基于二元粒子群优化的状态空间剪枝可靠性评估

R. Green, Lingfeng Wang, Mansoor Alam, C. Singh
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引用次数: 26

摘要

状态空间剪枝是一种用于提高蒙特卡罗仿真(MCS)计算复合电力系统可靠性指标的计算效率和收敛性的方法。这种方法通过修剪状态空间来提高MCS的性能,从而创建一个比原始状态空间具有更高故障状态密度的新状态空间。先前提出的一种提高MCS效率的方法是使用基于种群的智能搜索(PIS),特别是遗传算法(GA)来修剪状态空间。本文将这些思想扩展到另一种PIS方法:二进制粒子群优化(BPSO)。研究结果表明,BPSO在修剪状态空间和提高MCS收敛性方面是非常有效的。采用IEEE可靠性测试系统对该方法进行了测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
State space pruning for reliability evaluation using binary particle swarm optimization
State space pruning is a methodology that has been successfully applied to improve the computational efficiency and convergence of Monte Carlo Simulation (MCS) when computing the reliability indices of composite power systems. This methodology increases performance of MCS by pruning state spaces in such a manner that a new state space with a higher density of failure states than the original state space is created. A method that was previously proposed to increase the efficiency of MCS was the use of Population-based Intelligent Search (PIS), specifically Genetic Algorithms (GA), to prune the state space. This paper extends these ideas to another PIS methodology: Binary Particle Swarm Optimization (BPSO). The results of this study show that BPSO is highly effective in pruning the state space and improving the convergence of MCS. This method is tested using the IEEE reliability test system.
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