Model uncertainty and the management of a system of infrastructure facilities

The network-level infrastructure management problem involves selecting and scheduling maintenance, repair, and rehabilitation (MR&R) activities on networks of infrastructure facilities so as to maintain the level of service provided by the network in a cost-effective manner. This problem is frequently formulated as a Markov decision problem (MDP) solved via linear programming (LP). The conditions of facilities are represented by elements of discrete condition rating sets, and transition probabilities are employed to describe deterioration processes. Epistemic and parametric uncertainties not considered within the standard MDP/LP framework are associated with the transition probabilities used in infrastructure management optimization routines. This paper contrasts the expected costs incurred when model uncertainty is ignored with those incurred when this uncertainty is explicitly considered using robust optimization. A case study involving a network-level pavement management MDP/LP problem demonstrates how explicitly considering uncertainty may limit worst-case MR&R expenditures. The methods and results can also be used to identify the costs of uncertainty in transition probability matrices used in infrastructure management systems.     

IGCT无吸收和串联工作电路仿真的新型有效的物理模型

文章讨论了用Saber MAST软件完成电路仿真,并精确计算集成门极换流晶闸管(IGCT)静态和动态性能的新物理模型.通过仿真结果与硬开关无吸收工作中4.5 kV/3 kA IGCT的试验结果的比较验证了该模型.此外,文章还分析了2个串联IGCT开关的仿真结果,特别是在忽略无吸收电路时器件特性和门极驱动存在的必然差别引起的关于开关之间阻断电压非均衡分布的问题.还给出了计算吸收容量的一种想法.