CN111737924A - Method for selecting typical load characteristic transformer substation based on multi-source data - Google Patents

Method for selecting typical load characteristic transformer substation based on multi-source data Download PDF

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CN111737924A
CN111737924A CN202010822559.8A CN202010822559A CN111737924A CN 111737924 A CN111737924 A CN 111737924A CN 202010822559 A CN202010822559 A CN 202010822559A CN 111737924 A CN111737924 A CN 111737924A
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substation
load
transformer
industry
clustering
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CN111737924B (en
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舒展
谌艳红
丁贵立
陈波
段志远
康兵
程思萌
陶翔
汪硕承
闵泽莺
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Jiangxi Electric Power Co Ltd
Nanchang Institute of Technology
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Jiangxi Electric Power Co Ltd
Nanchang Institute of Technology
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/04—Power grid distribution networks

Abstract

The invention relates to the technical field of power system load modeling, in particular to a method for selecting a typical load characteristic transformer substation based on multi-source data. The method comprises the steps of generally surveying load characteristics of a transformer substation of a target power grid under the same voltage level to obtain load characteristic data, wherein the load characteristic data comprises load type data and industry composition data; performing type clustering analysis on the transformer substations according to the load type data, and performing load classification on the transformer substations to obtain a plurality of transformer substation groups with similar load characteristics; performing industry clustering analysis on the transformer substation groups according to the industry composition data, and performing industry classification on the transformer substations; and selecting a typical transformer substation capable of representing the load characteristics according to the load classification and the industry classification. The invention applies the aggregation theory method to the selection of the typical sites, provides the basis for selecting the typical sites according to the membership relationship, and provides scientific basis for the modeler in the process of selecting the typical sites.

Description

Method for selecting typical load characteristic transformer substation based on multi-source data
Technical Field
The invention relates to the technical field of power system load modeling, in particular to a method for selecting a typical load characteristic transformer substation based on multi-source data.
Background
The load modeling work of the power system becomes a hotspot and key field of the power industry, the power system consists of a power plant, a power transmission network and a power load, the power load can be divided into industrial load, residential load, commercial load, agricultural load and other loads according to different power utilization main bodies, and in the research and application fields of the load modeling of the power system, the method for establishing the load model by the statistical synthesis method is widely used in the actual modeling work due to the advantages of clear physical model and high model precision. However, due to the characteristics of complexity, dispersion and randomness of the loads, the workload of completing detailed investigation and then integrating all the loads in the power system is too large, and the actual operability is not achieved. The comprehensive loads with load characteristics close to or similar to those of the same power grid at different load points and in different time periods are classified, and the classified load characteristics are described by using the same load model, so that the problem of workload redundancy of statistical comprehensive load modeling can be solved by selecting a typical transformer substation to replace the transformer substations with similar load characteristics to realize statistical comprehensive load modeling. The typical substation is selected according to personal experience, deviation is generated due to difference, and therefore the typical substation selection process needs to be achieved through a screening algorithm. The loads are divided into a plurality of types according to seasons, time, load levels or load composition, modeling is carried out on each type of load respectively, reasonable selection is achieved, the change of the difference of the loads is represented as continuity, and no distinct boundary exists between different objects, so that transformer substation classification has the characteristic of fuzziness, load characteristic classification and integration in the field of load modeling are necessary and feasible, and the load composition of the transformer substation can be selected as a characteristic vector to realize selection of a typical transformer substation. Industrial electricity occupies a large proportion in an electricity utilization structure of the whole society, so that load peak-valley difference is large, main electric equipment in different industries is different, so that load characteristics of different transformer substations are large, in order to further improve representativeness of a typical transformer substation, industry composition conditions in industrial loads are considered in a selection process, and the defects that a single general investigation result is random, and certain errors are caused when load classification and synthesis are carried out are overcome.
Therefore, it is necessary to provide a method for selecting a typical load characteristic substation more accurately.
Disclosure of Invention
The invention aims to at least solve one of the technical problems in the prior art and provides a method for selecting a typical load characteristic transformer substation based on multi-source data.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows: a method for selecting a typical load characteristic substation based on multi-source data comprises the following steps:
step 1, carrying out load characteristic general survey on a transformer substation of a target power grid under the same voltage level to obtain load characteristic data, wherein the load characteristic data comprises load type data and industry composition data;
step 2, performing type clustering analysis on the transformer substations according to the load type data, and performing load classification on the transformer substations to obtain a plurality of transformer substation groups with similar load characteristics;
step 3, performing industry clustering analysis on the transformer substation groups according to industry composition data, and classifying the transformer substations in industry;
and 4, selecting a typical transformer substation capable of representing the load characteristics according to the load classification and the industry classification.
Further, in the step 1, the method specifically includes:
step 1.1, dividing survey ranges of a target power grid according to cities;
step 1.2, carrying out load general survey on all transformer substations in the same voltage class in the district of the city;
step 1.3, investigating load types borne by each transformer substation, active power consumed by each load type and occupied proportion to obtain load type data, wherein the load types comprise industrial loads, residential loads, commercial loads, agricultural loads and other loads;
and step 1.4, investigating the specific industry types and the occupied proportions in the industrial loads of all the substations to obtain industry composition data.
