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International audience; With the increasing penetration of electric vehicles (EVs), more and more interactions appear between the transportation system and the power system, which might provide new hazards and channels for the proliferation of failures across the boundaries of the individual systems. In this context, this paper proposes an integrated risk assessment framework for an electric power system, considering scenarios that involve the electrified transportation system enabled by EVs charging technology in New York (NY) State. Firstly, scenarios in the transportation network of NY State, e.g. of reduced capacity and incident, are generated by a Monte Carlo non-sequential algorithm. Then, the cell transmission model (CTM) is used to simulate the evolution of the traffic flows under such scenarios. This allows evaluating the spatial-temporal EV charging loads in different areas of the electrified transportation system of NY State. Correspondingly, the running parameters in the studied power system are updated by the alternative current (AC) power flow model. Finally, the risk for the power system coming from the transportation system scenarios is assessed within a probabilistic risk analysis framework. The proposed integrated risk assessment framework is able to model the propagation of the effects of scenarios in the transportation system onto the power system of NY State and quantify the consequences. A real test case is used to illustrate the proposed framework.

International audience; In this paper, we develop a new method for Multi-Hazards Risk Aggregation (MHRA). A hierarchical framework is first developed for evaluating the trustworthiness of the risk assessment. The evaluation is based on two main attributes (criteria), i.e., the strength of knowledge supporting the assessment and the fidelity of the risk assessment model. These two attributes are further broken down into sub-attributes and, finally, leaf attributes. The trustworthiness is calculated using a weighted average of the leaf attributes, in which the weights are calculated using the Dempster Shafer Theory-Analytical Hierarchy Process (DST-AHP). Risk aggregation is, then, performed by a “weighted posterior” method, considering the level of trustworthiness. An application to the risk aggregation of two hazard groups in Nuclear Power Plants (NPP) is illustrated.

International audience; We introduce a new perspective to improve the reliability of a network, which aims at finding cost-effective portfolios of Prognostics and Health Management (PHM) systems to be installed throughout the network. To do this, we estimate the reliability of the single network element equipped with a PHM system, whose prognostic performance is measured in terms of the − α λ performance, false positive and false negative metrics. Then, we apply genetic algorithms for finding the portfolios of PHM systems to be installed on the network elements, which are optimal with respect to cost and a global reliability efficiency index of the network. The workbench case study of the IEEE 14 bus network is considered as application.

International audience; A sequential Bayesian approach is presented for remaining useful life (RUL) prediction of dependent competing failure processes (DCFP). The DCFP considered comprises of soft failure processes due to degradation and hard failure processes due to random shocks, where dependency arises due to the abrupt changes to the degradation processes brought by the random shocks. In practice, random shock processes are often unobservable, which makes it difficult to accurately estimate the shock intensities and predict the RUL. In the proposed method, the problem is solved recursively in a two-stage framework: in the first stage, parameters related to the degradation processes are updated using particle filtering, based on the degradation data observed through condition monitoring; in the second stage, the intensities of the random shock processes are updated using the Metropolis-Hastings algorithm, considering the dependency between the degradation and shock processes, and the fact that no hard failure has occurred. The updated parameters are, then, used to predict the RUL of the system. Two numerical examples are considered for demonstration purposes and a real dataset from milling machines is used for application purposes. Results show that the proposed method can be used to accurately predict the RUL in DCFP conditions.

International audience; In this work, we propose a multi-perspective framework of analysis of critical infrastructures (CIs) with respect to supply service, topology and controllability. The framework enables identifying the role of CI elements and quantifying the consequences of scenarios of multiple failures, with respect to the different perspectives considered. To present the analysis framework, a benchmark network representative of a real gas transmission network across several countries of the European Union (EU) is considered. The information extracted from such analysis can help us to identify the critical elements and how the properties of the network are affected by failures, and to propose corresponding improvements for CIs. The findings of this paper demonstrate the interest of considering several perspectives in the analysis of CIs for providing useful information for ensuring their safe and reliable operation.

