CN103632306A - Distribution network power supply area division method based on clustering analysis - Google Patents
Distribution network power supply area division method based on clustering analysis Download PDFInfo
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Abstract
The invention relates to a distribution network power supply area division method based on clustering analysis. The method comprises the following steps: first, a power supply area to be divided is selected, the status of the power supply area and main factors influencing power supply area division are analyzed and an evaluation index system for power supply area division is established; then, distribution of status indexes and planning indexes of the power supply area is analyzed and an appropriate method is chosen to process data to enable the processed data to basically obey normal distribution; next, SAS software is adopted and an appropriate method is selected to make a clustering analysis on sample data of each index respectively or make a clustering analysis on a plurality of indexes to enable a clustering result to basically obey normal distribution; and finally, a division criterion for the power supply area is established according to a clustering analysis result. The method has high adaptability, can be applicable to division of power supply areas of different scales in different areas and can lay a foundation for formulation of differentiated distribution network planning design technology guide rules and related technical standards.
Description
Technical field
The method that the present invention relates to distribution system planning, is specifically related to a kind of power distribution network division of the power supply area method based on cluster analysis.
Background technology
China is vast in territory, and power supply area is vast, and various places socio-economic development situation and electrical network feature difference are obvious.If build power distribution network according to a unified standard, can cause the situation of the not high even serious waste of asset of equipments utilization factor, in technology, unreasonable economically.Therefore, in order to embody regional disparity and distribution network planning, the otherness of building, realize the lean management of power distribution network, improve efficiency and the level of distribution network planning work, promote China's distribution network planning to standardization, Scientific Development, should run through all the time " differentiation planning " theory, in the situation that considering that Regional Economic social development presents non-equilibrium, in conjunction with each department power distribution network present situation and development trend, take into account the differentiation feature of different regions different stages of development, China's power distribution network power supply area is divided, for the distribution network planning of differentiation and the foundation of typical powering mode lay the foundation.
(1) clustering methodology:
Cluster analysis is the reason according to " things of a kind come together, people of a mind fall into the same group ", a kind of Multielement statistical analysis method that sample or index are classified, its discussion to as if a large amount of samples, requirement can reasonably reasonably be classified by characteristic separately, can be for reference or follow without any pattern, be not carry out in the situation that there is no priori.
Its basic thought is according to the method for the characteristic research individual segregation of things itself;
Cluster principle is that the individuality in same class has larger similarity, and the individual difference in inhomogeneity is very large.
Base program is a plurality of observation indexs according to a batch sample, finds out particularly the statistic that some can measure similarity degree between sample or index, then utilizes statistic that sample or index are sorted out.
1. the classification of clustering methodology:
The content of cluster analysis is very abundant, can be divided into following several by the method for its cluster:
(1) hierarchical clustering method: start each object and constitute a class by itself, then two the most similar classes are merged at every turn, recalculate distance or the proximity of new class and other classes after merging and estimate.This process continues always until all objects are classified as a class.And the process of class can be described with a pedigree dendrogram.
(2) dynamic clustering method: first to n object preliminary classification, then according to the as far as possible little principle of the loss function of classification, classification is adjusted, until classification rationally.
(3) Means of Clustering Ordered Sample: start to regard all samples as a class, then according to certain optiaml ciriterion, they are divided into two classes, three classes, till being divided into required K class always.This method is applicable to the classification problem of Ordered Sample, also referred to as the clustering procedure of Ordered Sample.
(4) fuzzy clustering algorithm: utilize fuzzy set theory to carry out treatment classification problem, it has obvious classifying quality to having binary states data or the polymorphic data of fuzzy behaviour in economic field.
(5) graph theory clustering method: utilize the concept of Minimal Spanning Tree in graph theory to carry out treatment classification problem, created the method for unique style.
(6) cluster method of prediction: utilize clustering method to process forecasting problem, in multivariate statistical analysis, can be used to do the method forecast a lot, as regretional analysis or discriminatory analysis.But to some abnormal datas, as the forecast of the diastrous weather in meteorology, the effect that recurrence or discriminatory analysis are processed is all bad, and cluster forecast has made up this deficiency, and this is the method for a significant.
In these clustering methods, hierarchical clustering method is in real work, to use maximum class methods at present.It is by class by changeable to a kind of few method.
