Consensus Clustering for Bi-objective Power Network Partition

作     者：Yi Wang Luzian Lebovitz Kedi Zheng Yao Zhou Yi Wang;Luzian Lebovitz;Kedi Zheng;Yao Zhou

作者机构：the Power Systems LaboratoryETH Zurich8092 ZurichSwitzerland Department of Elctrical EngineeringTsinghua UniversityBeijing 100084China School of Engineeringthe University of EdinburghEH93FBEdinburghUK 

出 版 物：《CSEE Journal of Power and Energy Systems》 (中国电机工程学会电力与能源系统学报（英文）)

年 卷 期：2022年第8卷第4期

页      面：973-982页

核心收录：

学科分类：0820[工学-石油与天然气工程] 0808[工学-电气工程] 080802[工学-电力系统及其自动化] 08[工学] 

基　　金：supported in part by the National Key R&D Program of China(No.2016YFB0900100) the Major Smart Grid Joint Project of National Natural Science Foundation of China and State Grid(No.U1766212) 

主　　题：Consensus clustering network partition bi-objective partition machine learning 

摘      要：Partitioning a complex power network into a number of sub-zones can help realize a divide-and-conquer’management structure for the whole system,such as voltage and reactive power control,coherency identification,power system restoration,*** partitioning methods have been proposed by defining various distances,applying different clustering methods,or formulating varying optimization models for one specific ***,a power network partition may serve two or more objectives,where a trade-off among these objectives is *** paper proposes a novel weighted consensus clustering-based approach for bi-objective power network *** varying the weights of different partitions for different objectives,Pareto improvement can be explored based on the node-based and subset-based consensus clustering *** studies on the IEEE 300-bus test system are conducted to verify the effectiveness and superiority of our proposed method.