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文章摘要
基于MAQPSO 的电力系统无功优化研究
Power System Reactive Power Optimization Based on Multi Agent System Quantum Particle Swarm Algorithm
Received:July 03, 2014  Revised:October 10, 2014
DOI:
中文关键词: 无功优化  多智能体系统(MAS)  量子粒子群算法(QPSO)  多智能体量子粒子群算法(MAQPSO)
英文关键词: reactive power optimization  multi agent system  quantum particle swarm optimization algorithm  multi agent system quantum particle swarm algorithm
基金项目:广东省自然科学基金资助项目 (S2012040007895);广东省电网公司科技项目(K-GD2013-0789 )
Author NameAffiliationE-mail
LI Gao-cheng Maoming Power Supply Bureau,Guangdong Maoming 475453171@qq.com 
MENG An-bo* Guangdong University of Technology,Guangdong Guangzhou 475453171@qq.com 
LI Chao Maoming Power Supply Bureau,Guangdong Maoming  
LI Yang Guangdong University of Technology,Guangdong Guangzhou  
CHEN Si-zhe Guangdong University of Technology,Guangdong Guangzhou  
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中文摘要:
      本文提出一种多智能体量子粒子群优化算法(Multi Agent Quantum Particle Swam Optimization,MAQPSO)求解电力系统无功优化问题,改善了传统量子粒子群算法后期收敛速度慢、易陷入局部最优解等缺点。该算法结合了量子粒子群算法和多智能体进化思想,每一个Agent相当于量子粒子群优化算法中的一个粒子,通过Agent的邻域竞争、自学习等操作,使得算法能够更迅速、更精确地收敛到全局最优解。通过对IEEE14、30、57和118节点系统的优化仿真,结果表明该算法有收敛精度高、寻优速度快等优点。
英文摘要:
      This paper proposed a novel quantum particle swam optimization algorithm based on multi agent system(MAQPSO) approach to the reactive power optimization.The algorithm overcome the defect of conventional quantum particle swam optimization slow convergent speed and easy convergenve to local minimum point of error function on later. This algorithm combined quantum particle swam optimization algorithm with multi-agent system platform.Every Agent serves as a particle of quantum particle swam,competition by the Agent of the neighborhood and selfstudy,the algorithm can more quickly and more accurately converge to global optimal solution.Based on IEEE14, 30, 57 and 118 nodes system optimization simulation, the results show that the algorithm has high convergence precision and speed of optimization.
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