the meteorology
Count off evidence, meteorological live data, microclimate online monitoring data, icing online monitoring data, cluster analysis is carried out, calculate and obtain
Specific Weather Forecast Information, the specific Weather Forecast Information are included when certain specific meteorological, microclimate and icing occurs, and having can
The defects of occurring；
Failure prediction information generating module 1025, for pre- according to the second specified defect information and the specific meteorology
Notify breath, be associated analysis, the specified defect of the specific line unit of transmission line of electricity, the transmission line of electricity tool can be predicted in generation
The specified defect of body line unit is included when certain specific meteorological, microclimate and icing occurs, it is possible to defect occurs, it would be possible to
The defects of generation the transmission line structure parameter related to the transmission line of electricity defect historical data, line channel mima type microrelief number
According to associated, generate specific overhead line structures it is possible that specified defect.In the present embodiment, the transmission line structure ginseng
Number includes：Line account, voltage class, circuit division number, wire type, shaft tower property, tower, shaft tower longitude and latitude, span, exhale
Height, shaft tower material, fixed form, shaft tower are high.
In the present embodiment, when carrying out cluster analysis, defect mode is divided into six classes, including ice trouble, thunderbolt, windburn,
Pollution flashover, external force is destroyed and bird pest.
The defects of transmission line of electricity provided in an embodiment of the present invention Forecasting Methodology and device, can using big data analytical technology pair
A variety of effective informations that transmission line of electricity can be influenceed carry out mining analysis, and can effectively predict may by meteorological effect on transmission line of electricity
The defects of generation, to realize Electrical Safety.
It should be understood by those skilled in the art that, embodiments of the invention can be provided as method, system or computer program
Product.Therefore, the present invention can use the reality in terms of complete hardware embodiment, complete software embodiment or combination software and hardware
Apply the form of example.Moreover, the present invention can use the computer for wherein including computer usable program code in one or more
The computer program production that usable storage medium is implemented on (including but is not limited to magnetic disk storage, CD-ROM, optical memory etc.)
The form of product.
The present invention is the flow with reference to method according to embodiments of the present invention, equipment (system) and computer program product
Figure and/or block diagram describe.It should be understood that can be by every first-class in computer program instructions implementation process figure and/or block diagram
Journey and/or the flow in square frame and flow chart and/or block diagram and/or the combination of square frame.These computer programs can be provided
The processors of all-purpose computer, special-purpose computer, Embedded Processor or other programmable data processing devices is instructed to produce
A raw machine so that produced by the instruction of computer or the computing device of other programmable data processing devices for real
The device for the function of being specified in present one flow of flow chart or one square frame of multiple flows and/or block diagram or multiple square frames.
These computer program instructions, which may be alternatively stored in, can guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works so that the instruction being stored in the computer-readable memory, which produces, to be included referring to
Make the manufacture of device, the command device realize in one flow of flow chart or multiple flows and/or one square frame of block diagram or
The function of being specified in multiple square frames.
These computer program instructions can be also loaded into computer or other programmable data processing devices so that counted
Series of operation steps is performed on calculation machine or other programmable devices to produce computer implemented processing, so as in computer or
The instruction performed on other programmable devices is provided for realizing in one flow of flow chart or multiple flows and/or block diagram one
The step of function of being specified in individual square frame or multiple square frames.
Apply specific embodiment in the present invention to be set forth the principle and embodiment of the present invention, above example
Explanation be only intended to help understand the present invention method and its core concept；Meanwhile for those of ordinary skill in the art,
According to the thought of the present invention, there will be changes in specific embodiments and applications, in summary, in this specification
Appearance should not be construed as limiting the invention.
Claims (10)
- A kind of 1. the defects of transmission line of electricity Forecasting Methodology, it is characterised in that including：Obtain Production MIS data, power transmission and transformation equipment state monitoring system and the weather information number of transmission line of electricity According to, wherein, Production MIS packet parameter containing transmission line structure, transmission line of electricity defect historical data, line Paths mima type microrelief data, the power transmission and transformation equipment state monitoring system includes microclimate online monitoring data, microclimate monitors Historical data, icing online monitoring data, icing monitoring historical data, the weather information data include weather forecast data, Meteorological historical data, meteorological live data；According to Production MIS data, power transmission and transformation equipment state monitoring system and weather information data, machine is utilized Study analysis algorithm, generate the specified defect information of forecasting of the transmission line of electricity.
- 2. the defects of transmission line of electricity according to claim 1 Forecasting Methodology, it is characterised in that the machine learning analysis is calculated Method includes cluster analysis and association analysis.
