Multi-objective VAr planning with SVC for a large power system using PSO and GA

被引：16

作者：

Farsangi, Malibe M. ^{[1
]}

Nezamabadi-Pour, Hossien ^{[1
]}

Lee, Kwang Y. ^{[2
]}

机构：

[1] Kerman Univ, Kerman, Iran

[2] Penn State Univ, Dept Elect Engn, University Pk, PA 16802 USA

来源：

2006 IEEE/PES POWER SYSTEMS CONFERENCE AND EXPOSITION. VOLS 1-5 | 2006年

关键词：

genetic algorithm; particle swarm optimization; SVC; multiobjective optimization; fuzzy;

D O I：

10.1109/PSCE.2006.296319

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Particle Swarm Optimization (PSO) Algorithm is used for planning the Static Var Compensator (SVC) in a large-scale power system. The primary function of an SVC is to improve transmission system voltage, thereby enhancing the maximum power transfer limit. To enhance voltage stability, the planning problem is formulated as a multiobjective optimization problem for maximizing fuzzy performance indices. The multiobjective var planning problem in a large-scale power system is solved by the fuzzy PSO with very encouraging results, and the results are compared with those obtained by the Genetic Algorithm (GA).

引用

页码：274 / +

页数：2

共 19 条

[1]

[Anonymous], 1989, GENETIC ALGORITHM SE

[2] Optimal multiobjective SVC planning for voltage stability enhancement [J].