Hybrid GA–PSO for optimal placement of static VAR compensators in power system

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• 作者：Abdelmalek Gacem ; Djilani Benattous
• 关键词：FACTS ; SVC ; HGAPSO ; PSO ; GA ; Multi ; objective
• 刊名：International Journal of System Assurance Engineering and Management
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：8
• 期：1-supp
• 页码：247-254
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Quality Control, Reliability, Safety and Risk; Engineering Economics, Organization, Logistics, Marketing;
• 出版者：Springer India
• ISSN：0976-4348
• 卷排序：8

文摘

In recent years, genetic algorithm (GA), particle swarm optimization (PSO) and hybrid genetic algorithm particle swarm optimization (HGAPSO) have attracted considerable attention among various modern heuristic optimization techniques. In this study the HGAPSO, PSO and GA optimization techniques are used for to search the optimal placement and sizing of static VAR compensator (SVC) in power system. The objective function is defined for reducing power loss, voltage deviation and investment costs of SVC. The effectiveness of the proposed hybrid based approach is applied and demonstrated on IEEE 30 Bus network. The results show that the proposed hybrid HGAPSO compared with PSO and GA optimization for performs and giving better solution.