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Dynamic configuration of distribution network based on improved hierarchical clustering and GL-APSO algorithm
- Chinese Journal of Intelligent Science and Technology Vol. 4, Issue 3, Pages: 410-417(2022)
- 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：
- 基金信息：
DOI：10.11959/j.issn.2096-6652.202243
CLC： TP273-
Published Online：2022-09，
Published：15 September 2022
- 稿件说明：
移动端阅览
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.
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.
关键词
Keywords
references
LEE C , LIU C , MEHROTRA S , et al . Robust distribution network reconfiguration [J ] . IEEE Transactions on Smart Grid , 2015 , 6 ( 2 ): 836 - 842 .
SHIRMOHAMMADI D , HONG H W . Reconfiguration of electric distribution networks for resistive line losses reduction [J ] . IEEE Transactions on Power Delivery , 1989 , 4 ( 2 ): 1492 - 1498 .
GUO Z M , LI X L , GENG J C , et al . Distribution network reconfiguration based on opposition learning genetic algorithm [C ] // Proceedings of 2018 IEEE Innovative Smart Grid Technologies - Asia . Piscataway:IEEE Press , 2018 : 345 - 349 .
徐渊 . 改进粒子群算法在含分布式电源配电网优化重构中的应用 [J ] . 电测与仪表 , 2021 , 58 ( 3 ): 98 - 104 .
XU Y . Application of improved particle swarm optimization in distribution network reconfiguration with distributed generation [J ] . Electrical Measurement ＆ Instrumentation , 2021 , 58 ( 3 ): 98 - 104 .
吴建旭 , 于永进 . 基于改进和声搜索算法的多目标配电网重构优化 [J ] . 电力系统保护与控制 , 2021 , 49 ( 19 ): 78 - 86 .
WU J X , YU Y J . Multi-objective distribution network reconfiguration optimization based on an improved harmony searchalgorithm [J ] . Power System Protection and Control , 2021 , 49 ( 19 ): 78 - 86 .
TYAGI A , VERMA A , BIJWE P R . Reconfiguration for loadabilitylimit enhancement of distribution systems [J ] . IET Generation,Transmission ＆ Distribution , 2018 , 12 ( 1 ): 88 - 93 .
LANDEROS A , KOZIEL S , ABDEL-FATTAH M F , . Distribution network reconfiguration using feasibility-preserving evolutionary optimization [J ] . Journal of Modern Power Systems and Clean Energy , 2019 , 7 ( 3 ): 589 - 598 .
徐泽 , 杨伟 , 张文强 , 等 . 基于连锁环网与改进离散粒子群算法的多目标配电网重构 [J ] . 电力系统保护与控制 , 2021 , 49 ( 6 ): 114 - 123 .
XU Z , YANG W , ZHANG W Q , et al . Multi-objective distribution network reconfiguration based on chain loops and improved binary particle swarm optimization [J ] . Power System Protection and Control , 2021 , 49 ( 6 ): 114 - 123 .
杨胡萍 , 彭云焰 , 熊宁 . 配网动态重构的静态解法 [J ] . 电力系统保护与控制 , 2009 , 37 ( 8 ): 53 - 57 .
YANG H P , PENG Y Y , XIONG N . A static method for distribution network dynamic reconfiguration [J ] . Power System Protection and Control , 2009 , 37 ( 8 ): 53 - 57 .
SHARIATKHAH M H , HAGHIFAM M R , SALEHI J , et al . Duration based reconfiguration of electric distribution networks using dynamic programming and harmony search algorithm [J ] . International Journal of Electrical Power ＆ Energy Systems , 2012 , 41 ( 1 ): 1 - 10 .
周洁洁 , 阙凌燕 , 王良毅 , 等 . 采用改进最优模糊 C 均值聚类和改进和声搜索算法的配电网动态重构 [J ] . 机电工程 , 2015 , 32 ( 4 ): 531 - 536 , 543 .
ZHOU J J , QUE L Y , WANG L Y , et al . Dynamic reconfiguration of distribution network based on improved optimal fuzzy C-means clustering and improved harmony search algorithm [J ] . Journal of Mechanical ＆ Electrical Engineering , 2015 , 32 ( 4 ): 531 - 536 , 543 .
田书欣 , 刘浪 , 魏书荣 , 等 . 基于改进灰狼优化算法的配电网动态重构 [J ] . 电力系统保护与控制 , 2021 , 49 ( 16 ): 1 - 11 .
TIAN S X , LIU L , WEI S R , et al . Dynamic reconfiguration of a distribution network based on an improved grey wolf optimization algorithm [J ] . Power System Protection and Control , 2021 , 49 ( 16 ): 1 - 11 .
董志辉 , 林凌雪 . 基于改进模糊C均值聚类时段划分的配电网动态重构 [J ] . 电网技术 , 2019 , 43 ( 7 ): 2299 - 2305 .
DONG Z H , LIN L X . Dynamic reconfiguration of distribution network based on improved fuzzy C-means clustering of time division [J ] . Power System Technology , 2019 , 43 ( 7 ): 2299 - 2305 .
王瑞峰 , 王庆荣 . 基于改进双层聚类多目标优化的配电网动态重构 [J ] . 电力系统保护与控制 , 2019 , 47 ( 21 ): 92 - 99 .
WANG R F , WANG Q R . Multi-objective optimization of dynamic reconfiguration of distribution network based on improved Bilayer clustering [J ] . Power System Protection and Control , 2019 , 47 ( 21 ): 92 - 99 .
AH KING R T F , RADHA B , RUGHOOPUTH H C S . A real-parameter genetic algorithm for optimal network reconfiguration [C ] // Proceedings of IEEE International Conference on Industrial Technology . Piscataway:IEEE Press , 2003 : 54 - 59 .
GONG Y J , LI J J , ZHOU Y C , et al . Genetic learning particle swarm optimization [J ] . IEEE Transactions on Cybernetics , 2016 , 46 ( 10 ): 2277 - 2290 .
RATNAWEERA A , HALGAMUGE S K , WATSON H C . Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients [J ] . IEEE Transactions on Evolutionary Computation , 2004 , 8 ( 3 ): 240 - 255 .
赵静翔 , 牛焕娜 , 王钰竹 . 基于信息熵时段划分的主动配电网动态重构 [J ] . 电网技术 , 2017 , 41 ( 2 ): 402 - 408 .
ZHAO J X , NIU H N , WANG Y Z . Dynamic reconfiguration of active distribution network based on information entropy of time intervals [J ] . Power System Technology , 2017 , 41 ( 2 ): 402 - 408 .
梁杉 , 毛弋 , 刘小丽 , 等 . 改进和声搜索算法用于配电网重构 [J ] . 电力系统及其自动化学报 , 2017 , 29 ( 3 ): 90 - 95 .
LIANG S , MAO Y , LIU X L , et al . Improved harmony search algorithm for distribution network reconfiguration [J ] . Proceedings of the CSU-EPSA , 2017 , 29 ( 3 ): 90 - 95 .
徐小琴 , 王博 , 赵红生 , 等 . 基于布谷鸟搜索和模拟退火算法的两电压等级配网重构方法 [J ] . 电力系统保护与控制 , 2020 , 48 ( 11 ): 84 - 91 .
XU X Q , WANG B , ZHAO H S , et al . Reconfiguration of two-voltage distribution network based on cuckoo search and simulated annealing algorithm [J ] . Power System Protection and Control , 2020 , 48 ( 11 ): 84 - 91 .
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