2. hierarchical clustering method:
Be provided with n sample, each sample records m item index.The basic thought of Hierarchical Clustering method is: first define the distance of sample room and the distance between class and class.At the beginning n sample constituted a class by itself separately, at this moment the distance of the distance between class and sample room is of equal value; Then two nearest classes are merged, and calculate the between class distance of new class and other classes, then by minimum distance criterion class.Each like this class of dwindling, knows all samples all and till becoming a class.This and class process can be expressed with pedigree dendrogram.
By the basic thought of above hierarchical clustering method, can show that its basic step is as follows:
(1) each sample is regarded as to a class (at this moment having n class);
(2) classification to j step (comprising j=0), calculates the distance between all kinds of and class, and two classes that combined distance is nearest, to obtain the classification of j+1 step;
(3) by j=0, started, repeating step (2), until all samples is classified as a class.
(4) make dendrogram.
Because class can have different definition methods from the distance between class, just produced different coefficient clustering procedures, introduce wherein several below:
(1) bee-line method: the distance definition between class and class is at a distance of the distance of nearest two sample rooms in two classes.Due to bee-line method be with between two classes recently the distance of sample point gather, so the method is not suitable for that separation is obtained to very poor colony and carries out cluster.
(2) longest distance method: the distance between definition class and class is at a distance of the distance of two sample rooms farthest in two classes.Longest distance method is easily by seriously distortion of exceptional value, and an effective method is to carry out cluster after these exceptional values are taken out separately again.
(3) intermediate distance method: the distance between class and class is neither got the distance of the nearest sample room of two classes, does not get the two classes distance of sample room farthest yet, but gets the distance in the middle of both.
(4) the class method of average: the square distance that defines two classes equals squared-distance average between any two of element in two classes.The class method of average has been utilized the information between all samples preferably, and it is considered to a kind of reasonable hierarchical clustering method under many circumstances, applies more extensive.
(5) gravity model appoach: the distance definition between class and class is the distance between two class centers of gravity (sample mean in class).Compare with other system clustering procedure, gravity model appoach is more sane aspect processing exceptional value, but aspect other, is generally being not so good as the effective of the class method of average or sum of squares of deviations method.
(6) sum of squares of deviations method (Ward method): first n sample respectively constituted a class by itself, each and fall a class during cluster, and select to make the summation of sum of squares of deviations in class to increase by two minimum classes, merged, until all samples are classified as a class.Sum of squares of deviations method makes two large classes tend to larger distance, thereby is difficult for merging; On the contrary, two little classes are but because tending to be easy to merging compared with little distance.This often meets our actual requirement to cluster.
3. the class method of average:
The class method of average adopt two class samples between any two mean distance square average as the distance between class,
Wherein: d
ijrepresent sample X
(i)and X
(j)between distance; D
pqrepresentation class G
pand G
qbetween distance.
As certain step class G
pand G
qmerge into G
r: G
r={ G
p, G
q, and n
r=n
p+ n
q, G
rwith other class G
kthe recursion formula of distance is:
The class method of average is that a kind of use is relatively more extensive, the good method of Clustering Effect.This report adopt the method to carry out Hierarchical Clustering.
(2) data processing method
The sample data amount of considering each index is large, gap or discreteness between data are larger, has sometimes abnormal data, need to carry out pre-service or conversion to data.Data processing method is by raw data is converted, and the discreteness of the data after conversion is reduced, and obeys or approach normal distribution, and the size variation of average is restricted, and square extent reaches unanimity.Conventional data transfer device is as follows:
1. square root conversion (square root transformation)
This method is applicable to each group all has the data of certain proportionate relationship between side and its average, is particularly useful for being totally the data of Poisson distribution.The method of conversion is to obtain the square root of former data
if promising 0 number or most observed reading are less than 10 in former observed reading, former data transformation is become
for stable all sides, conversion is conducive to meet the requirement of normality.
2. pair number conversion (logarithmic transformation)
If it is substantially proportional respectively to organize the standard deviation of data and its average, or effect is multiplicity or non-addivitity, by former data transformation, is logarithm logx(or lnx) after, can make variance become relatively more consistent and make effect become additivity by multiplicity.
If former data include 0, can adopt log(x+1) conversion method.
Generally speaking, stronger than square root conversion for the effect that weakens large parameter to number conversion.For example to do square root conversion be 1,3.16,10 to parameter 1,10,100, and doing is 0,1,2 to number conversion.
3. inverse sine conversion (arcsine transformation)
Inverse sine conversion also claims angular transition.This method is applicable to obey the data of binomial distribution.The method of conversion is to obtain the inverse sine of each former data (with percentage or fractional representation)
the angle of the numerical value Shi Yiduwei unit after conversion.
