文章基本信息

标题：Gravity Local Search Inspired Particle Swarm Algorithm for Economic Power Dispatch Planning Problem in Small Scale System
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作者：Navpreet Singh Tung ; Sandeep Chakravorty ; Harkamal Singh Bhullar 等
期刊名称：International Journal of Grid and Distributed Computing
印刷版ISSN：2005-4262
出版年度：2016
卷号：9
期号：5
页码：111-124
DOI：10.14257/ijgdc.2016.9.5.11
出版社：SERSC
摘要：This research presents novel Particle swarm optimization inspired by gravitational based search method to solve active power dispatch problem in electrical power system planning. The proposed PSO utilizes the operator of social thinking coupled with search capacity of gravity inspired algorithm to formulate and develop technique for active power dispatch problem to satisfy power demand requirements. Optimal scheduling of generators and system constraints to match load demand and losses is successfully done with proposed method. Total operating cost is minimized satisfying various bounds of system with proposed method. Exploration and convergence efficiency are evaluated to checklist the computational efficiency and robustness of the proposed technique. The suggested technique is tested and evaluated on different test systems comprises three, five, six test systems. Test results are compared with other techniques presented in literature .Investigations shows promising results which further benchmark the effectiveness of proposed method to solve complex optimization non linear problems.
关键词：Unit Commitment (UC); Economic Power Dispatch (EPD); Gravity Local ; Search Particle Swarm Algorithm (GLSPSA)