Further, in the step 2, the method specifically includes:
step 2.1, selecting the load constitution of the transformer substation as a characteristic vector;
Figure 737118DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 873832DEST_PATH_IMAGE002
representing the load composition of the ith transformer substation, wherein n is the number of the transformer substations, and m is the number of the characteristic indexes;
step 2.2, applying a polymerization theory method to the transformer substation load type classification, and improving a fuzzy clustering algorithm by adopting genetic simulated annealing;
and 2.3, calculating the similarity among the substations through fuzzy clustering, and converting the similarity among different substation load compositions into the magnitude of the membership degree for expression, wherein the membership degree is as follows:
Figure 122411DEST_PATH_IMAGE003
in the formula (I), the compound is shown in the specification,
Figure 986462DEST_PATH_IMAGE004
is the degree of membership of the ith substation to the jth substation,
Figure 269676DEST_PATH_IMAGE005
is the jth initial cluster center and,
Figure 810247DEST_PATH_IMAGE006
is the kth initial clustering center, K is the number of clustering centers,
Figure 546122DEST_PATH_IMAGE007
is the eigenvector of the ith substation, and m is the characteristic dimension of the substation;
step 2.4, constructing a fuzzy similar matrix according to the distance matrix, randomly selecting c clustering centers through a genetic algorithm, initializing control parameters, initializing a population, calculating the membership and fitness of each individual according to the distance matrix, performing selection, crossing, mutation and other operations through the genetic algorithm to generate a new population, and replacing or receiving the old individual through a simulated annealing algorithm; wherein, the clustering center is:
Figure 948285DEST_PATH_IMAGE008
in the formula (I), the compound is shown in the specification,
Figure 86005DEST_PATH_IMAGE004
is the degree of membership of the ith substation to the jth substation,
Figure 897530DEST_PATH_IMAGE007
is the eigenvector of the ith substation, and m is the characteristic dimension of the substation;
step 2.5, target function
Figure 120701DEST_PATH_IMAGE009
The distance square sum of each substation load and the corresponding clustering center is formed and then summed, and an objective function is output
Figure 592133DEST_PATH_IMAGE010
A minimum cluster center and membership matrix, wherein the objective function is:
Figure 584360DEST_PATH_IMAGE011
in the formula (I), the compound is shown in the specification,
Figure 748625DEST_PATH_IMAGE009
each substation load constitutes the sum of squared distances from its corresponding cluster center,
Figure 708360DEST_PATH_IMAGE012
is the degree of membership of the ith substation to the jth substation,
Figure 452325DEST_PATH_IMAGE007
is the feature vector of the ith substation,
Figure 830217DEST_PATH_IMAGE005
is the jth initial cluster center;
and 2.6, according to the maximum membership principle of the substation membership matrix and the clustering center, taking the membership degree between the substation and the clustering center as a basis for selecting a typical site, and carrying out load classification on the substation.
Further, in the step 3, the method specifically includes:
step 3.1, selecting industry components in the industrial load as feature vectors;
Figure 634225DEST_PATH_IMAGE013
in the formula (I), the compound is shown in the specification,
Figure 831988DEST_PATH_IMAGE014
the method is characterized by comprising the following steps that (1) the industrial composition in the ith transformer substation industrial load is realized, p is the category of the transformer substation industrial load, and n is the number of the transformer substations;
step 3.2, performing data dimension reduction on the industry composition data in the industrial load through multidimensional scaling MDS, and mapping data point pairs with equal distances in a high-dimensional space in a low-dimensional space;
Figure 395955DEST_PATH_IMAGE015
in the formula (I), the compound is shown in the specification,
Figure 362774DEST_PATH_IMAGE016
is a characteristic vector formed by industries in the i-th substation industrial load after dimensionality reduction,
Figure 868842DEST_PATH_IMAGE017
is the x-axis vector after the dimension reduction,
Figure 288322DEST_PATH_IMAGE018
the y-axis vector after dimensionality reduction, and n is the number of the transformer substations;
3.3, clustering the industry composition data subjected to dimensionality reduction by adopting a genetic simulated annealing improved fuzzy clustering algorithm;
step 3.4, outputting the objective function
Figure 639669DEST_PATH_IMAGE019
The objective function of the minimum clustering center and the membership matrix is as follows:
Figure 975841DEST_PATH_IMAGE020
in the formula (I), the compound is shown in the specification,
Figure 387231DEST_PATH_IMAGE019
the distance square sum of each transformer substation industry composition and the corresponding clustering center is summed,
Figure 182755DEST_PATH_IMAGE021
is the membership degree formed by the i-th substation and the j-th substation industry,
Figure 72214DEST_PATH_IMAGE016
is a characteristic vector formed by industries in the i-th substation industrial load after dimensionality reduction,
Figure 13625DEST_PATH_IMAGE022
is the jth clustering center, n is the number of the transformer substations, and T is the number of the clustering centers;
the membership matrix is:
Figure 110763DEST_PATH_IMAGE023
in the formula (I), the compound is shown in the specification,
Figure 504835DEST_PATH_IMAGE024
is the membership degree formed by the i-th substation and the j-th substation industry,
Figure 197985DEST_PATH_IMAGE016
is a characteristic vector formed by industries in the i-th substation industrial load after dimensionality reduction,
Figure 993902DEST_PATH_IMAGE022
is the j-th cluster center and,
Figure 747095DEST_PATH_IMAGE025
is the T-th clustering center, and T is the number of the clustering centers;
the clustering center is as follows:
Figure 379196DEST_PATH_IMAGE026
in the formula (I), the compound is shown in the specification,
Figure 876036DEST_PATH_IMAGE022
is the j-th cluster center and,
Figure 792039DEST_PATH_IMAGE027
is the membership degree formed by the i-th substation and the j-th substation industry,
Figure 450554DEST_PATH_IMAGE016
the characteristic vector is formed by industries in the i-th transformer substation industrial load after dimensionality reduction, and n is the number of the transformer substations;
and 3.5, classifying the industry composition membership matrix and the corresponding clustering center matrix according to the maximum membership principle.
Further, in the step 4, for the transformer substation groups obtained by load classification, the transformer substation closest to the corresponding clustering center is selected from each group as a typical load characteristic transformer substation.