We consider reliability engineering in modern civil aviation industry, and the related engineering activities and methods. We consider reliability in a broad sense, referring to other system characteristics that are related to it, like availability, maintainability, safety and durability. We covered the entire lifecycle of the equipment, including reliability requirement identification, reliability analysis and design, verification and validation of reliability requirements (typically involved in the equipment design and development phase), quality assurance (which typically enters in the manufacturing phase), and fault diagnosis and prognosis and maintenance (which are connected to the operation phase). Lessons learnt from reliability engineering practices in civil aviation industry are given, which might serve as reference for reliability managers and engineers, also from other industries with high reliability requirements. Keywords: Civil aviation, Commercial aircraft, Fault diagnosis and prognosis, Human reliability analysis, Maintenance, Quality control, Reliability design, Reliability engineering

International audience; A B S T R A C T Phased-mission systems (PMSs) are widely used, especially in the aerospace industry. As in the outer space there are many kinds of cosmic rays, such as the Galactic Cosmic Rays (GCR), randomly hitting on these systems and causing significant impact on the electronics inside or outside the equipment, a reliability model for PMSs considering both finite and infinite random shocks is proposed in this paper. The modularization method is used to simplify the state space model for each phase and reduce the amount of system states, and the Markov regenerative process (MRGP) is used to describe the hybrid components' lifetime distributions and the dynamic behaviors within the modules. Then, two kinds of scenarios, finite and infinite random shocks effect, are both integrated into the dynamic modules. For demonstration, a phased altitude and orbit control system (AOCS) subjected to infinite random shocks is illustrated to demonstrate the procedure of the proposed Monte Carlo simulation. Thirdly, the evaluated system reliability under infinite random shocks is compared with the same system without considering random shocks. At last, a sensitivity analysis is also provided for completion.

International audience; Sensor data validation has become an important issue in the operation and control of energy production plants. An undetected sensor malfunction may convey inaccurate or misleading information about the actual plant state, possibility leading to unnecessary downtimes and, consequently, large financial losses. The objective of this work is the development of a novel sensor data validation method to promptly detect sensor malfunctions. The proposed method is based on the analysis of data regularity properties, through the joint use of Continuous Wavelet Transform and image analysis techniques. Differently from the typical sensor data validation techniques which detect a sensor malfunction by observing variations in the relationships among measurements provided by different sensors, the proposed method validates the data collected by a given sensor only using historical data collected from the sensor itself. The proposed method is shown able to correctly detect different types and intensities of sensor malfunctions from energy production plants.

International audience; Consumers in smart grids are expected to engage demand-response programs by two-way communication. This makes smart grids vulnerable to cyber attacks. In this paper, we study the false pricing attacks and model the interaction between attackers and defenders using a zero-sum Markov game, where neither player has full knowledge of the game model. A multi-agent reinforcement learning method is used to solve the Markov game and find the Nash Equilibrium policies for both players. An application to a simple radial power distribution system is worked out. The results show that the proposed algorithm can help the players find mixed strategies to maximize their long-term return.

International audience; Considerable attention has been paid to the vulnerability of critical infrastructures, because of the increasing occurrence of disruptive events, such as man-made or natural disasters. Even small disruptions could eventually affect the normal function of infrastructure systems. Enhancing the reliability of these systems and their robustness to disruptions is necessary and urgent. High-speed rail is a critical infrastructure that is subject to various disruptions, including component aging, malicious attacks, natural disasters, demand surges. In this study, we analyze the topological centrality indicators of China Railway High-speed (CRH) network using network theory, and take real train flow information for assessing the importance of network components in terms of vulnerability to disruption. By Monte Carlo simulation, we analyze the risk of the CRH network under random attacks and spatially localized failures. The significance of taking pre-actions for protecting critical infrastructures by mitigating its vulnerability to disruptions is emphasized.

