CN109446474A - A kind of methods of exhibiting and system of waving property of data - Google Patents
A kind of methods of exhibiting and system of waving property of data Download PDFInfo
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Abstract
A kind of methods of exhibiting and system of waving property of data, comprising: the potential impact factor pair based on transmission line galloping waves information record sheet and carries out screening acquisition first kind impact factor；Based on it is described wave to wave correlation between data in information record sheet and carry out dimension-reduction treatment to first kind impact factor obtain the second class impact factor；Waving property of data is shown based on the second class impact factor.Technical solution provided by the invention can the intuitively positive negative sample of display data distribution situation, data characteristics are waved convenient for understanding, to predict that transmission line galloping provides foundation by waving data.
Description
Technical field
It prevents and reduces natural disasters field the present invention relates to transmission line of electricity, and in particular to a kind of methods of exhibiting of waving property of data and be
System.
Background technique
Transmission line galloping accident is to threaten one of the important disaster form of power network safety operation, in recent years, with
The fast development of artificial intelligence technology, power grid prevent and reduce natural disasters field application increasingly deeply, people are by artificial intelligence technology
It is introduced into the work of transmission line galloping prediction and warning, largely waves related data using accumulation, it can to the generation for the accident of waving
Energy property carries out prediction and warning, so that route O&M department can prejudge the generation of disaster in advance, formulates counter-measure, reduces
Wave the damage that accident generates power grid.However, waving, related data is various informative, how to carrying out in the data of magnanimity complexity
It is intuitive to show, information most valuable in research is waved convenient for intuitively reacting, is furtherd investigate.
Summary of the invention
In order to solve the problems, such as waving in the presence of the prior art, predictablity rate is low, and the present invention provides a kind of data dance
The methods of exhibiting and system of dynamic property.
Present invention provide the technical scheme that a kind of methods of exhibiting of waving property of data, comprising:
Potential impact factor pair based on transmission line galloping waves information record sheet and carries out screening acquisition first kind influence
The factor；
Based on described wave correlation between data is waved in information record sheet dimensionality reduction is carried out to first kind impact factor
Processing obtains the second class impact factor；
Waving property of data is shown based on the second class impact factor.
Preferably, it is described based on it is described wave waved in information record sheet correlation between data on the first kind influence because
Son carries out dimension-reduction treatment and obtains the second class impact factor, comprising:
From the power transmission line waved and the first impact factor and first impact factor is selected to be related in information record sheet
Road related data generates analytical data；
Dimension-reduction treatment is carried out to first kind impact factor using Principal Component Analysis based on the analytical data and obtains the
Two class impact factors.
Preferably, described that dimensionality reduction is carried out to first kind impact factor using Principal Component Analysis based on the analytical data
Processing obtains the second class impact factor, comprising:
Correlation matrix is constructed based on the related coefficient waved between data in the analytical data；
The characteristic value and feature vector of the correlation matrix are obtained based on Jacobi method；
Quantity based on the characteristic value and first kind impact factor calculates the accumulative contribution of the first kind impact factor
Rate；
Relationship based on the contribution rate of accumulative total and preset range, the contribution rate of accumulative total that selection meets condition are corresponding
Characteristic value；
Principal component load is calculated based on the corresponding characteristic value and described eigenvector；
The second class impact factor is calculated by the formula of setting based on the principal component load and first kind impact factor.
Preferably, described that related coefficient square is constructed based on the related coefficient waved between data in the analytical data
Battle array, is shown below:
In formula: R: correlation matrix；rij: i-th of impact factor x in first kind impact factoriWith j-th of impact factor
xjRelated coefficient；P: the quantity of first kind impact factor.
Preferably, i-th of impact factor x in the first kind impact factoriWith j-th of impact factor xjRelated coefficient
rij, it is calculated as follows:
In formula: n: number of samples；xki: i-th of impact factor x in the first kind impact factor in k-th of samplei；The
I-th of impact factor x in a kind of impact factoriMean value；xkj: j-th of influence in the first kind impact factor in k-th of sample
Factor xj；J-th of impact factor x in first kind impact factorjMean value；
Wherein, i-th of impact factor x in the first kind impact factoriMean valueIn the first kind impact factor
J-th of impact factor xjMean valueIt is calculated as follows:
Preferably, the quantity based on the characteristic value and first kind impact factor, calculate the first kind influence because
The contribution rate of accumulative total of son, is calculated as follows:
In formula: Fm: the contribution rate of accumulative total of m impact factor in first kind impact factor；λi: the feature of i-th of impact factor
Value；P: the quantity of first kind impact factor.
