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A Critical Review of Active Distribution Network Reconfiguration:Concepts,Development,and Perspectives
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作者 Bo Yang Rui zhang +10 位作者 Jie zhang Xianlong Cheng Jiale Li Yimin Zhou Yuanweiji Hu Bin He gongshuai zhang Xiuping Du Si Ji Yiyan Sang Zhengxun Guo 《Energy Engineering》 EI 2024年第12期3487-3547,共61页
In recent years,the large-scale grid connection of various distributed power sources has made the planning and operation of distribution grids increasingly complex.Consequently,a large number of active distribution ne... In recent years,the large-scale grid connection of various distributed power sources has made the planning and operation of distribution grids increasingly complex.Consequently,a large number of active distribution network reconfiguration techniques have emerged to reduce system losses,improve system safety,and enhance power quality via switching switches to change the system topology while ensuring the radial structure of the network.While scholars have previously reviewed these methods,they all have obvious shortcomings,such as a lack of systematic integration of methods,vague classification,lack of constructive suggestions for future study,etc.Therefore,this paper attempts to provide a comprehensive and profound review of 52 methods and applications of active distribution network reconfiguration through systematic method classification and enumeration.Specifically,these methods are classified into five categories,i.e.,traditional methods,mathematical methods,meta-heuristic algorithms,machine learning methods,and hybrid methods.A thorough comparison of the various methods is also scored in terms of their practicality,complexity,number of switching actions,performance improvement,advantages,and disadvantages.Finally,four summaries and four future research prospects are presented.In summary,this paper aims to provide an up-to-date and well-rounded manual for subsequent researchers and scholars engaged in related fields. 展开更多
关键词 Active distribution network RECONFIGURATION meta-heuristic algorithm machine learning
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