Drinking water for human health and well-being is crucial. Accidental and intentional water contamination can pose great danger to consumers. Optimal design of a system that can quickly detect the presence of contamination in a water distribution network is very challenging for technical and operational reasons. However, on the one hand improvement in chemical and biological sensor technology has created the possibility of designing efficient contamination detection systems. On the other hand, methods and tools from complex network theory, which was primarily the domain of mathematicians and physicists, provide analytical output for engineers to design, optimize, operate, and maintain complex network systems such as power grids, water distribution networks, telecommunication systems, internet, roads, supply chains, traffic and transportation systems. In this work, we develop a new modeling approach for the optimal placement of sensors for contamination detection in a water distribution network. The approach originally combines classical optimization and complex systems theory., 23 pages, 8 figures, 7 tables

International audience; Modeling the evolution of system reliability in the presence of Condition Monitoring (CM) signals is an important issue for improved reliability assessment and system lifetime prediction. In practice, during its lifetime, a system usually works under varying operating conditions due to internal or external factors such as the ambient environments, operational profiles or workloads. In this context, the system reliability can show varying evolution behaviors (follow changing underlying trajectories), which presents new challenges to describe precisely the dynamics of system reliability. Thus, this paper proposes a novel data-driven approach to address the problems including the identification of varying operating conditions, the construction and dynamical updating of evolution model, and finally the online prediction of system reliability, focusing on systems under one common and typical case of varying operating conditions, the multi-state operating condition. Experiments based on artificial data and some widely studied real reliability cases reveal that the proposed method has superior performance compared with some existing benchmark approaches, in the case under consideration. This improved reliability prediction provides fundamental basis for advanced prognostics such as the Remaining Useful Life (RUL) estimation.

International audience; 10 Concerns on the impacts of disruptive events of various nature on business operations have increased 11 significantly during the past decades. In this respect, business continuity management (BCM) has been proposed as 12 a comprehensive and proactive framework to prevent the disruptive events from impacting the business operations 13 and reduce their potential damages. Most existing business continuity assessment (BCA) models that numerically 14 quantify the business continuity are time-static, in the sense that the analysis done before operation is not updated to 15 consider the aging and degradation of components and systems which influence their vulnerability and resistance to 16 disruptive events. On the other hand, condition monitoring is more and more adopted in industry to maintain under 17 control the state of components and systems. On this basis, in this work, a dynamic and quantitative method is 18 proposed to integrate in BCA the information on the conditions of components and systems. Specifically, a particle 19 filtering-based method is developed to integrate condition monitoring data on the safety barriers installed for system 20 protection, to predict their reliability as their condition changes due to aging. An installment model and a stochastic 21 price model are also employed to quantify the time-dependent revenues and tolerable losses from operating the 22 system. A simulation model is developed to evaluate dynamic business continuity metrics originally introduced. A 23 case study regarding a nuclear power plant (NPP) risk scenario is worked out to demonstrate the applicability of the 24 proposed approach. 25

International audience; Prognostics and health management can improve the reliability and safety of transportation systems. Data collected from diverse sources provide a chance and at the same time a challenge for data-driven PHM methods and models. The data often exhibit challenging characteristics like imbalanced data on normal and faulty conditions, noise and outliers, data points of different importance for the data-driven model, etc. In this paper, a k nearest neighbors-based fuzzy support vector machine is proposed for reducing the computational burden and tackling the issue of imbalance and outlier data, in fault detection. Fault detection is mathematically a classification problem. In this paper, the reverse nearest neighbors technique is adopted for detecting outliers and the k nearest neighbors technique is used to identify the borderline points for defining the classification hyperplane in support vector machines. Considering the position of each data point and the distribution of its nearest neighbors, a new method is proposed for calculating their estimation error costs. The proposed method is verified by comparison with several benchmark methods on five public datasets. Then, a real case study concerning fault detection in a braking system of a high-speed train is considered.

