Robust Topology Design of Complex Infrastructure Systems

Abstract

Optimizing the topology of complex infrastructure systems can minimize the impact of cascading failures due to an initiating failure event. This paper presents a model-based design approach for the concept-stage robust design of complex infrastructure systems, as an alternative to modern network analysis methods. This approach focuses on system performance after cascading has occurred and examines design tradeoffs of the resultant (or degraded) system state. In this research, robustness is classically defined as the invariability of system performance due to uncertain failure events, implying that a robust network has the ability to meet minimum performance requirements despite the impact of cascading failures. This research is motivated by catastrophic complex infrastructure system failures such as the August 13th Blackout of 2003, highlighting the vulnerability of systems such as the North American power grid (NAPG). A mathematical model was developed using an adjacency matrix, where removing network connections simulates uncertain failure events. Performance degradation is iteratively calculated as failures cascade throughout the system, and robustness is measured by the lack of performance variability over multiple cascading failure scenarios. Two case studies are provided: an extrapolated IEEE 14 test bus and the Oregon State University (OSU) campus power network. The overarching goal of this research is to understand key system design tradeoffs between robustness, performance objectives, and cost, and explore the benefits of optimizing network topologies during the concept-stage design of these systems (e.g., microgrids).

Ensemble of Artificial Neural Networks for Approximating the Survival Signature of Critical Infrastructures

“…Water and gas distribution systems, energy transmission systems, transportation and telecommunication systems are critical infrastructures (CIs) [1], [2]. The reliability assessment of CIs is fundamental [3], [4], but, given their complexity [5], it cannot be carried out with classical methods.…”

“…Recently, the survival signature has been introduced in reliability assessment [6] for modelling dependencies [4], [7], [8], handling imprecise probabilities in a Bayesian scheme [9]- [11] and dealing with the complexity of the CIs [12]- [15]. The survival signature allows decoupling the structural information of the system from the probabilistic one [6], [16], which can be a major advantage for the reliability assessment of large systems.…”