CN110189031A - A kind of power distribution network diagnosis index classification method based on regression analysis on factors - Google Patents
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Abstract
The power distribution network diagnosis index classification method based on regression analysis on factors that the invention discloses a kind of, comprising the following steps: the power distribution network index of selection is divided into destination layer, main indicator layer, sub- indicator layer with analytic hierarchy process (AHP)；Factorial analysis is carried out to diagnosis of the status quo index sample data and obtains the common factor of diagnosis of the status quo index；Multiple regression analysis is carried out, Collinearity Diagnosis Analysis is carried out, deletes redundancy index.The mentioned method of the present invention can quickly and accurately find out the critical index for influencing power distribution network diagnostic analysis, and can be realized the classification of power distribution network critical index, help to improve diagnostic analysis efficiency and accuracy.
Description
Technical field
The present invention relates to distribution network planning technical field, especially a kind of power distribution network diagnosis based on regression analysis on factors refers to
Mark classification method.
Background technique
Power distribution network diagnosis of the status quo assessment during, can be collected into a large amount of achievement datas relevant to assessing and directly
Operation data.Include a large amount of information in these data, can comprehensively reflect what various indexs assessed status power grid
Effect, it can be seen that in most cases, obtained pointer type is complicated, there is certain correlation between each other, to commenting
The percentage contribution for estimating work is also not quite similar.Also, the different index of some assessment objects is practical, and there is no embody to influence distribution
The different factors of net diagnosis of the status quo, but power distribution network circumstantial impact factor is in the performance of different aspect.Therefore, these are directly used
The index being collected into will affect the precision of assessment models, have ignored the importance of data mining and processing.
Summary of the invention
The power distribution network diagnosis index classification method based on regression analysis on factors that the object of the present invention is to provide a kind of, can be fast
Speed, the classification for being accurately realized power distribution network index facilitate power distribution network diagnostic analysis personnel and carry out diagnostic analysis work.
To achieve the above object, the present invention adopts the following technical solutions:
The power distribution network diagnosis index classification method based on regression analysis on factors that the present invention provides a kind of, including following step
It is rapid:
The power distribution network index of selection is divided into destination layer, main indicator layer, sub- indicator layer with analytic hierarchy process (AHP)；
Factorial analysis is carried out to diagnosis of the status quo index sample data and obtains the common factor of diagnosis of the status quo index；
Multiple regression analysis is carried out, Collinearity Diagnosis Analysis is carried out, deletes redundancy index.
Further, described that the power distribution network index of selection is divided into destination layer, main indicator layer, son with analytic hierarchy process (AHP)
Further include index normalized step before indicator layer step:
The number of samples of power distribution network diagnosis of the status quo assessment is q, and index number is m, if the collection of profit evaluation model and cost type index
It is combined into V1And V2, the Max-Min normalization result of j-th of index value of i-th of sample are as follows:
Wherein, xijAnd xij' be respectively Communalities before and after index value；It is attainable for index mapping normalization
Maximum value,Attainable minimum value is normalized for index mapping, under normal circumstances
Further, after the index normalized step, further includes: obtain power distribution network diagnostic assessment Communalities
Index value after, before carrying out factorial analysis, need the index value x of normalized againij":
Further, described that the power distribution network index of selection is divided into destination layer, main indicator layer, son with analytic hierarchy process (AHP)
Indicator layer specifically includes:
The destination layer is power distribution network diagnosis of the status quo evaluation index system；The main indicator layer includes power supply quality, electricity
Web frame, equipment and power supply capacity；The sub- indicator layer includes three-phase imbalance platform area accounting, rate of qualified voltage, voltage
Out-of-limit user's accounting, user's annual frequency of power cut, user's annual power off time, single line or monotropic accounting, main transformer N-1 number of units
Percent of pass, route N-1 item number percent of pass, distribution line contact rate, transferable load accounting, radius of electricity supply, the route time limit are more than
20 years accountings, the main transformer time limit be more than 20 years accountings, standardization distribution transforming accounting, power distribution automation coverage rate, intelligent electric meter coverage rate,
Intelligent patrol detection coverage rate, ratio of transformer capacity to load, main transformer overload number of units accounting, circuit overload item number accounting, per family capacity of distribution transform.
Further, described that the public of diagnosis of the status quo index is obtained to the progress factorial analysis of diagnosis of the status quo index sample data
The factor specifically includes:
Statistics control calculating is carried out to the standardized data of q sample of m index after standardization；
Sample correlation matrix R and its characteristic value are calculated, and carries out descending sort；
Selection is so that accumulative variance contribution ratio is greater than the preceding k index of the first setting value；
Seek the feature vector of k characteristic value；
Calculate Factor load-matrix A；
Export the classification results of m index of diagnosis of the status quo.