International audience; Multi-state physics systems (MSPS) modeling framework incorporates multi-state models that describes the systems degradation/maintenance process through transitions among discrete states, and physics-based models that describe the degradation process within the states by using physics knowledge and equations. In previous works, piecewise-deterministic Markov process (PDMP) has been adopted to treat the system dynamics and the degradation dependence in MSPS. For reliability assessment, Monte Carlo simulation and finite-volume method are two widely used numerical approaches to solve PDMP. In the present work, a comparative study considering different evaluation criteria of the two approaches is conducted on two representative case studies. We provide clear guidelines for the selection of the two approaches.

A tubular moving-magnet linear oscillating motor (TMMLOM) has merits of high efficiency and excellent dynamic capability. To enhance the thrust performance, quasi-Halbach permanent magnet (PM) arrays are arranged on its mover in the application of a linear electro-hydrostatic actuator in more electric aircraft. The arrays are assembled by several individual segments, which lead to gaps between them inevitably. To investigate the effects of the gaps on the radial magnetic flux density and the machine thrust in this paper, an analytical model is built considering both axial and radial gaps. The model is validated by finite element simulations and experimental results. Distributions of the magnetic flux are described in condition of different sizes of radial and axial gaps. Besides, the output force is also discussed in normal and end windings. Finally, the model has demonstrated that both kinds of gaps have a negative effect on the thrust, and the linear motor is more sensitive to radial ones. Keywords: Air-gap flux density, Linear motor, Mover gaps, Quasi-Halbach array, Thrust output, Tubular moving-magnet linear oscillating motor (TMMLOM)

Common-Cause Failures (CCFs) are an important threat to safety critical systems. Most existing CCF models assume that the component failure behavior does not vary over time. Such an assumption is often challenged in practice due to the influence of various degradation mechanisms, e.g., wear, corrosion, fatigue, etc. In this paper, we develop a new model for CCFs considering components degradation. The model is developed in the mathematical framework of Stochastic Hybrid Systems (SHS). The CCFs are modeled as random shock processes that affect a group of components simultaneously and the components degradation processes are modeled by stochastic differential equations derived from physics-of-failures. The benefit of using the SHS model for CCFs is that the developed model is analytically solvable. The system reliability can, then, also be solved analytically in closed form. The proposed CCF modelling framework is demonstrated by a numerical example of a three-unit redundant system and, then, applied to an Auxiliary Feedwater Pump (AFP) system of a Nuclear Power Plant (NPP). A comparison to the Binomial Failure Rate (BFR) model of literature shows that by considering the components degradation processes, the proposed model can accurately describe the CCF effect on the reliability of a system with degrading components.

Big data from very large fleets of assets challenge the asset management, as the number of maintenance strategies to optimize and administrate may become very large. To address this issue, we exploit a clustering approach that identifies a small number of sets of assets with similar reliability behaviors. This enables addressing the maintenance strategy optimization issue once for all the assets belonging to the same cluster and, thus, introduces a strong simplification in the asset management. However, the clustering approach may lead to additional maintenance costs, due to the loss of refinement in the cluster reliability model. For this, we propose a cost model to support asset managers in trading off the simplification brought by the cluster-based approach against the related extra costs. The proposed approach is applied to a real case study concerning a set of more than 30,000 switch point machines.

Interval availability, defined as the fraction of time that a system is operational during a period of time $[ {0,T} ]$ , is an important indicator of system performance, especially for industries with a Service Level Agreement, e.g., telecommunication industry, computer industry, etc. Most existing methods to compute interval availability are based on numerical simulations. In this paper, we present a new analytical solution for interval availability of Markov repairable systems. Three interval availability indexes, i.e., interval availability, interval availability in a general interval, and interval availability in multiple intervals, are considered. The interval availability indexes are derived based on aggregated stochastic processes and the results are obtained in closed form using Laplace transforms. A numerical example is presented and the results are compared with those of Monte Carlo simulation. The developed methods are applied to calculate the interval availability of a fault-tolerant database system from the literature.

