Resilience Decision-Making for Complex Systems

Abstract

Complex systems—such as gas turbines, industrial plants, and infrastructure networks—are of paramount importance to modern societies. However, these systems are subject to various threats. Novel research does not only focus on monitoring and improving the robustness and reliability of systems but also focus on their recovery from adverse events. The concept of resilience encompasses these developments. Appropriate quantitative measures of resilience can support decision-makers seeking to improve or to design complex systems. In this paper, we develop comprehensive and widely adaptable instruments for resilience-based decision-making. Integrating an appropriate resilience metric together with a suitable systemic risk measure, we design numerically efficient tools aiding decision-makers in balancing different resilience-enhancing investments. The approach allows for a direct comparison between failure prevention arrangements and recovery improvement procedures, leading to optimal tradeoffs with respect to the resilience of a system. In addition, the method is capable of dealing with the monetary aspects involved in the decision-making process. Finally, a grid search algorithm for systemic risk measures significantly reduces the computational effort. In order to demonstrate its wide applicability, the suggested decision-making procedure is applied to a functional model of a multistage axial compressor, and to the U-Bahn and S-Bahn system of Germany's capital Berlin.

A machine learning approach for real‐time selection of preventive actions improving power network resilience

“…Standard deviation of ELNS is also reduced for SVM compared to no action (Table 1) except for the medium impact and high impact scenarios. Note that this is purely an effect of the reduced standard deviation 𝜎 LL e for preventive actions, as discussed in Section 3, and 𝜎 LL e is not part of the definition of the ideal action in (11). Thus, further reductions in the standard deviation could be achieved by extending this definition, however, this was not part of this study.…”

“…As discussed in the previous section, the event parameters and SP, which can be calculated based on the event parameters, are used as predictors. For each event, the ideal action is determined based on (11), that is, the action that leads to the minimum ELNS. ELNS is calculated for all events and actions using an MC simulation of 100 event outcomes, that is, randomly generated sets of lines damaged by the event.…”

“…The decision whether to apply a preventive action, and, if several alternative actions are available, which one to apply, is a non-trivial classification problem, and must consider the stochastic nature of extreme events. Established resilience enhancement frameworks usually include an optimisation problem for a resilience metric, for example, the energy not supplied [8,10,11]. However, calculating these metrics and solving the optimisation problem is time-consuming, and often involves Monte Carlo (MC) simulations.…”

Resilience Assessment and Importance Measure for Interdependent Critical Infrastructures

“…In recent years, diverse severe disruptive events occurred to ICIs around the world and caused immense economic losses and serious casualties, e.g., 9/11 terrorist attack, 2003 country-wide blackout in Italy, 2010 Chilean earthquake and tsunami, 2011 Tohoku earthquake in Japan, 2017 Harvey hurricane in North America, etc. Facing these catastrophic events, great emphasis has been placed on studies of system resilience [8][9][10]. Many definitions of system resilience have been proposed from various perspectives [11].…”

Resilience Assessment: A Performance-Based Importance Measure

“…As Figure 1 shows, the reliability of the system resilience in the original stable state before a disruption occurs starts from the time to (normal or baseline state). Reliability is defined as the probability that a system can perform a required function under given conditions at a given instant of time or over a given time interval, assuming the required external resources are provided [3][4][5][6][7][8][9][10]. At the time of t 1 the system will be hit by a disruptive event (overstress such as an earthquake).…”