Preferably, the relationship based on the contribution rate of accumulative total and preset range, selection meet the described accumulative of condition
The corresponding characteristic value of contribution rate, comprising:
As the contribution rate of accumulative total F of m impact factor in the first kind impact factormHave and only one is in preset range
When interior, the contribution rate of accumulative total F is selectedmCorresponding characteristic value；
As the contribution rate of accumulative total F of m impact factor in the first kind impact factormHave and at least there are two in default model
When enclosing interior, the corresponding contribution rate of accumulative total F of the smallest m is selectedm, and obtain the contribution rate of accumulative total FmCorresponding characteristic value.
Preferably, described that principal component load is calculated based on the corresponding characteristic value and described eigenvector, it counts as the following formula
It calculates:
In formula: lij: the principal component load of i-th of impact factor and j-th of impact factor in first kind impact factor；λi:
The characteristic value of i-th of impact factor；eij: in the first kind impact factor feature of i-th of impact factor and j-th of impact factor to
Amount；P: the quantity of first kind impact factor.
Preferably, described to be calculated second by the formula of setting based on the principal component load and first kind impact factor
Class impact factor, is calculated as follows:
In formula: the Z: the second class impact factor；zm: m-th of principal component in the second class impact factor；lij: the first kind influences
The principal component load of i-th of impact factor and j-th of impact factor in the factor；xp: p-th of influence in first kind impact factor
The factor.
Preferably, the potential impact factor pair based on transmission line galloping waves information record sheet and carries out screening acquisition
First kind impact factor, comprising:
It is related that the potential impact factor based on transmission line galloping obtains the transmission line of electricity that the potential impact factor is related to
Data；
Information is waved in the transmission line of electricity related data building being related to based on the potential impact factor and the potential impact factor
Record sheet；
Impact factor composition first kind impact factor to be analyzed is filtered out from the information record sheet of waving；
Wherein, the potential impact factor of the transmission line galloping, comprising: wind speed, wind direction, line alignment and wind direction press from both sides
Angle, temperature, humidity, precipitation form, wire icing thickness, landform, height above sea level, voltage class, shaft tower feeder number, twisted wire section
Product, arrangement of conductor and conducting wire division number.
Preferably, the potential impact factor pair based on transmission line galloping waves information record sheet and carries out screening acquisition
First kind impact factor, further includes:
Numerical value shape will be converted by the conversion principle of setting by the impact factor of verbal description in first kind impact factor
Formula.
It is preferably, described that waving property of data is shown based on the second class impact factor, comprising:
The first two principal component is selected from the second class impact factor, and principal component described in analytical data is corresponding defeated
Electric line related data is plotted in rectangular coordinate system, passes through waving property of rectangular coordinate system display data.
Based on the same inventive concept, the present invention also provides a kind of display systems of waving property of data, comprising:
Screening module, for the potential impact factor pair based on transmission line galloping wave information record sheet carry out screening obtain
Obtain first kind impact factor；
Dimensionality reduction module, for waving correlation between data in information record sheet based on described wave and being influenced on the first kind
The factor carries out dimension-reduction treatment and obtains the second class impact factor；
Display module, for being shown based on the second class impact factor to waving property of data.
Preferably, the dimensionality reduction module, comprising:
Generate submodule, for from it is described wave selected in information record sheet the first impact factor and it is described first influence because
The transmission line of electricity related data that son is related to generates analytical data；
Dimensionality reduction submodule, for being carried out using Principal Component Analysis to first kind impact factor based on the analytical data
Dimension-reduction treatment obtains the second class impact factor.