International audience; We propose an analytic, time-variant model that conservatively evaluates the increase in reliability achievable when a component is equipped with a Prognostics and Health Management system of known performance metrics. The reliability model builds on metrics of literature and is applicable to different industrial contexts. A simulated case study concerning crack propagation in a mechanical component is considered to validate the proposed model.

International audience; This paper proposes a novel risk assessment method for power network failures considering a uniform-pricing market environment, different from previous risk assessment studies, which mainly emphasize technical consequences of the failures. In this type of market, dispatch infeasibilities caused by line failures are solved using a counter-trading mechanism where costs arise as a result of correcting the power dispatch. The risk index proposed takes into account these correction costs as well as the cost of the energy not served due to the failure, while considering an oligopolistic behavior of the generation companies. A 3-stage model is proposed to simulate the bidding behavior in the market, under different line failures scenarios. The risk index proposed and the method for its calculation are applied on an adapted IEEE 6-bus reliability test system. A sensitivity analysis is performed to investigate the sensitivity of the results with respect to the level of competitiveness of the generation companies, measured by the conjectured-price response parameter which is assumed to be exogenous in our study.

International audience; In this paper, a new method is developed to model dependent failure behaviors among failure mechanisms. Unlike existing methods, the developed method models the root cause of the dependency explicitly, so that a deterministic model, rather than a probabilistic one, can be established. Three steps comprise the developed method. First, physics-of-failure (PoF) models are utilized to model each failure mechanism. Then, interactions among failure mechanisms are modeled as a combination of three basic relations, competition, superposition and coupling. This is the reason why the method is referred to as " compositional method ". Finally, the PoF models and the interaction model are combined to develop a deterministic model of the dependent failure behaviors. As a demonstration, the method is applied on an actual spool and the developed failure behavior model is validated by a wear test. The result demonstrates that the compositional method is an effective way to model dependent failure behaviors.

International audience; In this work, we consider the problem of predicting the remaining useful life of a piece of equipment, based on data collected from a heterogeneous fleet working under different operating conditions. When the equipment experiences variable operating conditions, individual data-driven prognostic models are not able to accurately predict the remaining useful life during the entire equipment life. The objective of this work is to develop an ensemble approach of different prognostic models for aggregating their remaining useful life predictions in an adaptive way, for good performance throughout the degradation progression. Two data-driven prognostic models are considered, a homogeneous discrete-time finite-state semi-Markov model and a fuzzy similarity–based model. The ensemble approach is based on a locally weighted strategy that aggregates the outcomes of the two prognostic models of the ensemble by assigning to each model a weight and a bias related to its local performance, that is, the accuracy in predicting the remaining useful life of patterns of a validation set similar to the one under study. The proposed approach is applied to a case study regarding a heterogeneous fleet of aluminum electrolytic capacitors used in electric vehicle powertrains. The results have shown that the proposed ensemble approach is able to provide more accurate remaining useful life predictions throughout the entire life of the equipment compared to an alternative ensemble approach and to each individual homogeneous discrete-time finite-state semi-Markov model and fuzzy similarity–based models.

Integrated Deterministic and Probabilistic Safety Analysis (IDPSA) of dynamic systems calls for the development of efficient methods for accidental scenarios generation. The necessary consideration of failure events timing and sequencing along the scenarios requires the number of scenarios to be generated to increase with respect to conventional PSA. Consequently, their postprocessing for retrieving safety relevant information regarding the system behavior is challenged because of the large amount of generated scenarios that makes the computational cost for scenario postprocessing enormous and the retrieved information difficult to interpret. In the context of IDPSA, the interpretation consists in the classification of the generated scenarios as safe, failed, Near Misses (NMs), and Prime Implicants (PIs). To address this issue, in this paper we propose the use of an ensemble of Semi-Supervised Self-Organizing Maps (SSSOMs) whose outcomes are combined by a locally weighted aggregation according to two strategies: a locally weighted aggregation and a decision tree based aggregation. In the former, we resort to the Local Fusion (LF) principle for accounting the classification reliability of the different SSSOM classifiers, whereas in the latter we build a classification scheme to select the appropriate classifier (or ensemble of classifiers), for the type of scenario to be classified. The two strategies are applied for the postprocessing of the accidental scenarios of a dynamic U-Tube Steam Generator (UTSG).