The invention has the beneficial effects that: compared with the prior art, the method has the advantages that the aggregation theory method is used for selecting the typical sites, the basis for selecting the typical sites is given according to the membership relationship, and scientific basis is provided for modelers in the process of selecting the typical sites; when load characteristic classification is carried out on the investigated target transformer substation, a classification principle is not set, so that the problem that the manually set classification principle is unreasonable is solved; the simulated annealing algorithm and the genetic algorithm are combined to improve the fuzzy clustering algorithm and are applied to selecting a typical transformer substation, so that the problem of inaccurate algorithm convergence caused by overlarge quantity space and improper initial value selection is solved; multilevel data such as load type composition of the transformer substation and industry composition in industrial load are organically combined in the process of selecting the typical station, so that the selected typical transformer substation can simultaneously well represent load characteristics and industry characteristics, and the problem of uniformity of typical basis in the process of screening the typical station is solved. A practical and efficient method is provided for selecting a typical transformer substation process in the field of electric power system load modeling based on a statistical synthesis method.
Drawings
Fig. 1 is a block flow diagram of a method for selecting a typical load characteristic substation based on multi-source data according to a preferred embodiment of the present invention;
FIG. 2 is a flow chart of the genetic simulated annealing algorithm to improve the fuzzy clustering algorithm in the preferred embodiment of the present invention.
Detailed Description
The technical solutions in the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments.
Referring to fig. 1-2, a preferred embodiment of the present invention, a method for selecting a typical load characteristic substation based on multi-source data, the method comprises the following steps:
step 1, carrying out load characteristic general survey on a transformer substation of a target power grid under the same voltage level to obtain load characteristic data, wherein the load characteristic data comprises load type data and industry composition data;
step 2, performing type clustering analysis on the transformer substations according to the load type data, and performing load classification on the transformer substations to obtain a plurality of transformer substation groups with similar load characteristics;
step 3, performing industry clustering analysis on the transformer substation groups according to industry composition data, and classifying the transformer substations in industry;
and 4, selecting a typical transformer substation capable of representing the load characteristics according to the load classification and the industry classification.
In this embodiment, in the step 1, the method specifically includes:
step 1.1, dividing survey ranges of a target power grid according to cities;
step 1.2, carrying out load general survey on all transformer substations in the same voltage class in the district of the city;
step 1.3, investigating load types borne by each transformer substation, active power consumed by each load type and occupied proportion to obtain load type data, wherein the load types comprise industrial loads, residential loads, commercial loads, agricultural loads and other loads;
and step 1.4, investigating the specific industry types and the occupied proportions in the industrial loads of all the substations to obtain industry composition data.
In this embodiment, in the step 2, the method specifically includes:
step 2.1, selecting the load constitution of the transformer substation as a characteristic vector;
Figure 84797DEST_PATH_IMAGE028
wherein the content of the first and second substances,
Figure 369017DEST_PATH_IMAGE029
representing the load composition of the ith transformer substation, wherein n is the number of the transformer substations, and m is the number of the characteristic indexes;
step 2.2, applying a polymerization theory method to the transformer substation load type classification, and improving a fuzzy clustering algorithm by adopting genetic simulated annealing;
and 2.3, calculating the similarity among the substations through fuzzy clustering, and converting the similarity among different substation load compositions into the magnitude of the membership degree for expression, wherein the membership degree is as follows:
Figure 139527DEST_PATH_IMAGE030
in the formula (I), the compound is shown in the specification,
Figure 234522DEST_PATH_IMAGE031
is the degree of membership of the ith substation to the jth substation,
Figure 90483DEST_PATH_IMAGE032
is the jth initial cluster center and,
Figure 194705DEST_PATH_IMAGE033
is the kth initial clustering center, K is the number of clustering centers,
Figure 301945DEST_PATH_IMAGE034
is the eigenvector of the ith substation, and m is the characteristic dimension of the substation;
step 2.4, constructing a fuzzy similar matrix according to the distance matrix, randomly selecting c clustering centers through a genetic algorithm, initializing control parameters, initializing a population, calculating the membership and fitness of each individual according to the distance matrix, performing selection, crossing, mutation and other operations through the genetic algorithm to generate a new population, and replacing or receiving the old individual through a simulated annealing algorithm; wherein, the clustering center is:
Figure 833420DEST_PATH_IMAGE035
in the formula (I), the compound is shown in the specification,
Figure 911098DEST_PATH_IMAGE031
is the degree of membership of the ith substation to the jth substation,
Figure 819011DEST_PATH_IMAGE034
is the eigenvector of the ith substation, and m is the characteristic dimension of the substation;
step 2.5, target function
Figure 298534DEST_PATH_IMAGE009
The distance square sum of each substation load and the corresponding clustering center is formed and then summed, and an objective function is output
Figure 984599DEST_PATH_IMAGE009
A minimum cluster center and membership matrix, wherein the objective function is:
Figure 549572DEST_PATH_IMAGE036
in the formula (I), the compound is shown in the specification,
Figure 995597DEST_PATH_IMAGE009
each substation load constitutes the sum of squared distances from its corresponding cluster center,
Figure 329627DEST_PATH_IMAGE031
is the degree of membership of the ith substation to the jth substation,
Figure 202905DEST_PATH_IMAGE037
is the feature vector of the ith substation,
Figure 5907DEST_PATH_IMAGE038
is the jth initial cluster center;
and 2.6, according to the maximum membership principle of the substation membership matrix and the clustering center, taking the membership degree between the substation and the clustering center as a basis for selecting a typical site, and carrying out load classification on the substation.