Compared with prior art, the invention has the benefit that
Technical solution provided by the invention, the potential impact factor pair based on transmission line galloping wave information record sheet into
Row screening obtains first kind impact factor；Correlation between data is waved in information record sheet to the first kind based on described wave
Impact factor carries out dimension-reduction treatment and obtains the second class impact factor；Waving property of data is carried out based on the second class impact factor
Show, can the intuitively positive negative sample of display data distribution situation, data characteristics are waved in understanding, for by waving data prediction
Transmission line galloping provides foundation.
Technical solution provided by the invention can extract and wave the closely related impact factor of incidence, reduce dance
The dimension of input vector, improves the accuracy rate for waving prediction and warning in dynamic prediction model.
Detailed description of the invention
Fig. 1 is methods of exhibiting flow chart provided by the invention；
Fig. 2 waves information record sheet schematic diagram to be provided in an embodiment of the present invention；
Fig. 3 is the coordinate transform figure of wind direction provided in an embodiment of the present invention and line alignment Data Format Transform；
Fig. 4 is the principal component analysis schematic diagram in the embodiment of the present invention；
Fig. 5 is to wave positive and negative sample distribution schematic diagram in data in the embodiment of the present invention.
Specific embodiment
For a better understanding of the present invention, the contents of the present invention are done further with example with reference to the accompanying drawings of the specification
Explanation.
Embodiment 1:
Fig. 1 is methods of exhibiting flow chart provided by the invention, comprising:
S1, the potential impact factor pair based on transmission line galloping wave information record sheet and carry out screening acquisition first kind shadow
Ring the factor；
S2, based on described wave the correlation between data is waved in information record sheet first kind impact factor is dropped
Dimension handles to obtain the second class impact factor；
S3, waving property of data is shown based on the second class impact factor.
For the text data used in the present embodiment is " waving information record sheet ", the displaying side that provides through the invention
Method is shown, and part of data are as shown in Figure 2.
S1, the potential impact factor pair based on transmission line galloping wave information record sheet and carry out screening acquisition first kind shadow
The factor is rung, is specifically included:
1.1 wave data description
1.1.1 initial data and its formatting
Transmission line galloping related data is numerous and jumbled, and form is polynary, including the multiple types such as image, text, video, the present invention
It is studied just for text data.
The potential impact factor based on transmission line galloping obtains the transmission line of electricity related data that the potential impact factor is related to；
Information record is waved in the transmission line of electricity related data building being related to based on the potential impact factor and the potential impact factor
Table.
It marks the record for being to be denoted as positive sample in Fig. 2, is denoted as negative sample labeled as the non-record waved, it is clear that positive and negative
Sample distribution is unbalanced, and ratio is about 1:7.
Since the present embodiment is intended to analyze the major influence factors of transmission line galloping, it only chooses and waves the origin cause of formation
Relevant data are analyzed.And the potential impact factor of transmission line galloping can be classified as three categories, first is that meteorologic factor, two
It is geographic factor, third is that the structure feature of transmission line of electricity itself.
1) meteorologic factor: wind speed, wind direction, line alignment and wind direction angle, temperature, humidity, precipitation form, wire icing are thick
Degree.
2) geographic factor: landform, height above sea level.
3) structure feature of transmission line of electricity itself: voltage class, shaft tower feeder number, twisted wire sectional area, arrangement of conductor,
Conducting wire division number.
According to waving genesis mechanism and wave the theoretical analysis result of rule, the following 12 impact factors composition of screening the
A kind of impact factor is analyzed: voltage class, height above sea level, line alignment, wind speed, wind direction, wind direction and route angle, temperature
Degree, humidity, precipitation form, wire icing thickness, outer twisted wire area and conducting wire division number.
From waving the transmission line of electricity dependency number for selecting the first impact factor and the first impact factor to be related in information record sheet
According to generation analytical data.
Numerical value shape will be converted by the conversion principle of setting by the impact factor of verbal description in first kind impact factor
Formula.
As shown in Figure 2, line alignment, wind direction, 3 impact factors of precipitation form value be verbal description, remaining 9 influence
The factor is numeric type information, in order to be identified by Data Mining Tools, needs to be converted to verbal description numeric type letter
Breath, wherein line alignment and wind direction are converted according to coordinate shown in Fig. 3.
Specific format conversion principle is as shown in table 1.1 to table 1.3.