International audience; Defenders have to enforce defense strategies by taking decisions on allocation of resources to protect the integrity and survivability of cyber–physical systems (CPSs) from intentional and malicious cyber attacks. In this work, we propose an adversarial risk analysis approach to provide a novel one‐sided prescriptive support strategy for the defender to optimize the defensive resource allocation, based on a subjective expected utility model, in which the decisions of the adversaries are uncertain. This increases confidence in cyber security through robustness of CPS protection actions against uncertain malicious threats compared with prescriptions provided by a classical defend–attack game‐theoretical approach. We present the approach and the results of its application to a nuclear CPS, specifically the digital instrumentation and control system of the advanced lead‐cooled fast reactor European demonstrator.

International audience; In this paper, a framework is proposed for integrating condition monitoring and inspection data in Dynamic Risk Assessment (DRA). Condition monitoring data are online-collected by sensors and indirectly relate to component degradation; inspection data are recorded in physical inspections that directly measure the component degradation. A Hidden Markov Gaussian Mixture Model (HM-GMM) is developed for modeling the condition monitoring data and a Bayesian network (BN) is developed to integrate the two data sources for DRA. Risk updating and prediction are exemplified on an Event Tree (ET) risk assessment model. A numerical case study and a real-world application on a Nuclear Power Plant (NPP) are performed to demonstrate the application of the proposed framework.

International audience; The paper investigates how resilience can be understood as an operational paradigm for system management from a multistage decision making perspective. Specifically, agents involved in the resilience management aim at an optimum process of sequential preparedness/protection, emergency response, recovery, and adaptation activities, trading off the loss due to lack of system functionality with the needed investment. Agents' decisions are made sequentially aiming at minimizing the overall system loss in the presence of numerous uncertainties in the intensity of the disruption, post-disruption demand, repair durations, etc. We restrict the actions to only depend on uncertain parameters realized up to the current decision period. This process can be formulated as a multi-stage optimization problem. We discuss how the linear decision rule approximation can be used to overcome the curse of the computational complexity of the problem and to derive operationally implementable policies. By referring to a simple example of a notional infrastructure, we demonstrate that the proposed approach helps providing a holistic view of resilience management and deriving optimal actions and policies for resilience enhancement.

International audience; Operational flexibility is an integral part of the design of power systems with a high share of renewable energy sources. Resilience against severe weather is also becoming an important concern. In this paper, we propose a comprehensive framework for power systems planning which considers both flexibility and resilience against extreme weather events. A set of piece-wise linear models are developed to calculate the impact of extreme heat waves and drought events on the performance of the power generation units and on the system load. We analyze the results obtained on a case study under real future climate projections from the Coupled Model Intercomparison Project phase 5 and compare them to those from conventional planning methods.

International audience; For components subject to degradation, cost-efficient maintenance is necessary. Periodic or continuous collection of information, reducing uncertainty on the component's state of health, generally leads to a better-informed and, thus, more efficient maintenance. Processing condition monitoring data to estimate the current and future health states of the component, can prove valuable. In this paper, it is proposed to quantify the Value of Information (VoI) that may be obtained from state estimation and prediction procedures, with known precision, applied for condition-based and predictive maintenance. VoI is computed numerically using gamma process paths and on the basis of the optimization of the parameters of different maintenance strategies.

