3,070 research outputs found

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

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    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

    Component Outage Estimation based on Support Vector Machine

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    Predicting power system component outages in response to an imminent hurricane plays a major role in preevent planning and post-event recovery of the power system. An exact prediction of components states, however, is a challenging task and cannot be easily performed. In this paper, a Support Vector Machine (SVM) based method is proposed to help estimate the components states in response to anticipated path and intensity of an imminent hurricane. Components states are categorized into three classes of damaged, operational, and uncertain. The damaged components along with the components in uncertain class are then considered in multiple contingency scenarios of a proposed Event-driven Security-Constrained Unit Commitment (E-SCUC), which considers the simultaneous outage of multiple components under an N-m-u reliability criterion. Experimental results on the IEEE 118-bus test system show the merits and the effectiveness of the proposed SVM classifier and the E-SCUC model in improving power system resilience in response to extreme events

    Energy security issues in contemporary Europe

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    Throughout the history of mankind, energy security has been always seen as a means of protection from disruptions of essential energy systems. The idea of protection from disorders emerged from the process of securing political and military control over energy resources to set up policies and measures on managing risks that affect all elements of energy systems. The various systems placed in a place to achieve energy security are the driving force towards the energy innovations or emerging trends in the energy sector. Our paper discusses energy security status and innovations in the energy sector in European Union (EU). We analyze the recent up-to-date developments of the energy policy and exploitation of energy sources, as well as scrutinize the channels of energy streaming to the EU countries and the risks associated with this energy import. Moreover, we argue that the shift to the low-carbon production of energy and the massive deployment of renewable energy sources (RES) might become the key issue in ensuring the energy security and independency of the EU from its external energy supplies. Both RES, distributed energy resources (DER) and “green energy” that will be based on the energy efficiency and the shift to the alternative energy supply might change the energy security status quo for the EU

    Advanced Testing Chain Supporting the Validation of Smart Grid Systems and Technologies

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    New testing and development procedures and methods are needed to address topics like power system stability, operation and control in the context of grid integration of rapidly developing smart grid technologies. In this context, individual testing of units and components has to be reconsidered and appropriate testing procedures and methods need to be described and implemented. This paper addresses these needs by proposing a holistic and enhanced testing methodology that integrates simulation/software- and hardware-based testing infrastructure. This approach presents the advantage of a testing environment, which is very close to f i eld testing, includes the grid dynamic behavior feedback and is risks-free for the power system, for the equipment under test and for the personnel executing the tests. Furthermore, this paper gives an overview of successful implementation of the proposed testing approach within different testing infrastructure available at the premises of different research institutes in Europe.Comment: 2018 IEEE Workshop on Complexity in Engineering (COMPENG

    Modelling and vulnerability analysis of cyber-physical power systems based on interdependent networks

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    The strong coupling between the power grid and communication systems may contribute to failure propagation, which may easily lead to cascading failures or blackouts. In this paper, in order to quantitatively analyse the impact of interdependency on power system vulnerability, we put forward a “degree–electrical degree” independent model of cyber-physical power systems (CPPS), a new type of assortative link, through identifying the important nodes in a power grid based on the proposed index–electrical degree, and coupling them with the nodes in a communication system with a high degree, based on one-to-one correspondence. Using the double-star communication system and the IEEE 118-bus power grid to form an artificial interdependent network, we evaluated and compare the holistic vulnerability of CPPS under random attack and malicious attack, separately based on three kinds of interdependent models: “degree–betweenness”, “degree–electrical degree” and “random link”. The simulation results demonstrated that different link patterns, coupling degrees and attack types all can influence the vulnerability of CPPS. The CPPS with a “degree–electrical degree” interdependent model proposed in this paper presented a higher robustness in the face of random attack, and moreover performed better than the degree–betweenness interdependent model in the face of malicious attack

    DeepPR: Progressive Recovery for Interdependent VNFs with Deep Reinforcement Learning

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    The increasing reliance upon cloud services entails more flexible networks that are realized by virtualized network equipment and functions. When such advanced network systems face a massive failure by natural disasters or attacks, the recovery of the entire system may be conducted in a progressive way due to limited repair resources. The prioritization of network equipment in the recovery phase influences the interim computation and communication capability of systems, since the systems are operated under partial functionality. Hence, finding the best recovery order is a critical problem, which is further complicated by virtualization due to dependency among network nodes and layers. This paper deals with a progressive recovery problem under limited resources in networks with VNFs, where some dependent network layers exist. We prove the NP-hardness of the progressive recovery problem and approach the optimum solution by introducing DeepPR, a progressive recovery technique based on Deep Reinforcement Learning (Deep RL). Our simulation results indicate that DeepPR can achieve the near-optimal solutions in certain networks and is more robust to adversarial failures, compared to a baseline heuristic algorithm.Comment: Technical Report, 12 page
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