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Power distribution network transformer substation locating and sizing method and device based on improved K-means algorithm

A substation site selection and K-mean technology, applied in computing, computer components, instruments, etc., can solve the problems of large influence of human factors and unfavorable promotion, and achieve the effect of promoting, benefiting economy and operating reliability

Pending Publication Date: 2021-01-22
STATE GRID XINJIANG ELECTRIC POWER CO ECONOMIC TECH RES INST +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The present invention provides a method and device for site selection and capacity determination of distribution network substations based on the improved K-means algorithm. Different control parameter values ​​are set according to different planning areas, resulting in the problem that human factors have a large impact and are not conducive to promotion

Method used

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  • Power distribution network transformer substation locating and sizing method and device based on improved K-means algorithm
  • Power distribution network transformer substation locating and sizing method and device based on improved K-means algorithm
  • Power distribution network transformer substation locating and sizing method and device based on improved K-means algorithm

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Embodiment 1

[0046]Example 1: As attachedfigure 1 As shown, this embodiment discloses a method for site selection and capacity determination of a distribution network substation based on an improved K-means algorithm, including:

[0047]S101: Determine the total number of new substations, the total number of loads, the location of each load, and the load value in the planned area;

[0048]S102: Using each substation as a cluster, determine the initial cluster center of each cluster, and obtain the position and load value of the initial cluster center, where the initial cluster center is the load with the largest sum of distances to all cluster centers;

[0049]S103: Set the number of iterations, perform K-means cluster analysis on the loads that have not entered the cluster, put each load in the nearest cluster, and update the cluster center and total load value of each cluster;

[0050]S104. Set the total load threshold of the clusters, compare to obtain clusters greater than the total load threshold, move...

Embodiment 2

[0067]Example 2: As attachedimage 3 As shown, this embodiment discloses a method for site selection and capacity determination of a distribution network substation based on an improved K-means algorithm, including:

[0068]S201, determine the total number of new substations in the planned area Ns, Total load Nl, The position and load value of each load;

[0069]S202, set the load number as j and the cluster number as i, then j=1, 2,...,Nl, The position of the jth load is (xj,yj), the load value of the jth load is Pj, I=1,2,...,Ns, Where NlIs the total number of loads, NsIs the total number of substations;

[0070]S203, find the load value PjMaximum load jmax , As the first cluster C1The initial cluster center of, get the first cluster C1Initial cluster centerLoad valueAnd put it into cluster C1In the collection

[0071]S204. Set the cluster number i=i+1, traverse each load, obtain the sum of the distances from each load to all initial cluster centers, and obtain the load j corresponding to the ...

Embodiment 3

[0083]Example 3, as attachedFigure 5 As shown, this embodiment discloses a method for site selection and capacity determination of a distribution network substation based on an improved K-means algorithm, including:

[0084]S301: Determine the total number of newly built substations, the total number of loads, the location of each load, and the load value in the planned area;

[0085]S302: Using each substation as a cluster, determine the initial cluster center of each cluster, and obtain the position and load value of the initial cluster center, where the initial cluster center is the load with the largest sum of distances to all cluster centers;

[0086]S303: Set the total number of iterations, and set the number of iterations It=1;

[0087]S304: Calculate the distance between each load and each cluster center, obtain the load closest to each cluster center, put it into the corresponding cluster, and update the cluster center of each cluster;

[0088]S305: Set the number of iterations It=It+1, a...

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Abstract

The invention relates to the technical field of substation locating and sizing and particularly relates to a power distribution network substation locating and sizing method and device based on an improved K-means algorithm. The method comprises steps of determining the total number of newly-built substations in a planning region, the total number of loads, and the positions and load values of allloads; taking each transformer substation as a cluster to obtain the position and the load value of an initial clustering center; performing K-means clustering analysis on the loads which do not enter clustering, and updating the clustering center and the total load value of each cluster; and setting a total load threshold of the clusters to enable the total load values of all the clusters to besmaller than the total load threshold, and updating the clustering centers and the total load values of the corresponding clusters. According to the method, manual parameter setting and random assignment of the initial clustering center of each cluster are not needed, transformer substation site selection and constant volume can be realized through the load value and the load position of each loadin the planning area, and the clustering center and the total load value of each cluster are adjusted for the second time by setting the threshold of the total load value.

Description

Technical field[0001]The invention relates to the technical field of substation site selection and capacity determination, and is a method and device for distribution network substation site selection and capacity determination based on an improved K-means algorithm.Background technique[0002]The location and capacity of distribution network substations is to determine the optimal number, capacity and location of substations in the planned area, which directly affects the rationality of the distribution network structure and the configuration of reactive power sources, and affects the economy of the entire urban power grid. Performance and operational reliability.[0003]Based on the intelligent algorithm, the results of the distribution network substation location are greatly affected by the control parameters. At present, there is a lack of unified control parameter values. Different planning areas need to select different control parameters, which is difficult to promote practically...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/02G06Q50/06G06K9/62
CPCG06Q30/0205G06Q50/06G06F18/23
Inventor 周红莲曹茜王洪涛胡志云张瑞龙王威
Owner STATE GRID XINJIANG ELECTRIC POWER CO ECONOMIC TECH RES INST