1.1 line alignment format conversion principle of table
The conversion of 1.2 wind direction format of table
| Wind direction | Corresponding numerical value |
| Northern (by north) | 90 |
| Northeast | 45 |
| Northwest | 135 |
The conversion of 1.3 precipitation formal mode of table
| Precipitation form | Corresponding numerical value |
| Rime | 1 |
| Glaze | 2 |
| Snow slush | 3 |
1.1.2 data are intuitively shown
Sample (positive sample) and the non-distribution situation for waving sample (negative sample) are waved in order to more intuitively show, first
The difference for comparing the mean value and standard deviation of each impact factor of positive negative sample, as shown in table 1.4 and table 1.5.
The mean value of 1.4 each impact factor of positive negative sample of table compares
The standard deviation of 1.5 each impact factor of positive negative sample of table compares
By table 1.4 and table 1.5 it is found that the mean value of each impact factor of positive negative sample and the difference of standard deviation are larger, show
Wave sample and it is non-wave sample apparent difference is distributed with.
S2, based on described wave the correlation between data is waved in information record sheet first kind impact factor is dropped
Dimension handles to obtain the second class impact factor, comprising:
In order to more intuitively show the distribution situation of positive negative sample, drawing sample distribution figure is optimal selection, but is waved
Dynamic data have 12 dimensions, belong to high dimensional data, if wanting to be shown in two or three-dimensional space, need to drop to data
Dimension.
The present invention carries out dimensionality reduction, principal component analysis (Principal to data are waved using principal component analytical method
Component Analysis, PCA) it is that one kind carries out data to compress common method, this method is by original variable
By linear transformation, new variables as few as possible is established, so that these new variables are incoherent two-by-two, and these new variables
Original information is kept as far as possible.The essence of principal component analysis is that original coordinate system is translated and rotated, so that new coordinate
Origin be overlapped with the center of gravity of data group point, the first axle of new coordinate system and the maximum direction of data variation are corresponding, new coordinate
Second axis and first axle normal orthogonal, and the maximum direction for corresponding to data variation is corresponding, and so on.
As shown in figure 4, these new axis are properly termed as the first main shaft e1, the second main shaft e2Etc., after being given up a small amount of information,
Main shaft e1,e2,...,em(m < p) can effectively indicate the variation situation of former data, be down to m dimension by original p dimension space and generate
New space L (e1,e2,...,em) it is known as the main hyperplane of m dimension, projection approximation of the sample origin on main hyperplane expresses former group
Point.
Projection of the former group's point in main hyperplane first axle constitutes the first variable z of new data table1∈Rp, referred to as first is main
Ingredient, generally, zhReferred to as h principal component h=1,2 ..., m.
If with μhIndicate zhMean value, ∨ (zh) indicate zhVariance, then the result of principal component analysis are as follows:
According to data variation maximum direction principle, z1It is to carry the most one-dimensional variable of former data information, and m dimension master is super flat
Face is to retain the maximum m-dimensional space of former data information, and by least square principle, m ties up the m that main hyperplane is closest to original sample point
Tie up hyperplane.
Suppose there is n sample, each sample shares p variable, constitutes the data matrix of n × p rank,
Remember that former variable index is x1, x2..., xpIf the overall target after their dimension-reduction treatment, i.e. new variables are z1, z2,
z3..., zm(m≤p), then
Coefficient l in the present embodimentijDetermination principle it is as follows:
①ziWith zj(i≠j；I, j=1,2 ..., m) it is independent of each other；
②z1It is x1,x2…xpAll linear combinations in variance the maximum, z2It is and z2Incoherent x1,x2…xpInstitute
Variance the maximum in linear combination；zmIt is and z1,z2…zm-1All incoherent x1,x2…xpAll linear combinations in variance
The maximum.
Second impact factor z1,z2…zmIt is referred to as the first impact factor x1,x2…xpThe the 1st, the 2nd ..., m it is main at
Point.
From above analysis as can be seen that the essence of principal component analysis is exactly to determine primal variable xj(j=1,2 ..., p)
In all principal component ziLoad l on (i=1,2 ..., m)ij(i=1,2 ..., m；J=1,2 ..., p).
