基于改进层次聚类和GL-APSO算法的配电网动态重构

王云; 王美蕴; 周健; 邹媛媛; 李少远

doi:10.11959/j.issn.2096-6652.202243

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基于改进层次聚类和GL-APSO算法的配电网动态重构

学术论文 | 更新时间：2024-06-05

- 基于改进层次聚类和GL-APSO算法的配电网动态重构
- Dynamic configuration of distribution network based on improved hierarchical clustering and GL-APSO algorithm
- 智能科学与技术学报 2022年4卷第3期页码：410-417
- 作者机构：
  
  1. 国网上海市电力公司，上海 200125
  2. 上海交通大学电子信息与电气工程学院，上海 200240
- 作者简介：
  
  [ "王云（1986- ），男，博士，国网上海市电力公司工程师，主要研究方向为配电自动化" ]
  [ "王美蕴（1997- ），女，上海交通大学电子信息与电气工程学院工程师，主要研究方向为配电网优化" ]
  [ "周健（1974- ），男，国网上海市电力公司高级工程师，主要研究方向为配电自动化" ]
  [ "邹媛媛（1980- ），女，博士，上海交通大学电子信息与电气工程学院教授，主要研究方向为系统优化与控制" ]
  [ "李少远（1965- ），男，博士，上海交通大学电子信息与电气工程学院教授，主要研究方向为系统优化与控制" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61833012)
- DOI：10.11959/j.issn.2096-6652.202243
  中图分类号： TP273
- 网络出版日期：2022-09，
  
  纸质出版日期：2022-09-15
- 稿件说明：
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王云, 王美蕴, 周健, 等. 基于改进层次聚类和GL-APSO算法的配电网动态重构[J]. 智能科学与技术学报, 2022,4(3):410-417.

Yun WANG, Meiyun WANG, Jian ZHOU, et al. Dynamic configuration of distribution network based on improved hierarchical clustering and GL-APSO algorithm[J]. Chinese journal of intelligent science and technology, 2022, 4(3): 410-417.
王云, 王美蕴, 周健, 等. 基于改进层次聚类和GL-APSO算法的配电网动态重构[J]. 智能科学与技术学报, 2022,4(3):410-417. DOI： 10.11959/j.issn.2096-6652.202243.

Yun WANG, Meiyun WANG, Jian ZHOU, et al. Dynamic configuration of distribution network based on improved hierarchical clustering and GL-APSO algorithm[J]. Chinese journal of intelligent science and technology, 2022, 4(3): 410-417. DOI： 10.11959/j.issn.2096-6652.202243.

摘要

针对含分布式电源（DG）的配电网动态重构问题，提出考虑配电网负荷需求与 DG 出力动态变化的配电网动态重构方案。首先，基于不同时段的负荷特性与最优网络结构的综合相似性，提出了一种改进层次聚类的时段划分方法。在此基础上，提出了遗传学习自适应粒子群优化算法，实现以网络损耗最小为优化目标的动态重构优化计算。针对基本粒子群算法中缺乏速度动态调节、易陷入局部最优等问题，提出基于粒子个体最优位置的遗传学习方案增加搜索多样性，提高算法的全局搜索能力，并引入自适应惯性权值和加速系数以满足不同时期的寻优要求。最后，以IEEE 33节点配电系统为例进行仿真，验证了所提方法的有效性和优越性。

Abstract

Aiming at the problem of dynamic reconfiguration of distribution network with distributed generation (DG)

a dynamic distribution networks reconfiguration scheme considering the time-varying property of DG and distribution network load was proposed.Firstly

according to the comprehensive similarity between different periods based on both load characteristics and optimal network structure

an improved hierarchical clustering method was used to divide the reconstruction interval into segments.On this basis

the genetic learning adaptive particle swarm optimization algorithm was proposed to realize the dynamic reconstruction with minimum network loss.To tackle the shortcomings such as the lack of speed dynamic adjustment strategy and ease to fall into local optimum in basic particle swarm optimization algorithm

a genetic learning scheme based on the optimal position of individual particles was proposed to enhance diversity and improve global search ability.Adaptive inertia weight and acceleration coefficients were introduced to meet the optimization requirements of different periods.Finally

a simulation was carried out through the IEEE 33-bus distribution system as an example to verify the effectiveness and superiority of the proposed method.

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references

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