Multi-objective Oriented Search Algorithm for Multi-objective Reactive Power Optimization

被引：0
|
作者：
Zhang, Xuexia [1 ]
Chen, Weirong [1 ]
机构：
[1] SW Jiaotong Univ, Sch Elect Engn, Chengdu 610031, Peoples R China
来源：
EMERGING INTELLIGENT COMPUTING TECHNOLOGY AND APPLICATIONS: WITH ASPECTS OF ARTIFICIAL INTELLIGENCE | 2009年 / 5755卷
关键词：
MOOSA; Pareto-optimal solutions; Pareto front; multi-objective reactive power optimization; FLOW LITERATURE;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents a novel algorithm, multi-objective oriented search algorithm (MOOSA), to deal with the problem of multi-objective reactive power optimization in power system. The multi-objective oriented search algorithm has strong ability to search optimal solutions and well-distributed solutions in Pareto front. The results show that the proposed algorithm is able to balance the multi objects in multi-objective reactive power optimization through the simulations on IEEE 30-bus testing system. The paper concludes that MOOSA is an effective tool to handle the problem of multi-objective reactive power optimization.
引用
收藏
页码：232 / 241
页数：10
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