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

页码：232 / 241

页数：10

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