(3) SAS software:
Statistics Analysis System(SAS) system is large-scale integrated software system, has complete data access, management, analyzes and presents and application and development function.In data processing and statistical study field, SAS system has become international standard software system.
Module for Hierarchical Clustering in SAS system is CLUSTER(Hierarchical Clustering) process, this process adopts respectively 11 kinds of methods to carry out genealogical classification to the observation of SAS data centralization.CLUSTER process is the last output data set that generates a record class process, and the information that TREE process provides according to this data set draws pedigree dendrogram (dendrogram), or presses the classification number output cluster result of the option appointment of TREE process.
Summary of the invention
For the deficiencies in the prior art, the object of this invention is to provide a kind of power distribution network division of the power supply area method based on cluster analysis, the method has overcome existing method and has only leaned on the shortcoming of expert or leading experience qualitative classification, from affecting the factor of power distribution network division of the power supply area, clustering methodology in abundant application mathematical statistics and ripe business software SAS, great amount of samples data analysis is calculated, increase substantially the actual operability of the method, there is very high engineering using value; This method has higher adaptability, applicable to the division of different regions, different scales power supply area, can lay the foundation for the formulation of the distribution network planning design of differentiation and correlation technique guide rule, standard.
The object of the invention is to adopt following technical proposals to realize:
The invention provides a kind of power distribution network division of the power supply area method based on cluster analysis, its improvements are, described method comprises the steps:
(1) analyze power distribution network division of the power supply area mode and criteria for classifying present situation;
(2) selected power supply area scope of dividing, analyzes the factor of its Actualities and influence division of the power supply area;
(3) set up the assessment indicator system of division of the power supply area;
(4) analyze the distribution situation of each present situation index of power supply area and planning index;
(5) achievement data is processed;
(6) data of each index are carried out to cluster analysis, check whether cluster analysis result meets normal distribution;
(7) set up the criteria for classifying of power supply area.
Further, in described step (2), described power supply area area is not less than 5km
2(according to the company standard Q/GDW738-2012 < < of State Grid Corporation of China distribution network planning designing technique guide rule > >).
Further, in described step (3), according to the factor that affects division of the power supply area in step (2), from politics, economy and load three aspects:, set up first the assessment indicator system of division of the power supply area, wherein:
Politics index comprises administrative grade;
Economic target comprise a year GDP per capita, year per capita social power consumption, year domestic load and per GDP power consumption per capita;
Load index comprises load density, load character and load priority.
Further, in described step (4), the distribution situation of analyzing power supply area quantifiable indicator comprises: year GDP per capita, year per capita social power consumption, year domestic load, per GDP power consumption and load density per capita.
Further, in described step (5), select square root conversion, number conversion or inverse sine transformation approach are processed achievement data, make the achievement data Normal Distribution after processing.
Further, in described step (6), set power supply area type number, use standard software system SRS software, select coefficient clustering procedure to carry out respectively cluster analysis or two or more indexs are carried out to cluster analysis together each achievement data, check whether cluster analysis result meets normal distribution: if export and analyze division of the power supply area result; Otherwise, adopt the additive method of coefficient clustering procedure to re-start cluster analysis, until cluster analysis result Normal Distribution.
Further, in described step (7), according to cluster analysis result, set up power supply area the criteria for classifying (such as, when the span of each index is how many times, just can be divided into category-A power supply area), title and the number of power supply area type are set according to actual conditions.
The formed achievement in research of application the present invention has been brought in the company standard Q/GDW738-2012 < < of State Grid Corporation of China distribution network planning designing technique guide rule > >.
Compared with the prior art, the beneficial effect that the present invention reaches is:
1. this method is from affecting the factor of power distribution network division of the power supply area, clustering methodology in abundant application mathematical statistics and ripe business software SAS, great amount of samples data analysis is calculated, increased substantially the actual operability of the method, there is very high engineering using value.
2. this method has taken into full account the principal element that affects division of the power supply area, has set up the index system of division of the power supply area from politics, economy, load three aspects:, can make the division of power supply area more economically, rationally, evidence-based.
3. this method depends on a large amount of sample datas, and sample data amount is larger, and division of the power supply area result is just more accurate, and can to the great amount of samples data of several indexs, carry out cluster analysis simultaneously, makes division of the power supply area result more credible;
4. this method has higher adaptability, has both been applicable to national grid company, provincial Utilities Electric Co., is also applicable to city-level electric company, can lay the foundation for the formulation of the distribution network planning design of differentiation and correlation technique guide rule, standard.
