Methodologies synthesis

This deliverable deals with the modelling and analysis of interdependencies between critical infrastructures, focussing attention on two interdependent infrastructures studied in the context of CRUTIAL: the electric power infrastructure and the information infrastructures supporting management, control and maintenance functionality. The main objectives are: 1) investigate the main challenges to be addressed for the analysis and modelling of interdependencies, 2) review the modelling methodologies and tools that can be used to address these challenges and support the evaluation of the impact of interdependencies on the dependability and resilience of the service delivered to the users, and 3) present the preliminary directions investigated so far by the CRUTIAL consortium for describing and modelling interdependencies

List of requirements on formalisms and selection of appropriate tools

This deliverable reports on the activities for the set-up of the modelling environments for the evaluation activities of WP5. To this objective, it reports on the identified modelling peculiarities of the electric power infrastructure and the information infrastructures and of their interdependencies, recalls the tools that have been considered and concentrates on the tools that are, and will be, used in the project: DrawNET, DEEM and EPSys which have been developed before and during the project by the partners, and M\uf6bius and PRISM, developed respectively at the University of Illinois at Urbana Champaign and at the University of Birmingham (and recently at the University of Oxford)

Quantitative dependability and interdependency models for large-scale cyber-physical systems

Cyber-physical systems link cyber infrastructure with physical processes through an integrated network of physical components, sensors, actuators, and computers that are interconnected by communication links. Modern critical infrastructures such as smart grids, intelligent water distribution networks, and intelligent transportation systems are prominent examples of cyber-physical systems. Developed countries are entirely reliant on these critical infrastructures, hence the need for rigorous assessment of the trustworthiness of these systems. The objective of this research is quantitative modeling of dependability attributes -- including reliability and survivability -- of cyber-physical systems, with domain-specific case studies on smart grids and intelligent water distribution networks. To this end, we make the following research contributions: i) quantifying, in terms of loss of reliability and survivability, the effect of introducing computing and communication technologies; and ii) identifying and quantifying interdependencies in cyber-physical systems and investigating their effect on fault propagation paths and degradation of dependability attributes. Our proposed approach relies on observation of system behavior in response to disruptive events. We utilize a Markovian technique to formalize a unified reliability model. For survivability evaluation, we capture temporal changes to a service index chosen to represent the extent of functionality retained. In modeling of interdependency, we apply correlation and causation analyses to identify links and use graph-theoretical metrics for quantifying them. The metrics and models we propose can be instrumental in guiding investments in fortification of and failure mitigation for critical infrastructures. To verify the success of our proposed approach in meeting these goals, we introduce a failure prediction tool capable of identifying system components that are prone to failure as a result of a specific disruptive event. Our prediction tool can enable timely preventative actions and mitigate the consequences of accidental failures and malicious attacks --Abstract, page iii

Dependability analysis and recovery support for smart grids

The increasing scale and complexity of power grids exacerbate concerns about failure propagation. A single contingency, such as outage of a transmission line due to overload or weather-related damage, can cause cascading failures that manifest as blackouts. One objective of smart grids is to reduce the likelihood of cascading failure through the use of power electronics devices that can prevent, isolate, and mitigate the effects of faults. Given that these devices are themselves prone to failure, we seek to quantify the effects of their use on dependability attributes of smart grid. This thesis articulates analytical methods for analyzing two dependability attributes - reliability and survivability - and proposes a recovery strategy that limits service degradation. Reliability captures the probability of system-level failure; Survivability describes degraded operation in the presence of a fault. System condition and service capacity are selected as measures of degradation. Both reliability and survivability are evaluated using N-1 contingency analysis. Importance analysis is used to determine a recovery strategy that maintains the highest survivability in the course of the recovery process. The proposed methods are illustrated by application to the IEEE 9-bus test system, a simple model system that allows for clear articulation of the process. Simulation is used to capture the effect of faults in both physical components of the power grid and the cyber infrastructure that differentiates it as a smart grid --Abstract, page iii

Reliability of Critical Infrastructure Networks: Challenges

Critical infrastructures form a technological skeleton of our world by providing us with water, food, electricity, gas, transportation, communication, banking, and finance. Moreover, as urban population increases, the role of infrastructures become more vital. In this paper, we adopt a network perspective and discuss the ever growing need for fundamental interdisciplinary study of critical infrastructure networks, efficient methods for estimating their reliability, and cost-effective strategies for enhancing their resiliency. We also highlight some of the main challenges arising on this way, including cascading failures, feedback loops, and cross-sector interdependencies.Comment: 12 pages, 3 figures, submitted for publication in the ASCE (American Society of Civil Engineers) technical repor

Application of the D3H2 Methodology for the Cost-Effective Design of Dependable Systems

The use of dedicated components as a means of achieving desirable levels of fault tolerancein a system may result in high costs. A cost effective way of restoring failed functions is to use heterogeneous redundancies: components that, besides performing their primary intended design function, can also restore compatible functions of other components. In this paper, we apply a novel design methodology called D3H2 (aDaptive Dependable Design for systems with Homogeneous and Heterogeneous redundancies) to assist in the systematic identification of heterogeneous redundancies, the design of hardware/software architectures including fault detection and reconfiguration, and the systematic dependability and cost assessments of the system. D3H2 integrates parameter uncertainty and criticality analyses to model inexact failure data in dependability assessment. The application to a railway case study is presented with a focus on analysing different reconfiguration strategies as well as types and levels of redundancies

Quantifying Tipping Risks in Power Grids and beyond

Critical transitions, ubiquitous in nature and technology, necessitate anticipation to avert adverse outcomes. While many studies focus on bifurcation-induced tipping, where a control parameter change leads to destabilization, alternative scenarios are conceivable, e.g. noise-induced tipping by an increasing noise level in a multi-stable system. Although the generating mechanisms can be different, the observed time series can exhibit similar characteristics. Therefore, we propose a Bayesian Langevin approach, implemented in an open-source tool, which is capable of quantifying both deterministic and intrinsic stochastic dynamics simultaneously. After a detailed proof of concept, we analyse two bus voltage frequency time series of the historic North America Western Interconnection blackout on 10th August 1996. Our results unveil the intricate interplay of changing resilience and noise influence. A comparison with the blackout's timeline supports our frequency dynamics' Langevin model, with the BL-estimation indicating a permanent grid state change already two minutes before the officially defined triggering event. A tree-related high impedance fault or sudden load increases may serve as earlier triggers during this event, as suggested by our findings. This study underscores the importance of distinguishing destabilizing factors for a reliable anticipation of critical transitions, offering a tool for better understanding such events across various disciplines.Comment: In total: 20 pages, 6 figures. Supplementary material, data and code available online on github. Enable cross-referencing between main article and supplement in the same folder by renaming them to Quantifying_Tipping_Risks.pdf and SI_Quantifying_Tipping_Risks.pdf, respectivel

Simulation Methods for the Analysis of Complex Systems

open access bookEveryday systems like communication, transportation, energy and industrial systems are an indispensable part of our daily lives. Several methods have been developed for their reliability assessment—while analytical methods are computationally more efficient and often yield exact solutions, they are unable to account for the structural and functional complexities of these systems. These complexities often require the analyst to make unrealistic assumptions, sometimes at the expense of accuracy. Simulation-based methods, on the other hand, can account for these realistic operational attributes but are computationally intensive and usually system-specific. This chapter introduces two novel simulation methods: load flow simulation and survival signature simulation which together address the limitations of the existing analytical and simulation methods for the reliability analysis of large systems