Further, the standardized data to q sample of m index after standardization carries out statistics control
It calculates, specifically includes:
Judge whether statistics control evaluation is greater than the second setting value, be, carries out next step；It is no, more new samples
Data recalculate the statistics control evaluation of sample after increasing sample size.
Further, the calculating Factor load-matrix A, specifically includes:
Whether judge index load value is less than third given threshold, is, using variance maximum rotary process, is rotating index
It projects on common factor axis afterwards and produces a polarization to minimax, calculate Factor load-matrix A；It is no, it will be greater than third setting threshold
The load value a of valueij' it is included into j-th of classification.
The effect provided in summary of the invention is only the effect of embodiment, rather than invents all whole effects, above-mentioned
A technical solution in technical solution have the following advantages that or the utility model has the advantages that
Method of the invention can quickly and accurately find out the critical index for influencing power distribution network diagnostic analysis, and can
The classification for realizing power distribution network critical index, helps to improve diagnostic analysis efficiency and accuracy.
Detailed description of the invention
Fig. 1 is present invention method flow diagram；
Fig. 2 is step S2 method flow diagram of the embodiment of the present invention；
Fig. 3 is the index set schematic diagram after screening of the embodiment of the present invention.
Specific embodiment
In order to clarify the technical characteristics of the invention, below by specific embodiment, and its attached drawing is combined, to this hair
It is bright to be described in detail.Following disclosure provides many different embodiments or example is used to realize different knots of the invention
Structure.In order to simplify disclosure of the invention, hereinafter the component of specific examples and setting are described.In addition, the present invention can be with
Repeat reference numerals and/or letter in different examples.This repetition is that for purposes of simplicity and clarity, itself is not indicated
Relationship between various embodiments and/or setting is discussed.It should be noted that illustrated component is not necessarily to scale in the accompanying drawings
It draws.Present invention omits the descriptions to known assemblies and treatment technology and process to avoid the present invention is unnecessarily limiting.
As described in Figure 1, the power distribution network diagnosis index classification method based on regression analysis on factors, comprising the following steps:
S1, the power distribution network index of selection is divided into destination layer, main indicator layer, sub- indicator layer with analytic hierarchy process (AHP)；
S2, the common factor of diagnosis of the status quo index is obtained to the progress factorial analysis of diagnosis of the status quo index sample data；
S3, multiple regression analysis is carried out, carries out Collinearity Diagnosis Analysis, delete redundancy index.
The power distribution network index of selection is divided into destination layer, main indicator layer, sub- indicator layer with analytic hierarchy process (AHP) by step S1
Further include index normalized step before step:
The number of samples of power distribution network diagnosis of the status quo assessment is q, and index number is m, if the collection of profit evaluation model and cost type index
It is combined into V1And V2, the Max-Min normalization result of j-th of index value of i-th of sample are as follows:
Wherein, xijAnd xij' be respectively Communalities before and after index value；It is attainable for index mapping normalization
Maximum value,Attainable minimum value is normalized for index mapping, under normal circumstances
After step index normalized step, further includes: obtain the index value of power distribution network diagnostic assessment Communalities
Afterwards, before carrying out factorial analysis, the index value x of normalized is needed againij":
In step S1, the power distribution network index of selection is divided into destination layer, main indicator layer, sub- index with analytic hierarchy process (AHP)
Layer, specifically includes: selected power distribution network index is divided into destination layer, main indicator layer, sub- indicator layer.Wherein each route, master
110,35,10,0.38kV4 kind voltage class can be divided by becoming index of correlation.As shown in table 1, destination layer is power distribution network diagnosis of the status quo
Evaluation index system；The main indicator layer includes power supply quality, electric network composition, equipment and power supply capacity；The son refers to
Mark floor includes three-phase imbalance platform area accounting, rate of qualified voltage, voltage out-of-limit user accounting, user's annual frequency of power cut, uses
Family annual power off time, single line or monotropic accounting, main transformer N-1 number of units percent of pass, route N-1 item number percent of pass, distribution line
It is more than 20 years accountings, standards that contact rate, transferable load accounting, radius of electricity supply, the route time limit, which are more than 20 years accountings, the main transformer time limit,
Change distribution transforming accounting, power distribution automation coverage rate, intelligent electric meter coverage rate, intelligent patrol detection coverage rate, ratio of transformer capacity to load, main transformer overload
Number of units accounting, circuit overload item number accounting, per family capacity of distribution transform.
Meet it is assumed that p ties up random distribution indicator vector
It is q n-dimensional random variable n, q≤p meetsIts component fiIt is referred to as public
The factor works to each component of X.It is that p ties up unobservable random vector, meets
AndThe component of eReferred to as specific factor, it is only to the component of XIt works.
