Extracting rare failure events in composite system reliability evaluation via subset simulation
Extracting rare failure events in composite system reliability evaluation via subset simulation
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DOI：
10.1109/tpwrs.2014.2327753
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发表时间：
2015-03
期刊：
影响因子：
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通讯作者：
Bowen Hua;Z. Bie;S. Au;Wenyuan Li;Xifan Wang
中科院分区：
文献类型：
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作者：
Bowen Hua;Z. Bie;S. Au;Wenyuan Li;Xifan Wang
仅提供摘要形式。提出了一种基于子集模拟的组合系统可靠性评估方法。其中心思想是，一个小的故障概率可以表示为一个产品的条件概率较大，从而把模拟一个罕见的故障事件的问题变成几个更频繁的中间故障事件的条件模拟。在现有的方法中，系统状态简单地以二进制安全/故障的方式进行评估。为了适应子集模拟的背景下，充分的系统状态的参数化与基于线性规划的度量，从而允许自适应选择的中间故障事件。以这些事件为条件的样本由马尔可夫链蒙特卡罗模拟产生。所提出的方法在仿真之前不需要先验信息。可再生能源的不同模式也可以容纳。数值试验表明，该方法比标准的蒙特卡罗模拟有明显的效率，特别是在模拟罕见的故障事件。
Summary form only given. This paper proposes an efficient method for evaluating composite system reliability via subset simulation. The central idea is that a small failure probability can be expressed as a product of larger conditional probabilities, thereby turning the problem of simulating a rare failure event into several conditional simulations of more frequent intermediate failure events. In existing methods, system states are simply assessed in a binary secure/failure manner. To fit into the context of subset simulation, the adequacy of system states is parametrized with a metric based on linear programming, thus allowing for an adaptive choice of intermediate failure events. Samples conditional on these events are generated by Markov chain Monte Carlo simulation. The proposed method requires no prior information before simulation. Different models for renewable energy sources can also be accommodated. Numerical tests show that this method is significantly more efficient than standard Monte Carlo simulation, especially for simulating rare failure events.