Mathematically it can be proved that they are feature vector corresponding to m biggish characteristic values of correlation matrix respectively,
Steps are as follows for calculating:
(1) correlation matrix is calculated
rij: i-th of impact factor x in first kind impact factoriWith j-th of impact factor xjRelated coefficient, and rij=
rji, its calculation formula is:
Wherein,Respectively indicate xiAnd xjMean value, and be calculated as follows:
(2) eigen vector is calculated
Solve characteristic equation | λ I-R |=0, common Jacobi method (Jacobi) finds out eigenvalue λi；
It is found out respectively corresponding to eigenvalue λiFeature vector ei(i=1,2...p), it is desirable thatI.e.Its
Middle eijIndicate the feature vector of i-th of impact factor and j-th of impact factor in first kind impact factor.
(3) contribution rate of accumulative total of principal component is calculated as follows
When accumulating contribution rate greater than 85%, it is considered as to reflect the information of primal variable enough, corresponding m is exactly to take out
The preceding m principal component taken.
(4) principal component load is calculated
The principal component load and first kind impact factor being calculated according to (formula 7) obtain new variables by (formula 2)
The first, second principal component is selected after dimensionality reduction to describe the distribution situation of positive negative sample, as shown in figure 5, wherein waving
Sample, that is, positive sample, it is non-to wave sample, i.e. negative sample；Obvious Fig. 5 can be it is clearly seen that positive negative sample has more significantly
Difference.
Embodiment 2
Based on the same inventive concept, the present embodiment additionally provides a kind of display systems of waving property of data, comprising:
Screening module, for the potential impact factor pair based on transmission line galloping wave information record sheet carry out screening obtain
Obtain first kind impact factor；
Dimensionality reduction module, for waving correlation between data in information record sheet based on described wave and being influenced on the first kind
The factor carries out dimension-reduction treatment and obtains the second class impact factor；
Display module, for being shown based on the second class impact factor to waving property of data.
In embodiment, the dimensionality reduction module, comprising:
Generate submodule, for from it is described wave selected in information record sheet the first impact factor and it is described first influence because
The transmission line of electricity related data that son is related to generates analytical data；
Dimensionality reduction submodule, for being carried out using Principal Component Analysis to first kind impact factor based on the analytical data
Dimension-reduction treatment obtains the second class impact factor.
In embodiment, the dimensionality reduction submodule, comprising:
Construction unit, for constructing related coefficient square based on the related coefficient waved between data in the analytical data
Battle array；
First computing unit, for obtaining the characteristic value and feature vector of the correlation matrix based on Jacobi method；
Contribution rate of accumulative total unit is calculated, for the quantity based on the characteristic value and first kind impact factor, described in calculating
The contribution rate of accumulative total of first kind impact factor；
Comparing unit, for the relationship based on the contribution rate of accumulative total and preset range, selection meets the described tired of condition
Count the corresponding characteristic value of contribution rate；
Second computing unit, for calculating principal component load based on the corresponding characteristic value and described eigenvector；
As a result unit, for being calculated by the formula of setting based on the principal component load and first kind impact factor
Two class impact factors.
In embodiment, the screening module, comprising:
Acquiring unit obtains what the potential impact factor was related to for the potential impact factor based on transmission line galloping
Transmission line of electricity related data；
Building record table unit, the transmission line of electricity phase for being related to based on the potential impact factor and the potential impact factor
It closes data building and waves information record sheet；
Recomposition unit, for filtering out impact factor composition first kind influence to be analyzed from the information record sheet of waving
The factor；
Wherein, the potential impact factor of the transmission line galloping, comprising: wind speed, wind direction, line alignment and wind direction press from both sides
Angle, temperature, humidity, precipitation form, wire icing thickness, landform, height above sea level, voltage class, shaft tower feeder number, twisted wire section
Product, arrangement of conductor and conducting wire division number.
Obviously, described embodiments are some of the embodiments of the present invention, instead of all the embodiments.Based on the present invention
In embodiment, all other implementation obtained by those of ordinary skill in the art without making creative efforts
Example, shall fall within the protection scope of the present invention.