Accompanying drawing explanation
Fig. 1 is the power distribution network division of the power supply area process flow diagram based on cluster analysis provided by the invention;
Fig. 2 is division of the power supply area result provided by the invention and sample distribution situation map.
Embodiment
Below in conjunction with accompanying drawing, the specific embodiment of the present invention is described in further detail.
Power distribution network division of the power supply area process flow diagram based on cluster analysis provided by the invention as shown in Figure 1, the present invention is used for power distribution network power supply area reasonably to divide, a set of complete assessment indicator system is proposed, by the clustering method in application mathematical statistics, to great amount of samples data analysis, set up practical division of the power supply area standard and division methods.The first selected power supply area scope that will divide of the method, analyzes the principal element of its Actualities and influence division of the power supply area, sets up the assessment indicator system of division of the power supply area; Analyze afterwards the distribution situation of this each quantifiable indicator of power supply area, and select suitable method to process data, make the basic Normal Distribution of data after processing; Then, use SAS software, select suitable method to carry out respectively cluster analysis or several indexs are carried out to cluster analysis together the sample data of each index, make the basic Normal Distribution of cluster result; Finally, according to cluster analysis result, set up the criteria for classifying of this power supply area.
Specifically comprise the steps:
(1) investigate existing power distribution network division of the power supply area method and criteria for classifying present situation, analyze the subject matter existing at present;
(2) the selected power supply area scope that will divide, analyzes the principal element of its Actualities and influence division of the power supply area;
(3) multianalysis affects the principal element of division of the power supply area, sets up the assessment indicator system of division of the power supply area from politics, economy and 3 aspects of loading, and wherein political index comprises administrative grade; Economic target comprise a year GDP per capita, year per capita social power consumption, year domestic load and per GDP power consumption per capita; Load index comprises load density, load character and load priority;
(4) analyze the distribution situation of this each quantifiable indicator of power supply area, these indexs comprise a year GDP per capita, year per capita social power consumption, year domestic load, per GDP power consumption and load density per capita;
(5) according to the distribution situation of each achievement data, select suitable data processing method to process data, make the basic Normal Distribution of data after processing; Frequently-used data disposal route comprises square root conversion, to number conversion and inverse sine conversion etc.
(6) use SAS software, select coefficient clustering procedure, as the class method of average is carried out respectively cluster analysis or several indexs are carried out to cluster analysis together the sample data of each index, check whether cluster analysis result meets normal distribution, if so, export and analyze division of the power supply area result; Otherwise, adopt other coefficient clustering methods to re-start cluster analysis, until the basic Normal Distribution of cluster result;
(7), according to cluster analysis result, set up the criteria for classifying of this power supply area.
Embodiment
Below in conjunction with specific embodiment, the present invention is done further and described in detail.
(1) division of the power supply area index system:
This method takes into full account the factor that affects division of the power supply area, mainly from politics, economic, load three aspects:, determines the index system of division of the power supply area, so that the division that makes power supply area is more economically, rationally, evidence-based, refers to table 1:
The index system of table 1 division of the power supply area
(2) cluster analysis of China 342Ge districts and cities index:
For the year GDP per capita of the index ，Dui China 342Ge districts and cities that list in table 1, year, domestic load and 4 indexs of load density are added up respectively per capita social power consumption, year per capita.Wherein:
<1> GDP per capita index has 339 samples;
<2> per capita social index on power consumption has 490 samples;
<3> per capita household electricity figureofmerit has 484 samples;
<4> service area present situation load density target has 510 samples;
<5> service area planning load density target has 524 samples.
By 4 indexs to China 342Ge districts and cities, carry out cluster analysis, and after rounding, by division of the power supply area, be A+, A, B+, B, C+, C, D+, D totally 8 classes, 4 indexs sample distribution situation in different power supply area types is as shown in Fig. 2 and table 2.From distribution results:
1) most of sample distribution is in B～D+ power supply area, and the sample size in the power supply area of two is less, if with after smooth curve matching or translation, substantially approach normal distribution, illustrates that cluster analysis result is rational;
2) with year per capita social index on power consumption compare, year GDP per capita, year, the distribution situation of domestic load and load density target was better per capita, raw sample data itself was described better or better after treatment;
3) criteria for classifying of planning load density is apparently higher than present situation load density, and present situation load density is lower, and load growth is faster;
4), if think that according to power distribution network actual conditions the type of division of the power supply area is too many, can reduce power supply area type number by merging:
If be 1. 4 classes by division of the power supply area, need to re-start cluster analysis or adjacent power supply area type is merged between two, be about to A+ and A and be merged into category-A power supply area, by that analogy, refer to table 3;
If be 2. 5 classes by division of the power supply area, need to re-start cluster analysis, refer to table 4.