In this embodiment, in the step 3, the method specifically includes:
step 3.1, selecting industry components in the industrial load as feature vectors;
Figure 724464DEST_PATH_IMAGE039
in the formula (I), the compound is shown in the specification,
Figure 444158DEST_PATH_IMAGE040
the method is characterized by comprising the following steps that (1) the industrial composition in the ith transformer substation industrial load is realized, p is the category of the transformer substation industrial load, and n is the number of the transformer substations;
step 3.2, performing data dimension reduction on the industry composition data in the industrial load through multidimensional scaling MDS, and mapping data point pairs with equal distances in a high-dimensional space in a low-dimensional space;
Figure 222758DEST_PATH_IMAGE041
in the formula (I), the compound is shown in the specification,
Figure 27903DEST_PATH_IMAGE016
is a characteristic vector formed by industries in the i-th substation industrial load after dimensionality reduction,
Figure 799419DEST_PATH_IMAGE042
is the x-axis vector after the dimension reduction,
Figure 373620DEST_PATH_IMAGE043
the y-axis vector after dimensionality reduction, and n is the number of the transformer substations;
3.3, clustering the industry composition data subjected to dimensionality reduction by adopting a genetic simulated annealing improved fuzzy clustering algorithm;
step 3.4, outputting the objective function
Figure 323121DEST_PATH_IMAGE044
The objective function of the minimum clustering center and the membership matrix is as follows:
Figure 84404DEST_PATH_IMAGE045
in the formula (I), the compound is shown in the specification,
Figure 675922DEST_PATH_IMAGE044
the distance square sum of each transformer substation industry composition and the corresponding clustering center is summed,
Figure 586853DEST_PATH_IMAGE021
is the membership degree formed by the i-th substation and the j-th substation industry,
Figure 972835DEST_PATH_IMAGE016
is a characteristic vector formed by industries in the i-th substation industrial load after dimensionality reduction,
Figure 486993DEST_PATH_IMAGE022
is the jth cluster centerN is the number of the transformer substations, and T is the number of the clustering centers;
the membership matrix is:
Figure 616623DEST_PATH_IMAGE046
in the formula (I), the compound is shown in the specification,
Figure 634258DEST_PATH_IMAGE021
is the membership degree formed by the i-th substation and the j-th substation industry,
Figure 705988DEST_PATH_IMAGE016
is a characteristic vector formed by industries in the i-th substation industrial load after dimensionality reduction,
Figure 441863DEST_PATH_IMAGE022
is the j-th cluster center and,
Figure 109604DEST_PATH_IMAGE047
is the T-th clustering center, and T is the number of the clustering centers;
the clustering center is as follows:
Figure 247325DEST_PATH_IMAGE048
in the formula (I), the compound is shown in the specification,
Figure 975109DEST_PATH_IMAGE022
is the j-th cluster center and,
Figure 949013DEST_PATH_IMAGE021
is the membership degree formed by the i-th substation and the j-th substation industry,
Figure 420445DEST_PATH_IMAGE016
the characteristic vector is formed by industries in the i-th transformer substation industrial load after dimensionality reduction, and n is the number of the transformer substations;
and 3.5, classifying the industry composition membership matrix and the corresponding clustering center matrix according to the maximum membership principle.
In this embodiment, in step 4, for the transformer substation groups obtained by load classification, the transformer substation closest to the corresponding clustering center is selected as the typical load characteristic transformer substation in each group.
The invention applies the aggregation theory method to the selection of the typical sites, gives the basis for selecting the typical sites according to the membership relationship, and provides scientific basis for the modeler in the process of selecting the typical sites; when load characteristic classification is carried out on the investigated target transformer substation, a classification principle is not set, so that the problem that the manually set classification principle is unreasonable is solved; the simulated annealing algorithm and the genetic algorithm are combined to improve the fuzzy clustering algorithm and are applied to selecting a typical transformer substation, so that the problem of inaccurate algorithm convergence caused by overlarge quantity space and improper initial value selection is solved; multilevel data such as load type composition of the transformer substation and industry composition in industrial load are organically combined in the process of selecting the typical station, so that the selected typical transformer substation can simultaneously well represent load characteristics and industry characteristics, and the problem of uniformity of typical basis in the process of screening the typical station is solved. A practical and efficient method is provided for selecting a typical transformer substation process in the field of electric power system load modeling based on a statistical synthesis method.
In order to facilitate an understanding of the present invention, the following provides a more detailed method of the present invention:
a method for selecting a typical load characteristic transformer substation based on multi-source data is characterized by comprising the following steps:
A. developing the general survey of the load characteristics of the transformer substation under the same voltage level of the target power grid, wherein the survey contents comprise: the load types and the occupied proportion of the load types in the power supply range of each transformer substation are as follows, the load types include 5 types: industrial, residential, commercial, agricultural and other loads; specific types and configurations of industrial loads, the industrial types including: mining, chemical, petroleum, paper, food processing, mechanical, transportation, electrical, electronics, textile, metal processing, rubber and plastic manufacturing, wood processing, tobacco industry, printing industry, leather industry, steel industry, electrified railroad, electrolytic aluminum, medicine, electroceramics, cement, photovoltaics, and others. (ii) a
A1. Dividing a survey range of a target power grid according to cities;
A2. carrying out load general investigation on all transformer substations in the same voltage level in the district of the local city;
A3. load type data survey content and form: investigating the active power consumed and the proportion thereof in the types of loads, wherein the types of loads comprise industrial loads, residential loads, commercial loads, agricultural loads and other loads;
A4. content and form of industrial load survey: investigating the specific type and the occupied proportion of the industrial load;
B. performing clustering analysis on the transformer substation according to the load type data to finish transformer substation load classification;
B1. selecting load constitution of a transformer substation as a characteristic vector;
B2. the aggregation theory method is used for transformer substation classification, a fuzzy clustering algorithm is improved by using genetic simulated annealing, and the detailed process is shown in an attached figure 2;
B3. calculating the similarity among the transformer substations through fuzzy clustering, and converting the similarity among different transformer substation load compositions into the magnitude of membership for expression;
B4. constructing a fuzzy similar matrix according to the distance matrix, randomly selecting c clustering centers through a genetic algorithm, initializing control parameters, initializing a population, calculating the membership degree and the fitness degree of each individual according to the distance matrix, performing selection, crossing, mutation and other operations through the genetic algorithm to generate a new population, and replacing or receiving old individuals through a simulated annealing algorithm; specifically, the specific process of the steps comprises:
recording n transformer substation objects to be classified as a set
Figure 678251DEST_PATH_IMAGE049
Each of the transformer substations
Figure 576937DEST_PATH_IMAGE050
There are m characteristic indexes (where power transformation is selected)Station 5 different types of load formation as eigenvectors), i.e.