It should be understood by those skilled in the art that, embodiments herein can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the application, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The application is referring to method, the process of equipment (system) and computer program product according to the embodiment of the present application
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
The above is only the embodiment of the present invention, are not intended to restrict the invention, all in the spirit and principles in the present invention
Within, any modification, equivalent substitution, improvement and etc. done, be all contained in apply pending scope of the presently claimed invention it
It is interior.
Claims (14)
1. a kind of methods of exhibiting of waving property of data characterized by comprising
Potential impact factor pair based on transmission line galloping waves information record sheet and carries out screening acquisition first kind impact factor；
Based on described wave correlation between data is waved in information record sheet dimension-reduction treatment is carried out to first kind impact factor
Obtain the second class impact factor；
Waving property of data is shown based on the second class impact factor.
2. methods of exhibiting as described in claim 1, which is characterized in that described to wave number based on described wave in information record sheet
Correlation between carries out dimension-reduction treatment to first kind impact factor and obtains the second class impact factor, comprising:
From the transmission line of electricity phase waved and the first impact factor and first impact factor is selected to be related in information record sheet
Data are closed, analytical data is generated；
Dimension-reduction treatment is carried out to first kind impact factor using Principal Component Analysis based on the analytical data and obtains the second class
Impact factor.
3. methods of exhibiting as claimed in claim 2, which is characterized in that described to utilize principal component point based on the analytical data
Analysis method carries out dimension-reduction treatment to first kind impact factor and obtains the second class impact factor, comprising:
Correlation matrix is constructed based on the related coefficient waved between data in the analytical data；
The characteristic value and feature vector of the correlation matrix are obtained based on Jacobi method；
Quantity based on the characteristic value and first kind impact factor calculates the contribution rate of accumulative total of the first kind impact factor；
Relationship based on the contribution rate of accumulative total and preset range, selection meet the corresponding feature of the contribution rate of accumulative total of condition
Value；
Principal component load is calculated based on the corresponding characteristic value and described eigenvector；
The second class impact factor is calculated by the formula of setting based on the principal component load and first kind impact factor.
4. methods of exhibiting as claimed in claim 3, which is characterized in that it is described based on wave in the analytical data data it
Between related coefficient construct correlation matrix, be shown below:
In formula: R: correlation matrix；rij: i-th of impact factor x in first kind impact factoriWith j-th of impact factor xj's
Related coefficient；P: the quantity of first kind impact factor.
5. methods of exhibiting as claimed in claim 4, which is characterized in that i-th of impact factor x in the first kind impact factori
With j-th of impact factor xjCorrelation coefficient rij, it is calculated as follows:
In formula: n: number of samples；xki: i-th of impact factor x in the first kind impact factor in k-th of samplei；The first kind
I-th of impact factor x in impact factoriMean value；xkj: j-th of impact factor in the first kind impact factor in k-th of sample
xj；J-th of impact factor x in first kind impact factorjMean value；
Wherein, i-th of impact factor x in the first kind impact factoriMean valueWith jth in the first kind impact factor
A impact factor xjMean valueIt is calculated as follows:
6. methods of exhibiting as claimed in claim 3, which is characterized in that described to be based on the characteristic value and first kind impact factor
Quantity, calculate the contribution rate of accumulative total of the first kind impact factor, be calculated as follows:
In formula: Fm: the contribution rate of accumulative total of m impact factor in first kind impact factor；λi: the characteristic value of i-th of impact factor；
P: the quantity of first kind impact factor.
7. methods of exhibiting as claimed in claim 6, which is characterized in that described based on the contribution rate of accumulative total and preset range
Relationship, selection meet the corresponding characteristic value of the contribution rate of accumulative total of condition, comprising:
As the contribution rate of accumulative total F of m impact factor in the first kind impact factormHave and only one within a preset range when,
Select the contribution rate of accumulative total FmCorresponding characteristic value；
As the contribution rate of accumulative total F of m impact factor in the first kind impact factormHave and at least there are two within a preset range
When, select the corresponding contribution rate of accumulative total F of the smallest mm, and obtain the contribution rate of accumulative total FmCorresponding characteristic value.