Table 2 division of the power supply area result and sample distribution situation
Table 3 power supply area is divided into the division result of four classes
Table 4 power supply area is divided into the division result of five classes
(2) explanation of method and expansion
In the document of publishing at present, still rarely seen to can power distribution network division of the power supply area method studying, Most scholars is all that transformer station's division of the power supply area and optimal power radius are studied.From existing research, the domestic method that power distribution network division of the power supply area be there is no to quantitative calculation and analysis, major part is the classification that expert or leader rely on experience in quality, is difficult to the good with bad of judgement division of the power supply area.Method provided by the invention relies on classical mathematical statistics method, applies ripe professional software, and a large amount of sample datas is analyzed, and makes division of the power supply area result evidence-based.
In addition, power supply area type division result and power supply area range size to be divided are closely related, if power supply area scope is excessive, can cause the type in some plot in region on the low side or higher, so power supply area range size should be reasonable.
The present invention has overcome existing method and has only leaned on the shortcoming of expert or leading experience qualitative classification, from affecting the factor of power distribution network division of the power supply area, clustering methodology in abundant application mathematical statistics and ripe business software SAS, great amount of samples data analysis is calculated, increase substantially the actual operability of the method, there is very high engineering using value; This method has higher adaptability, applicable to the division of different regions, different scales power supply area, can lay the foundation for the distribution network planning designing technique guide rule of differentiation and the formulation of Its Relevant Technology Standards.
Finally should be noted that: above embodiment is only in order to illustrate that technical scheme of the present invention is not intended to limit, although the present invention is had been described in detail with reference to above-described embodiment, those of ordinary skill in the field are to be understood that: still can modify or be equal to replacement the specific embodiment of the present invention, and do not depart from any modification of spirit and scope of the invention or be equal to replacement, it all should be encompassed in the middle of claim scope of the present invention.
Claims (7)
1. the power distribution network division of the power supply area method based on cluster analysis, is characterized in that, described method comprises the steps:
(1) analyze power distribution network division of the power supply area mode and criteria for classifying present situation;
(2) selected power supply area scope of dividing, analyzes the factor of its Actualities and influence division of the power supply area;
(3) set up the assessment indicator system of division of the power supply area;
(4) analyze the distribution situation of each present situation index of power supply area and planning index;
(5) achievement data is processed;
(6) data of each index are carried out to cluster analysis, check whether cluster analysis result meets normal distribution;
(7) set up the criteria for classifying of power supply area.
2. power distribution network division of the power supply area method as claimed in claim 1, is characterized in that, in described step (2), described power supply area area is not less than 5km
2.
3. power distribution network division of the power supply area method as claimed in claim 1, it is characterized in that, in described step (3), according to the factor that affects division of the power supply area in step (2), from politics, economy and load three aspects:, set up first the assessment indicator system of division of the power supply area, wherein:
Politics index comprises administrative grade;
Economic target comprise a year GDP per capita, year per capita social power consumption, year domestic load and per GDP power consumption per capita;
Load index comprises load density, load character and load priority.
4. power distribution network division of the power supply area method as claimed in claim 1, it is characterized in that, in described step (4), the distribution situation of analyzing power supply area quantifiable indicator comprises: year GDP per capita, year per capita social power consumption, year domestic load, per GDP power consumption and load density per capita.
5. power distribution network division of the power supply area method as claimed in claim 1, is characterized in that, in described step (5), selects square root conversion, number conversion or inverse sine transformation approach are processed achievement data, makes the achievement data Normal Distribution after processing.
6. power distribution network division of the power supply area method as claimed in claim 1, it is characterized in that, in described step (6), set power supply area type number, use standard software system SRS software, select coefficient clustering procedure to carry out respectively cluster analysis or two or more indexs are carried out to cluster analysis together each achievement data, check whether cluster analysis result meets normal distribution: if export and analyze division of the power supply area result; Otherwise, adopt the additive method of coefficient clustering procedure to re-start cluster analysis, until cluster analysis result Normal Distribution.
7. power distribution network division of the power supply area method as claimed in claim 1, is characterized in that, in described step (7), according to cluster analysis result, sets up the criteria for classifying of power supply area, and title and the number of power supply area type are set according to actual conditions.
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