Figure 21825DEST_PATH_IMAGE050
Can be used
Figure 546216DEST_PATH_IMAGE051
Dimensional property index vector to represent:
Figure 658529DEST_PATH_IMAGE052
，
Figure 728116DEST_PATH_IMAGE050
and the load structure of the ith transformer substation is shown, n is the number of the transformer substations, and m is the number of the characteristic indexes. All the characteristic indexes of m objects can form a matrix, and the matrix is recorded as
Figure 925879DEST_PATH_IMAGE053
Balance of
Figure 739114DEST_PATH_IMAGE054
Is a matrix of characteristic indicators for X.
Figure 453736DEST_PATH_IMAGE055
a. Data normalization
Because the dimension and the magnitude of the m characteristic indexes are not necessarily the same, the action of the characteristic indexes with a certain magnitude on classification can be highlighted in the operation process, and the action of the characteristic indexes with small magnitude is reduced or even eliminated. Therefore, the data needs to be standardized, scaled to fall into a small specific interval, unit limitation of the data is removed, and the data is converted into a dimensionless pure numerical value, so that indexes of different units or orders of magnitude can be compared and weighted conveniently.
Normalization treatment:
for forward sequence
Figure 428645DEST_PATH_IMAGE056
To carry outAs a result of the transformation as follows,
Figure 379283DEST_PATH_IMAGE057
then the new sequence after treatment
Figure 730630DEST_PATH_IMAGE058
All fall within the new interval [0,1 ]]Is internal and dimensionless, and has
Figure 817535DEST_PATH_IMAGE059
. The characteristic index matrix after normalization processing is as follows:
Figure 478192DEST_PATH_IMAGE060
b. constructing fuzzy similar matrix, initializing membership degree matrix
Clustering is to identify the closeness between elements in the characteristic index matrix X according to a certain criterion, and classify objects close to each other into a class. For this purpose, the required voltage is in [0,1 ]]Number in
Figure 650548DEST_PATH_IMAGE061
Showing objects in X
Figure 540006DEST_PATH_IMAGE062
And
Figure 481418DEST_PATH_IMAGE063
the degree of closeness or similarity between them, i.e. the similarity factor. Matrix of characteristic indexes
Figure 63709DEST_PATH_IMAGE054
Data in (1)
Figure 208513DEST_PATH_IMAGE064
Is standardized to obtain
Figure 901663DEST_PATH_IMAGE065
，
Figure 963160DEST_PATH_IMAGE066
And
Figure 716352DEST_PATH_IMAGE067
the degree of similarity between them is recorded as
Figure 863300DEST_PATH_IMAGE068
And in the interval [0,1 ]]In this way, a fuzzy similarity matrix between all objects can be obtained
Figure 343828DEST_PATH_IMAGE069
。
For the object
Figure 259832DEST_PATH_IMAGE066
And
Figure 183926DEST_PATH_IMAGE067
degree of similarity therebetween (similarity coefficient)
Figure 818169DEST_PATH_IMAGE068
The determination is measured by the linear distance between the object point and the point in the multidimensional space, and the Euclidean distance is adopted for determination:
Figure 853121DEST_PATH_IMAGE070
in the formula (I), the compound is shown in the specification,
Figure 105855DEST_PATH_IMAGE071
is that
Figure 200850DEST_PATH_IMAGE072
And
Figure 322389DEST_PATH_IMAGE073
the euclidean distance between them,
Figure 895453DEST_PATH_IMAGE072
is the feature vector of the ith substation,
Figure 786049DEST_PATH_IMAGE073
is the feature vector of the jth substation,
Figure 301213DEST_PATH_IMAGE074
is the 1 st feature vector of the ith substation,
Figure 644469DEST_PATH_IMAGE075
is the 1 st eigenvector of the jth substation, and m is the eigenvector dimension;
c. fuzzy classification
Due to the fuzzy relation matrix between the objects constructed by the above methods
Figure 286803DEST_PATH_IMAGE076
Generally speaking, it is just a fuzzy similarity matrix, and not necessarily transitive. Therefore, a new fuzzy equivalent matrix is constructed from the R, and then dynamic clustering is performed based on the fuzzy equivalent matrix.
As described above, the transitive closure of the fuzzy similarity matrix R
Figure 31905DEST_PATH_IMAGE077
Is a fuzzy equivalence matrix. To be provided with
Figure 468703DEST_PATH_IMAGE077
The clustering method based on classification is called fuzzy transmission closed-packet method.
The method comprises the following specific steps: (1) transfer closure for solving fuzzy similarity matrix R by using square self-synthesis method
Figure 299256DEST_PATH_IMAGE077
(ii) a (2) Selecting confidence level values appropriately
Figure 964854DEST_PATH_IMAGE078
In the range of [0,1]To find out
Figure 830042DEST_PATH_IMAGE077
Is cut matrix
Figure 437741DEST_PATH_IMAGE079
Which is an equivalent Boole matrix on X. Then press against
Figure 490011DEST_PATH_IMAGE079
Classifying, the obtained classification is
Figure 474147DEST_PATH_IMAGE080
And equivalence classification on the horizontal.
Is provided with
Figure 177530DEST_PATH_IMAGE081
，
Figure 221709DEST_PATH_IMAGE082
And then:
Figure 761275DEST_PATH_IMAGE083
Figure 549103DEST_PATH_IMAGE080
is a confidence level value for
Figure 857724DEST_PATH_IMAGE084
If, if
Figure 555028DEST_PATH_IMAGE085
Then is at
Figure 581890DEST_PATH_IMAGE080
Object at confidence level
Figure 173409DEST_PATH_IMAGE072
And object
Figure 336537DEST_PATH_IMAGE073
Fall into the same category.