8. methods of exhibiting as claimed in claim 3, which is characterized in that described to be based on the corresponding characteristic value and the feature
Vector calculates principal component load, is calculated as follows:
(i=1,2...m, j=1,2...p, and m < p)
In formula: lij: the principal component load of i-th of impact factor and j-th of impact factor in first kind impact factor；λi: i-th
The characteristic value of impact factor；eij: the feature vector of i-th of impact factor and j-th of impact factor in first kind impact factor；P:
The quantity of first kind impact factor.
9. methods of exhibiting as claimed in claim 3, which is characterized in that described to be influenced based on the principal component load and the first kind
The second class impact factor is calculated by the formula of setting in the factor, is calculated as follows:
In formula: the Z: the second class impact factor；zm: m-th of principal component in the second class impact factor；lij: first kind impact factor
In i-th of impact factor and j-th of impact factor principal component load；xp: influence p-th in first kind impact factor because
Son.
10. methods of exhibiting as described in claim 1, which is characterized in that it is described based on the potential impact of transmission line galloping because
Son carries out screening acquisition first kind impact factor to information record sheet is waved, comprising:
The potential impact factor based on transmission line galloping obtains the transmission line of electricity related data that the potential impact factor is related to；
Information record is waved in the transmission line of electricity related data building being related to based on the potential impact factor and the potential impact factor
Table；
Impact factor composition first kind impact factor to be analyzed is filtered out from the information record sheet of waving；
Wherein, the potential impact factor of the transmission line galloping, comprising: wind speed, wind direction, line alignment and wind direction angle, temperature
Degree, precipitation form, wire icing thickness, landform, height above sea level, voltage class, shaft tower feeder number, twisted wire sectional area, is led humidity
Line arrangement mode and conducting wire division number.
11. methods of exhibiting as claimed in claim 10, which is characterized in that it is described based on the potential impact of transmission line galloping because
Son carries out screening acquisition first kind impact factor to information record sheet is waved, further includes:
Numeric form will be converted by the conversion principle of setting by the impact factor of verbal description in first kind impact factor.
12. methods of exhibiting as claimed in claim 3, which is characterized in that described to be based on the second class impact factor to data
Waving property is shown, comprising:
The first two principal component is selected from the second class impact factor, and by the corresponding power transmission line of principal component described in analytical data
Road related data is plotted in rectangular coordinate system, passes through waving property of rectangular coordinate system display data.
13. a kind of display systems of waving property of data characterized by comprising
Screening module, waves information record sheet for the potential impact factor pair based on transmission line galloping and carries out screening and obtain the
A kind of impact factor；
Dimensionality reduction module, for waving correlation between data in information record sheet to first kind impact factor based on described wave
It carries out dimension-reduction treatment and obtains the second class impact factor；
Display module, for being shown based on the second class impact factor to waving property of data.
14. display systems as claimed in claim 13, which is characterized in that the dimensionality reduction module, comprising:
Submodule is generated, for selecting the first impact factor and first impact factor to relate in information record sheet from described wave
And transmission line of electricity related data, generate analytical data；
Dimensionality reduction submodule, for carrying out dimensionality reduction to first kind impact factor using Principal Component Analysis based on the analytical data
Processing obtains the second class impact factor.
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| CN106203839A (en) * | 2016-07-13 | 2016-12-07 | 国网湖南省电力公司 | Transmission line galloping affects key factor discrimination method and system |
| CN107239857A (en) * | 2017-05-31 | 2017-10-10 | 武汉大学 | Overhead transmission line methods of risk assessment based on LS_SVM and PCA |
| CN107436162A (en) * | 2017-07-31 | 2017-12-05 | 国网湖南省电力公司 | A kind of power network line waves Occurrence forecast method and system |
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|---|---|---|---|---|
| WO2016033883A1 (en) * | 2014-09-04 | 2016-03-10 | 国家电网公司 | Power transmission line gallop risk early-warning method based on adaboost |
| CN106203839A (en) * | 2016-07-13 | 2016-12-07 | 国网湖南省电力公司 | Transmission line galloping affects key factor discrimination method and system |
| CN107239857A (en) * | 2017-05-31 | 2017-10-10 | 武汉大学 | Overhead transmission line methods of risk assessment based on LS_SVM and PCA |
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