μ and A is parameter matrix.If X meets above formula, claim random vector X that there is factor structure.At this moment, it is easy to calculate
Matrix A is known as factor loading, element aijIt is i-th of componentIn j-th of factor fjOn load.NoteThen have
It can be seen thatReflect common factor pairInfluence, referred to as common factor pair" contribution ".When
When, distinguish common factor pairInfluence be greater than specific factorInfluence, it is also seen thatReflect componentTo public
Factor fiDegree of dependence.
On the other hand, to a specified common factor fi, noteReferred to as common factor fiContribution to X.
Value it is bigger, reflect common factor fiInfluence to X is also bigger, soIt is a ruler for measuring common factor importance
Degree.
As shown in Fig. 2, carrying out factorial analysis in step S2 to diagnosis of the status quo index sample data and obtaining diagnosis of the status quo index
Common factor, specifically include:
Statistics control calculating is carried out to the standardized data of q sample of m index after standardization, judges to count
Whether amount checking computation numerical value is greater than the second setting value, is, carries out next step；It is no, sample data is updated, sample size is increased
In the statistics control evaluation one embodiment for recalculating sample afterwards, the second setting value selection 0.7.
After obtaining power distribution network diagnostic assessment standardized index, by being carried out to power distribution network diagnosis of the status quo index sample data
KMO (Kaiser Meyer Olkin) statistics control.KMO is for the simple correlation and partial correlation coefficient between comparison variable
Index, when the simple correlation coefficient quadratic sum between all variables is much larger than partial correlation coefficient quadratic sum, value closer to 1,
Correlation i.e. between variable is stronger, and original variable is more suitable for carrying out factorial analysis.When KMO value is greater than 0.8, expression is suitable for
Carry out factorial analysis.To power distribution network diagnosis of the status quo index sample carry out factorial analysis can be obtained diagnosis of the status quo index it is public because
Son, these factors include evaluation index similar in multiple relationships, thus can be classified to diagnosis of the status quo index.
Sample correlation matrix R and its characteristic value are calculated, and carries out descending sort；
Selection is so that accumulative variance contribution ratio is greater than the preceding k index of the first setting value；In one embodiment, the first setting
Value selection 0.85.
Seek the feature vector of k characteristic value；
Factor load-matrix A is calculated, whether judge index load value is less than third given threshold, is, using variance maximum
Rotary process makes to project on the common factor axis of index after rotation and produce a polarization to minimax, calculates Factor load-matrix A；
It is no, it will be greater than the load value a of third given thresholdij' it is included into j-th of classification；
Export the classification results of m index of diagnosis of the status quo.
Present invention method is further described below by specific example.
The common factor of diagnosis of the status quo index can be obtained by carrying out factorial analysis to diagnosis of the status quo index sample data, this
A little factors include evaluation index similar in multiple relationships, thus can be classified to diagnosis of the status quo index.By can be calculated, sample
This KMO metric is 0.815, greater than required 0.7, shows that diagnosis of the status quo index is appropriate for factorial analysis.Factorial analysis
Communality and factor contribution rate analytical table are as described in table 2 and table 3.
2 factorial analysis communality of table
3 factor contribution rate of table
It is calculated loading matrix, resulting 5 common factors have higher load in multiple indexs, but part
The load value of index is less than the threshold value a of settingt, cannot classify to it.Using variance maximum rotary process, rotating index
It projects on common factor axis afterwards and produces a polarization to minimax, postrotational factor loading is as shown in table 4.As shown in Table 4, often
The corresponding loading coefficient of one and only one common factor is greater than given threshold 0.8 in a index, thus obtains under 5 classification
Power distribution network diagnosis of the status quo index.After classification, wherein 11 indexs describe electric network fault operational support rate；6 indexs describe
Equip heavy-overload situation；7 indexs describe power supply reliability and platform area situation；7 indexs describe equipment oldization situation；
6 indexs describe rack standardization and automation situation.
The orthogonal rotation postfactor load of table 4
Then multiple regression analysis is carried out, Collinearity Diagnosis Analysis is carried out, to delete redundancy index.This example p is 90, is taken significant
Horizontal α is 0.05, and by taking the first classified adaptive factor as an example, critical value t can be obtained by tabling look-up0.025,80=1.990.Calculate to obtain the first t to classify
Test statistics is as shown in table 5.
5 t of table examines the selection result
Index set after screening is as shown in Figure 3.Pass through principal component analysis and redundancy in electric network fault operational support rate
It rejects, eliminates 3 indexs of 35kV, 110kV ratio of transformer capacity to load and 0.38kV radius of electricity supply.There is not index to delete in equipment heavy-overload
It removes.Power supply reliability and platform area situation eliminate 1 index of average frequency of power cut of user.It equips oldization situation and eliminates 10kV
Distribution transforming is more than 20 years accountings, 1 index.Rack standardization eliminates 1 index of intelligent electric meter coverage rate with automation situation.