Degree of membership:
Figure 456939DEST_PATH_IMAGE086
in the formula (I), the compound is shown in the specification,
Figure 485944DEST_PATH_IMAGE031
is the degree of membership of the ith substation to the jth substation,
Figure 615574DEST_PATH_IMAGE038
is the jth initial cluster center and,
Figure 633209DEST_PATH_IMAGE006
is the k-th initial cluster center and,
Figure 190092DEST_PATH_IMAGE034
is the eigenvector of the ith substation, and m is the characteristic dimension of the substation;
clustering center:
Figure 191546DEST_PATH_IMAGE087
in the formula (I), the compound is shown in the specification,
Figure 344441DEST_PATH_IMAGE031
is the degree of membership of the ith substation to the jth substation,
Figure 747740DEST_PATH_IMAGE034
is the eigenvector of the ith substation, and m is the characteristic dimension of the substation;
d. initializing control parameters, including a weighting index, a maximum iteration number and an evaluation objective function in a fuzzy C-means clustering algorithm, a population size, a maximum evolution number, a cross probability and a variation probability in a genetic algorithm, and simulating an annealing initial temperature, a temperature cooling coefficient and a termination temperature in an annealing algorithm;
B5. an objective function
Figure 475525DEST_PATH_IMAGE009
The distance square sum of each substation load and the corresponding clustering center is formed and then summed, and an objective function is output
Figure 698696DEST_PATH_IMAGE010
The minimum clustering center and membership matrix;
an objective function:
Figure 170129DEST_PATH_IMAGE088
in the formula (I), the compound is shown in the specification,
Figure 427935DEST_PATH_IMAGE009
each substation load constitutes the sum of squared distances from its corresponding cluster center,
Figure 310309DEST_PATH_IMAGE031
is the degree of membership of the ith substation to the jth substation,
Figure 286355DEST_PATH_IMAGE034
is the feature vector of the ith substation,
Figure 295899DEST_PATH_IMAGE038
is the jth initial cluster center;
B6. according to the maximum membership principle of the substation membership matrix and the clustering center, the classification of the substations is completed, the membership degree between the substation and the clustering center is used as a basis for selecting typical sites, and a specific substation load classification principle is not set manually;
C. for each group of substations, carrying out substation industry classification on the substations according to the industry composition data;
step 3.1, selecting industry components in the industrial load as feature vectors;
Figure 408212DEST_PATH_IMAGE089
in the formula (I), the compound is shown in the specification,
Figure 477799DEST_PATH_IMAGE040
is the industry composition in the i-th substation industrial load, p is the class of the substation industrial load, n is the variationThe number of power stations;
step 3.2, performing data dimension reduction on the industry composition data in the industrial load through multidimensional scaling MDS, and mapping data point pairs with equal distances in a high-dimensional space in a low-dimensional space;
Figure 157786DEST_PATH_IMAGE090
，
in the formula (I), the compound is shown in the specification,
Figure 236600DEST_PATH_IMAGE016
is a characteristic vector formed by industries in the i-th substation industrial load after dimensionality reduction,
Figure 203419DEST_PATH_IMAGE042
is the x-axis vector after the dimension reduction,
Figure 443908DEST_PATH_IMAGE091
the y-axis vector after dimensionality reduction, and n is the number of the transformer substations;
3.3, clustering the industry composition data subjected to dimensionality reduction by adopting a genetic simulated annealing improved fuzzy clustering algorithm;
step 3.4, outputting the objective function
Figure 128967DEST_PATH_IMAGE092
The objective function of the minimum clustering center and the membership matrix is as follows:
Figure 995161DEST_PATH_IMAGE093
in the formula (I), the compound is shown in the specification,
Figure 816486DEST_PATH_IMAGE092
the distance square sum of each transformer substation industry composition and the corresponding clustering center is summed,
Figure 227876DEST_PATH_IMAGE021
is the membership degree formed by the i-th substation and the j-th substation industry,
Figure 134652DEST_PATH_IMAGE016
is a characteristic vector formed by industries in the i-th substation industrial load after dimensionality reduction,
Figure 555269DEST_PATH_IMAGE022
is the jth clustering center, n is the number of the transformer substations, and T is the number of the clustering centers;
the membership matrix is:
Figure 981833DEST_PATH_IMAGE094
in the formula (I), the compound is shown in the specification,
Figure 564124DEST_PATH_IMAGE021
is the membership degree formed by the i-th substation and the j-th substation industry,
Figure 223776DEST_PATH_IMAGE016
is a characteristic vector formed by industries in the i-th substation industrial load after dimensionality reduction,
Figure 182505DEST_PATH_IMAGE022
is the j-th cluster center and,
Figure 978422DEST_PATH_IMAGE047
is the T-th clustering center, and T is the number of the clustering centers;
the clustering center is as follows:
Figure 980882DEST_PATH_IMAGE095
in the formula (I), the compound is shown in the specification,
Figure 862251DEST_PATH_IMAGE022
is the j-th cluster center and,
Figure 359091DEST_PATH_IMAGE021
is the membership degree formed by the i-th substation and the j-th substation industry,
Figure 275094DEST_PATH_IMAGE016
the characteristic vector is formed by industries in the i-th transformer substation industrial load after dimensionality reduction, and n is the number of the transformer substations;
and 3.5, classifying the industry composition membership matrix and the corresponding clustering center matrix according to the maximum membership principle.
D. Selecting a typical transformer substation;
D1. for the load classification, selecting the transformer substation closest to the corresponding clustering center as a typical load characteristic in each class;
D2. the selected typical transformer substation can simultaneously well represent load characteristics and industrial characteristics, and the uniformity of typicality in the typical site selection process is guaranteed.
