Dynamic configuration of distribution network based on improved hierarchical clustering and GL-APSO algorithm
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...
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| Format: | Article |
| Language: | zho |
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POSTS&TELECOM PRESS Co., LTD
2022-09-01
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| Series: | 智能科学与技术学报 |
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| Online Access: | http://www.cjist.com.cn/thesisDetails#10.11959/j.issn.2096-6652.202243 |
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| _version_ | 1846171093885255680 |
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| author | Yun WANG Meiyun WANG Jian ZHOU Yuanyuan ZOU Shaoyuan LI |
| author_facet | Yun WANG Meiyun WANG Jian ZHOU Yuanyuan ZOU Shaoyuan LI |
| author_sort | Yun WANG |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0f9a90c0e0ac4266a7e5003a31d125aa |
| institution | Kabale University |
| issn | 2096-6652 |
| language | zho |
| publishDate | 2022-09-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 智能科学与技术学报 |
| spelling | doaj-art-0f9a90c0e0ac4266a7e5003a31d125aa2024-11-11T06:53:27ZzhoPOSTS&TELECOM PRESS Co., LTD智能科学与技术学报2096-66522022-09-01441041759641237Dynamic configuration of distribution network based on improved hierarchical clustering and GL-APSO algorithmYun WANGMeiyun WANGJian ZHOUYuanyuan ZOUShaoyuan LIAiming 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.http://www.cjist.com.cn/thesisDetails#10.11959/j.issn.2096-6652.202243distribution network dynamic reconfiguration;time division;hierarchical clustering;genetic learning adaptive particle swarm optimization algorithm |
| spellingShingle | Yun WANG Meiyun WANG Jian ZHOU Yuanyuan ZOU Shaoyuan LI Dynamic configuration of distribution network based on improved hierarchical clustering and GL-APSO algorithm 智能科学与技术学报 distribution network dynamic reconfiguration;time division;hierarchical clustering;genetic learning adaptive particle swarm optimization algorithm |
| title | Dynamic configuration of distribution network based on improved hierarchical clustering and GL-APSO algorithm |
| title_full | Dynamic configuration of distribution network based on improved hierarchical clustering and GL-APSO algorithm |
| title_fullStr | Dynamic configuration of distribution network based on improved hierarchical clustering and GL-APSO algorithm |
| title_full_unstemmed | Dynamic configuration of distribution network based on improved hierarchical clustering and GL-APSO algorithm |
| title_short | Dynamic configuration of distribution network based on improved hierarchical clustering and GL-APSO algorithm |
| title_sort | dynamic configuration of distribution network based on improved hierarchical clustering and gl apso algorithm |
| topic | distribution network dynamic reconfiguration;time division;hierarchical clustering;genetic learning adaptive particle swarm optimization algorithm |
| url | http://www.cjist.com.cn/thesisDetails#10.11959/j.issn.2096-6652.202243 |
| work_keys_str_mv | AT yunwang dynamicconfigurationofdistributionnetworkbasedonimprovedhierarchicalclusteringandglapsoalgorithm AT meiyunwang dynamicconfigurationofdistributionnetworkbasedonimprovedhierarchicalclusteringandglapsoalgorithm AT jianzhou dynamicconfigurationofdistributionnetworkbasedonimprovedhierarchicalclusteringandglapsoalgorithm AT yuanyuanzou dynamicconfigurationofdistributionnetworkbasedonimprovedhierarchicalclusteringandglapsoalgorithm AT shaoyuanli dynamicconfigurationofdistributionnetworkbasedonimprovedhierarchicalclusteringandglapsoalgorithm |