Above-mentioned, although the foregoing specific embodiments of the present invention is described with reference to the accompanying drawings, not protects model to the present invention
The limitation enclosed, those skilled in the art should understand that, based on the technical solutions of the present invention, those skilled in the art are not
Need to make the creative labor the various modifications or changes that can be made still within protection scope of the present invention.
Claims (7)
1. a kind of power distribution network diagnosis index classification method based on regression analysis on factors, characterized in that the following steps are included:
The power distribution network index of selection is divided into destination layer, main indicator layer, sub- indicator layer with analytic hierarchy process (AHP)；
Factorial analysis is carried out to diagnosis of the status quo index sample data and obtains the common factor of diagnosis of the status quo index；
Multiple regression analysis is carried out, Collinearity Diagnosis Analysis is carried out, deletes redundancy index.
2. the power distribution network diagnosis index classification method based on regression analysis on factors as described in claim 1, characterized in that described
Before the power distribution network index of selection is divided into destination layer, main indicator layer, sub- indicator layer step with analytic hierarchy process (AHP), further include
Index normalized step:
The number of samples of power distribution network diagnosis of the status quo assessment is q, and index number is m, if profit evaluation model and the collection of cost type index are combined into V1
And V2, the Max-Min normalization result of j-th of index value of i-th of sample are as follows:
Wherein, xijAnd xij' be respectively Communalities before and after index value；Attainable maximum is normalized for index mapping
Value,Attainable minimum value is normalized for index mapping, under normal circumstances
3. the power distribution network diagnosis index classification method based on regression analysis on factors as claimed in claim 2, characterized in that described
After index normalized step, further includes: after obtaining the index value of power distribution network diagnostic assessment Communalities, carrying out the factor
Before analysis, the index value x of normalized is needed againij":
4. the power distribution network diagnosis index classification method based on regression analysis on factors as claimed in claim 3, characterized in that described
The power distribution network index of selection is divided into destination layer, main indicator layer, sub- indicator layer with analytic hierarchy process (AHP), is specifically included:
The destination layer is power distribution network diagnosis of the status quo evaluation index system；The main indicator layer includes power supply quality, power grid knot
Structure, equipment and power supply capacity；The sub- indicator layer includes three-phase imbalance platform area accounting, rate of qualified voltage, voltage out-of-limit
User's accounting, user's annual frequency of power cut, user's annual power off time, single line or monotropic accounting, main transformer N-1 number of units pass through
Rate, route N-1 item number percent of pass, distribution line contact rate, transferable load accounting, radius of electricity supply, the route time limit are more than 20 years
Accounting, the main transformer time limit are more than 20 years accountings, standardization distribution transforming accounting, power distribution automation coverage rate, intelligent electric meter coverage rate, intelligence
Inspection coverage rate, ratio of transformer capacity to load, main transformer overload number of units accounting, circuit overload item number accounting, per family capacity of distribution transform.
5. the power distribution network diagnosis index classification method based on regression analysis on factors as claimed in claim 4, characterized in that described
Factorial analysis is carried out to diagnosis of the status quo index sample data and obtains the common factor of diagnosis of the status quo index, is specifically included:
Statistics control calculating is carried out to the standardized data of q sample of m index after standardization；
Sample correlation matrix R and its characteristic value are calculated, and carries out descending sort；
Selection is so that accumulative variance contribution ratio is greater than the preceding k index of the first setting value；
Seek the feature vector of k characteristic value；
Calculate Factor load-matrix A；
Export the classification results of m index of diagnosis of the status quo.
6. the power distribution network diagnosis index classification method based on regression analysis on factors as claimed in claim 5, characterized in that described
Statistics control calculating is carried out to the standardized data of q sample of m index after standardization, is specifically included:
Judge whether statistics control evaluation is greater than the second setting value, be, carries out next step；It is no, sample data is updated,
The statistics control evaluation of sample is recalculated after increase sample size.
7. the power distribution network diagnosis index classification method based on regression analysis on factors as claimed in claim 5, characterized in that described
Factor load-matrix A is calculated, is specifically included:
Whether judge index load value is less than third given threshold, is, using variance maximum rotary process, makes index after rotation
It projects on common factor axis and produces a polarization to minimax, calculate Factor load-matrix A；It is no, it will be greater than third given threshold
Load value aij' it is included into j-th of classification.
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| CN111579941B (en) * | 2020-05-11 | 2021-05-18 | 西南交通大学 | Online steel rail insulation defect diagnosis method based on all-line steel rail potential feature extraction |
| CN113743705A (en) * | 2021-03-04 | 2021-12-03 | 江苏安方电力科技有限公司 | Factor regression analysis-based storage, inspection and distribution integrated diagnostic index classification and screening method |
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