The method comprises the steps of combining load composition data with industrial composition data in industrial loads to finish selecting a typical transformer substation, and finishing the aggregation grouping of the transformer substations based on the load composition by selecting the load composition as a characteristic vector to ensure that the transformer substations in each group have similar load compositions; secondly, selecting industry composition data as a characteristic vector for each group of substations based on the grouping condition in the first step, and finishing the aggregation grouping of the substations based on the industry composition in the industrial load so as to classify the substations with similar industry compositions into one class; and thirdly, selecting representative transformer substations in each group of transformer substations as typical transformer substations according to the relation between the clustering centers and the membership degrees, so that the screened typical transformer substations can show the composition proportion of 5 different load types of the transformer substations and can also show the industry composition characteristics in industrial loads. The problem that the stability of results is affected due to different initialization is optimized through a simulated annealing algorithm, the accuracy of a fuzzy clustering algorithm is improved through iteration of a genetic algorithm, the simulated annealing algorithm is combined with the genetic algorithm to improve a fuzzy C mean value clustering method, the fuzzy C mean value clustering method is a clustering analysis method based on an objective function, the closeness degree, namely membership degree, of all elements is calculated according to the objective function and serves as a method for dividing similar transformer substations, and a clustering center can represent the load characteristics of the transformer substations.
One specific example is provided below:
the typical load site selection method provided by the invention comprises the following steps:
1. developing general load characteristic survey aiming at all 220kV transformer substations carried by the power grid in Jiangxi, and collecting 160 groups of effective data in the general load characteristic survey;
2. classifying the transformer substation according to the load composition;
fuzzy C-means clustering is improved through a genetic simulated annealing algorithm, loads are selected to form characteristic vectors, 160 transformer substations are divided into 9 classes, and the clustering center of each class is shown in table 1;
TABLE 1 clustering center List
Figure 199188DEST_PATH_IMAGE096
According to the query clustering center and the substation membership matrix, the substations are grouped according to the maximum membership principle, and the substation grouping result is shown in the following table 2;
TABLE 2 grouping of substations
Figure 833432DEST_PATH_IMAGE097
Figure 639624DEST_PATH_IMAGE098
3. Correspondingly sorting out the industry components in the industry components corresponding to each group of substations according to the grouping condition of the substations, and mapping 25-dimensional industry component data in a two-dimensional space in an equidistance manner by multi-dimensional scaling, wherein the data are shown in a table below;
table 3.1 mapping table is composed of the first group of substation industries
Figure 144555DEST_PATH_IMAGE099
Table 3.2 second group of substation industries form mapping table
Figure 505129DEST_PATH_IMAGE100
Table 3.3 mapping table is formed by the third group of substation industries
Figure 95510DEST_PATH_IMAGE101
Table 3.4 mapping table for industry composition of fourth group of substations
Figure 199733DEST_PATH_IMAGE102
Table 3.5 fifth group substation industry composition mapping table
Figure 74017DEST_PATH_IMAGE103
Table 3.6 sixth group of substations industry composition mapping table
Figure 605492DEST_PATH_IMAGE104
Table 3.7 seventh group substation industry composition mapping table
Figure 683169DEST_PATH_IMAGE105
Table 3.8 eighth group substation industry composition mapping table
Figure 59924DEST_PATH_IMAGE106
Table 3.9 ninth group substation industry composition mapping table
Figure 805026DEST_PATH_IMAGE107
4. The C mean value clustering algorithm is improved through a genetic simulated annealing algorithm to cluster the industry composition data of each group, and the clustering center of each group is shown in the following table 4;
Figure 992556DEST_PATH_IMAGE108
TABLE 4 Cluster centers of each group
5. Selecting a transformer substation of a clustering center as a typical transformer substation which represents the most representative industry composition characteristics in the load characteristic transformer substations, and referring to the following table 5;
Figure 557530DEST_PATH_IMAGE109
TABLE 5 typical substations
The above additional technical features can be freely combined and used in superposition by those skilled in the art without conflict.
It is to be understood that the present invention has been described with reference to certain embodiments, and that various changes in the features and embodiments, or equivalent substitutions may be made therein by those skilled in the art without departing from the spirit and scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.

Claims (5)

1. A method for selecting a typical load characteristic transformer substation based on multi-source data is characterized by comprising the following steps:
step 1, carrying out load characteristic general survey on a transformer substation of a target power grid under the same voltage level to obtain load characteristic data, wherein the load characteristic data comprises load type data and industry composition data;
step 2, performing type clustering analysis on the transformer substations according to the load type data, and performing load classification on the transformer substations to obtain a plurality of transformer substation groups with similar load characteristics;
step 3, performing industry clustering analysis on the transformer substation groups according to industry composition data, and classifying the transformer substations in industry;
and 4, selecting a typical transformer substation capable of representing the load characteristics according to the load classification and the industry classification.
2. The method for selecting the substation with the typical load characteristics based on the multi-source data according to claim 1, wherein the method comprises the following steps: in the step 1, the method specifically includes:
step 1.1, dividing survey ranges of a target power grid according to cities;
step 1.2, carrying out load general survey on all transformer substations in the same voltage class in the district of the city;
step 1.3, investigating load types borne by each transformer substation, active power consumed by each load type and occupied proportion to obtain load type data, wherein the load types comprise industrial loads, residential loads, commercial loads, agricultural loads and other loads;
and step 1.4, investigating the specific industry types and the occupied proportions in the industrial loads of all the substations to obtain industry composition data.