- 3. the defects of transmission line of electricity according to claim 2 Forecasting Methodology, it is characterised in that described to be believed according to production management System data, power transmission and transformation equipment state monitoring system and weather information data are ceased, using machine learning parser, generates institute The specified defect information of forecasting of transmission line of electricity is stated, is specifically included：According to the transmission line of electricity defect historical data and the meteorological historical data, microclimate monitoring historical data, icing prison Historical data is surveyed, analysis is associated, obtains the first specified defect information, the first specified defect information includes the transmission of electricity The transmission line of electricity defect directly or indirectly triggered by meteorological, microclimate and icing in line defct historical data；According to the first specified defect information and transmission line structure parameter, line channel mima type microrelief data, cluster point is carried out Analysis, obtains the second specified defect information, the second specified defect information include in the first specified defect information by defeated The transmission line of electricity defect that electric line structural parameters and line channel mima type microrelief data directly or indirectly trigger；Gone through according to the second specified defect information and the meteorological historical data, microclimate monitoring historical data, icing monitoring History data, analysis is associated, calculates and obtain specific meteorological historical information, the specific meteorological historical information includes described second The defects of causing to occur by the meteorology of a period of time in specified defect information；According to the specific meteorological historical information and the weather forecast data, meteorological live data, microclimate on-line monitoring number According to, icing online monitoring data, cluster analysis is carried out, calculates and obtains specific Weather Forecast Information, the specific weather forecast letter Breath is included when certain specific meteorological, microclimate and icing occurs, it is possible to the defects of occurring；According to the second specified defect information and the specific Weather Forecast Information, analysis is associated, generation can be predicted The specified defect of the specific line unit of transmission line of electricity, the specified defect of the specific line unit of transmission line of electricity, which includes working as, occurs certain When specific meteorological, microclimate and icing, it is possible to defect occurs, it would be possible to the defects of occurring and the transmission line of electricity defect history Related transmission line structure parameter, line channel mima type microrelief data are associated in data, and generating specific overhead line structures may The specified defect of appearance.
- 4. the defects of transmission line of electricity according to claim 3 Forecasting Methodology, it is characterised in that the transmission line structure ginseng Number includes：Line account, voltage class, circuit division number, wire type, shaft tower property, tower, shaft tower longitude and latitude, span, exhale Height, shaft tower material, fixed form, shaft tower are high.
- 5. the defects of transmission line of electricity according to claim 2 Forecasting Methodology, it is characterised in that when carrying out cluster analysis, Defect mode is divided into six classes, including ice trouble, thunderbolt, windburn, pollution flashover, external force destruction and bird pest.
- A kind of 6. the defects of transmission line of electricity prediction meanss, it is characterised in that including：Acquisition module, for obtain Production MIS data, the power transmission and transformation equipment state monitoring system of transmission line of electricity with And weather information data, wherein, Production MIS packet parameter containing transmission line structure, transmission line of electricity defect Historical data, line channel mima type microrelief data, the power transmission and transformation equipment state monitoring system include microclimate online monitoring data, Microclimate monitoring historical data, icing online monitoring data, icing monitoring historical data, the weather information data include meteorology Forecast data, meteorological historical data, meteorological live data；Analysis module, for according to Production MIS data, power transmission and transformation equipment state monitoring system and weather information Data, using machine learning parser, generate the specified defect information of forecasting of the transmission line of electricity.
- 7. the defects of transmission line of electricity according to claim 6 prediction meanss, it is characterised in that the machine learning analysis is calculated Method includes cluster analysis and association analysis.
- 8. the defects of transmission line of electricity according to claim 7 prediction meanss, it is characterised in that the analysis module is used for root According to the transmission line structure parameter, transmission line of electricity defect historical data and the weather forecast data, meteorological historical data, profit With machine learning parser, the specified defect information of forecasting of the transmission line of electricity is generated, is specifically included：First specified defect information generating module, for according to the transmission line of electricity defect historical data and the meteorological history number According to, microclimate monitoring historical data, icing monitoring historical data, analysis is associated, obtains the first specified defect information, it is described First specified defect information includes direct or indirect by meteorological, microclimate and icing in the transmission line of electricity defect historical data The transmission line of electricity defect of initiation；Second specified defect information generating module, for according to the first specified defect information and transmission line structure parameter, Line channel mima type microrelief data, cluster analysis is carried out, obtains the second specified defect information, the second specified defect information includes Directly or indirectly being triggered by transmission line structure parameter and line channel mima type microrelief data in the first specified defect information Transmission line of electricity defect；Specific meteorological historical information generation module, for according to the second specified defect information and the meteorological historical data, Microclimate monitoring historical data, icing monitoring historical data, are associated analysis, calculate and obtain specific meteorological historical information, institute State the defects of specific meteorological historical information includes causing to occur by the meteorology of a period of time in the second specified defect information；Specific Weather Forecast Information generation module, for according to the specific meteorological historical information and the weather forecast data, Meteorological live data, microclimate online monitoring data, icing online monitoring data, cluster analysis is carried out, calculate and obtain specific gas As forecast information, the specific Weather Forecast Information is included when certain specific meteorological, microclimate and icing occurs, it is possible to occurs The defects of；Failure prediction information generating module, for according to the second specified defect information and the specific Weather Forecast Information, Analysis is associated, the specified defect of the specific line unit of transmission line of electricity, the specific circuit of transmission line of electricity can be predicted in generation The specified defect of unit is included when certain specific meteorological, microclimate and icing occurs, it is possible to defect occurs, it would be possible to generation The defect transmission line structure parameter related to the transmission line of electricity defect historical data, line channel mima type microrelief data are related Connection, generate specific overhead line structures it is possible that specified defect.