3. The method for selecting the substation with the typical load characteristics based on the multi-source data according to claim 1, wherein the method comprises the following steps: in the step 2, the method specifically includes:
step 2.1, selecting the load constitution of the transformer substation as a characteristic vector;
Figure 798446DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 647322DEST_PATH_IMAGE002
the load composition of the ith transformer substation is shown, n is the number of the transformer substations, and m is the number of the characteristic indexes;
step 2.2, applying a polymerization theory method to the transformer substation load type classification, and improving a fuzzy clustering algorithm by adopting genetic simulated annealing;
and 2.3, calculating the similarity among the substations through fuzzy clustering, and converting the similarity among different substation load compositions into the magnitude of the membership degree for expression, wherein the membership degree is as follows:
Figure 989442DEST_PATH_IMAGE003
in the formula (I), the compound is shown in the specification,
Figure 776132DEST_PATH_IMAGE004
is the degree of membership of the ith substation to the jth substation,
Figure 557006DEST_PATH_IMAGE005
is the jth initial cluster center and,
Figure 745542DEST_PATH_IMAGE006
is the kth initial clustering center, K is the number of clustering centers,
Figure 773410DEST_PATH_IMAGE007
is the eigenvector of the ith substation, and m is the characteristic dimension of the substation;
step 2.4, constructing a fuzzy similar matrix according to the distance matrix, randomly selecting c clustering centers through a genetic algorithm, initializing control parameters, initializing a population, calculating the membership and fitness of each individual according to the distance matrix, performing selection, crossing, mutation and other operations through the genetic algorithm to generate a new population, and replacing or receiving the old individual through a simulated annealing algorithm; wherein, the clustering center is:
Figure 844134DEST_PATH_IMAGE008
in the formula (I), the compound is shown in the specification,
Figure 100803DEST_PATH_IMAGE004
is the degree of membership of the ith substation to the jth substation,
Figure 409425DEST_PATH_IMAGE007
is the eigenvector of the ith substation, and m is the characteristic dimension of the substation;
step 2.5, target function
Figure 155664DEST_PATH_IMAGE009
The distance square sum of each substation load and the corresponding clustering center is formed and then summed, and an objective function is output
Figure 916946DEST_PATH_IMAGE009
A minimum cluster center and membership matrix, wherein the objective function is:
Figure 226574DEST_PATH_IMAGE010
in the formula (I), the compound is shown in the specification,
Figure 452019DEST_PATH_IMAGE009
each substation load constitutes the sum of squared distances from its corresponding cluster center,
Figure 306842DEST_PATH_IMAGE004
is the degree of membership of the ith substation to the jth substation,
Figure 555421DEST_PATH_IMAGE007
is the feature vector of the ith substation,
Figure 216210DEST_PATH_IMAGE005
is the jth initial clustering center, and K is the number of clustering centers;
and 2.6, according to the maximum membership principle of the substation membership matrix and the clustering center, taking the membership degree between the substation and the clustering center as a basis for selecting a typical site, and carrying out load classification on the substation.
4. The method for selecting the substation with the typical load characteristics based on the multi-source data according to claim 1, wherein the method comprises the following steps: in the step 3, the method specifically includes:
step 3.1, selecting industry components in the industrial load as feature vectors;
Figure 233844DEST_PATH_IMAGE011
in the formula (I), the compound is shown in the specification,
Figure 774416DEST_PATH_IMAGE012
the method is characterized by comprising the following steps that (1) the industrial composition in the ith transformer substation industrial load is realized, p is the category of the transformer substation industrial load, and n is the number of the transformer substations;
step 3.2, performing data dimension reduction on the industry composition data in the industrial load through multidimensional scaling MDS, and mapping data point pairs with equal distances in a high-dimensional space in a low-dimensional space;
Figure 392783DEST_PATH_IMAGE013
in the formula (I), the compound is shown in the specification,
Figure 981896DEST_PATH_IMAGE014
is a characteristic vector formed by industries in the i-th substation industrial load after dimensionality reduction,
Figure 588458DEST_PATH_IMAGE015
is the x-axis vector after the dimension reduction,
Figure 565510DEST_PATH_IMAGE016
the y-axis vector after dimensionality reduction, and n is the number of the transformer substations;
3.3, clustering the industry composition data subjected to dimensionality reduction by adopting a genetic simulated annealing improved fuzzy clustering algorithm;
step 3.4, outputting the objective function
Figure 991943DEST_PATH_IMAGE017
Is the smallest cluster center and membership matrix, whose objective function is:
Figure 181485DEST_PATH_IMAGE018
in the formula (I), the compound is shown in the specification,
Figure 173712DEST_PATH_IMAGE017
the distance square sum of each transformer substation industry composition and the corresponding clustering center is summed,
Figure 603556DEST_PATH_IMAGE019
is the membership degree formed by the i-th substation and the j-th substation industry,
Figure 314023DEST_PATH_IMAGE014
is a characteristic vector formed by industries in the i-th substation industrial load after dimensionality reduction,
Figure 57988DEST_PATH_IMAGE020
is the jth clustering center, n is the number of the transformer substations, and T is the number of the clustering centers;
the membership matrix is:
Figure 701459DEST_PATH_IMAGE021
in the formula (I), the compound is shown in the specification,
Figure 20314DEST_PATH_IMAGE019
is the membership degree formed by the i-th substation and the j-th substation industry,
Figure 952498DEST_PATH_IMAGE014
is a characteristic vector formed by industries in the i-th substation industrial load after dimensionality reduction,
Figure 500154DEST_PATH_IMAGE020
is the jth clusterThe center of the device is provided with a central hole,
Figure 998131DEST_PATH_IMAGE022
is the T-th clustering center, and T is the number of the clustering centers;
the clustering center is as follows:
Figure 973040DEST_PATH_IMAGE023
in the formula (I), the compound is shown in the specification,
Figure 641788DEST_PATH_IMAGE020
is the j-th cluster center and,
Figure 789873DEST_PATH_IMAGE019
is the membership degree formed by the i-th substation and the j-th substation industry,
Figure 345619DEST_PATH_IMAGE014
the characteristic vector is formed by industries in the i-th transformer substation industrial load after dimensionality reduction, and n is the number of the transformer substations;
and 3.5, classifying the industry composition membership matrix and the corresponding clustering center matrix according to the maximum membership principle.
5. The method for selecting the substation with the typical load characteristics based on the multi-source data according to claim 1, wherein the method comprises the following steps: in the step 4, for the transformer substation groups obtained by load classification, the transformer substation closest to the corresponding clustering center is selected from each group as a typical load characteristic transformer substation.
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