- 9. the defects of transmission line of electricity according to claim 8 prediction meanss, it is characterised in that the transmission line structure ginseng Number includes：Line account, voltage class, circuit division number, wire type, shaft tower property, tower, shaft tower longitude and latitude, span, exhale Height, shaft tower material, fixed form, shaft tower are high.
- 10. the defects of transmission line of electricity according to claim 7 prediction meanss, it is characterised in that when carrying out cluster analysis, Defect mode is divided into six classes, including ice trouble, thunderbolt, windburn, pollution flashover, external force destruction and bird pest.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109359700A (en) * | 2018-11-21 | 2019-02-19 | 北京国网富达科技发展有限责任公司 | A kind of analysis method, equipment and system that transmission line of electricity external force is destroyed |
| CN109886396A (en) * | 2019-03-18 | 2019-06-14 | 国家电网有限公司 | A kind of transmission line galloping on-line prediction system and method |
| CN110232399A (en) * | 2019-04-24 | 2019-09-13 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | The transmission facility defect analysis method and system clustered based on Set Pair Analysis and K-means |
| CN111210086A (en) * | 2020-01-15 | 2020-05-29 | 杭州华网信息技术有限公司 | National power grid icing disaster prediction method |
| CN111652448A (en) * | 2020-07-08 | 2020-09-11 | 广东电网有限责任公司清远供电局 | Method, device and system for predicting defect rate of power transmission line |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103246936A (en) * | 2013-04-24 | 2013-08-14 | 广东电网公司中山供电局 | System and method for pre-warning of typhoon risks of overhead transmission lines of grid |
| CN104239672A (en) * | 2014-05-01 | 2014-12-24 | 云南大学 | Microclimate feature extraction and qualitative warning method for power transmission line icing processes |
| CN105809287A (en) * | 2016-03-10 | 2016-07-27 | 云南大学 | High-voltage transmission line icing process integrated prediction method |
| CN105891629A (en) * | 2016-03-31 | 2016-08-24 | 广西电网有限责任公司电力科学研究院 | Transformer equipment fault identification method |
| CN106469356A (en) * | 2016-09-28 | 2017-03-01 | 国家电网公司 | Transmission facility state evaluation based on multidimensional data and risk analysis method and device |
| CN106682772A (en) * | 2016-12-14 | 2017-05-17 | 贵州电网有限责任公司电力科学研究院 | Method for comprehensively predicting power transmission line fault rate based on multidimensional cloud model |
| CN107784392A (en) * | 2017-10-27 | 2018-03-09 | 华北电力科学研究院有限责任公司 | A kind of the defects of transmission line of electricity based on machine learning Forecasting Methodology and device |
-
2017
- 2017-10-27 CN CN201711027293.2A patent/CN107784393A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103246936A (en) * | 2013-04-24 | 2013-08-14 | 广东电网公司中山供电局 | System and method for pre-warning of typhoon risks of overhead transmission lines of grid |
| CN104239672A (en) * | 2014-05-01 | 2014-12-24 | 云南大学 | Microclimate feature extraction and qualitative warning method for power transmission line icing processes |
| CN105809287A (en) * | 2016-03-10 | 2016-07-27 | 云南大学 | High-voltage transmission line icing process integrated prediction method |
| CN105891629A (en) * | 2016-03-31 | 2016-08-24 | 广西电网有限责任公司电力科学研究院 | Transformer equipment fault identification method |
| CN106469356A (en) * | 2016-09-28 | 2017-03-01 | 国家电网公司 | Transmission facility state evaluation based on multidimensional data and risk analysis method and device |
| CN106682772A (en) * | 2016-12-14 | 2017-05-17 | 贵州电网有限责任公司电力科学研究院 | Method for comprehensively predicting power transmission line fault rate based on multidimensional cloud model |
| CN107784392A (en) * | 2017-10-27 | 2018-03-09 | 华北电力科学研究院有限责任公司 | A kind of the defects of transmission line of electricity based on machine learning Forecasting Methodology and device |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109359700A (en) * | 2018-11-21 | 2019-02-19 | 北京国网富达科技发展有限责任公司 | A kind of analysis method, equipment and system that transmission line of electricity external force is destroyed |
| CN109886396A (en) * | 2019-03-18 | 2019-06-14 | 国家电网有限公司 | A kind of transmission line galloping on-line prediction system and method |
| CN110232399A (en) * | 2019-04-24 | 2019-09-13 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | The transmission facility defect analysis method and system clustered based on Set Pair Analysis and K-means |
| CN111210086A (en) * | 2020-01-15 | 2020-05-29 | 杭州华网信息技术有限公司 | National power grid icing disaster prediction method |
| CN111210086B (en) * | 2020-01-15 | 2023-09-22 | 国网安徽省电力有限公司宁国市供电公司 | National power grid icing disaster prediction method |
| CN111652448A (en) * | 2020-07-08 | 2020-09-11 | 广东电网有限责任公司清远供电局 | Method, device and system for predicting defect rate of power transmission line |
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