International audience; In this paper, we extend a previously introduced traffic cell transmission model (CTM) to take electric vehicles (EVs) and charging stations (CSs) into account. The CTM is improved in the following aspects: firstly, traffic demand of multiple origins-destinations (O-D) is considered, whereas the original CTM can not simulate the specific traffic demand of each O-D pair; secondly, CSs are integrated into CTM by developing new queueing cells and charging cells, which could also describe the specific configurations of each CS and model the queueing phenomenon in CSs; third, EVs at different states of charge are considered and, thus, the spatial-temporal charging demands in CSs can be estimated with good approximation.

International audience; This article proposes a novel mathematical optimization framework for the identification of the vulnerabilities of electric power infrastructure systems (which is a paramount example of critical infrastructure) due to natural hazards. In this framework, the potential impacts of a specific natural hazard on an infrastructure are first evaluated in terms of failure and recovery probabilities of system components. Then, these are fed into a bi‐level attacker–defender interdiction model to determine the critical components whose failures lead to the largest system functionality loss. The proposed framework bridges the gap between the difficulties of accurately predicting the hazard information in classical probability‐based analyses and the over conservatism of the pure attacker–defender interdiction models. Mathematically, the proposed model configures a bi‐level max‐min mixed integer linear programming (MILP) that is challenging to solve. For its solution, the problem is casted into an equivalent one‐level MILP that can be solved by efficient global solvers. The approach is applied to a case study concerning the vulnerability identification of the georeferenced RTS24 test system under simulated wind storms. The numerical results demonstrate the effectiveness of the proposed framework for identifying critical locations under multiple hazard events and, thus, for providing a useful tool to help decisionmakers in making more‐informed prehazard preparation decisions.

International audience; This chapter addresses the challenges associated with assessing and improving the resilience of interdependent critical infrastructure systems under potential disruptive events. A specific set of analytical tools are introduced based on quantitative models of infrastructure systems operation and their functional interdependencies. Specifically, the game-theoretic attacker-defender and defender-attacker defender modeling techniques are applied to assessing the resilience of interdependent CI systems under worst-case disruptions, and advising policymakers on making pre-disruption decisions for improving the resilience of interdependent infrastructures. A case of interdependent power and gas systems is presented to show the proposed model and highlight the significance of protecting interdependent CIs.

Communication through social media can help engage end users to improve the efficiency of demand-side management in smart power grids. However, this opens a channel between the social network and the power grid through which malicious attackers can publish false information that can actually cause problems to the power grid. In this paper, we analyze this new problem by modeling a social network-coupled smart grid and investigating its vulnerability to false pricing attacks in the social network. The energy consumption profile based on social information is modeled as a consumption rescheduling problem, which aims to maximize the benefit of demand-side management. The false price spreading process is described by a multi-level influence propagation model, which takes into account the personalities of the end users. Different attack strategies are considered and the power operator's response is modeled. The residual ampacity of distribution lines and the expected energy not supplied are adopted to quantify the impacts of the attacks on the power system. To account for the stochastic characteristics of the influence propagation process, Monte Carlo simulation is utilized. The proposed modeling and analysis framework is applied on a modified IEEE 13 nodes test feeder and a notional social network. The vulnerability to attack is analyzed at both component and system levels.

International audience; Various models of fatigue crack growth in different scenarios have been proposed in the literature. Here, in this paper, we propose a general prognostic framework for tracking crack evolution in equipment undergoing fatigue and predicting the Remaining Useful Life (RUL). The main contribution of this work is to integrate Particle Filtering (PF) and a new ensemble model which combines diverse physical degradation models with respect to their accuracy performance in previous time steps, in order to maximize the overall prediction capability. To validate the effectiveness of the proposed framework, a case study concerning multiple fatigue crack growth degradations is extensively investigated.

International audience; Reliable and safe critical infrastructures are crucial for the sustainability of modern societies. To cope with increasing disruptive events such as man-made and natural disasters attacking infrastructures, resilience should be considered as an integrated perspective into the system planning process. This paper presents a p-robust optimization model for infrastructure network planning against spatially localized disruptions. The optimization aims at minimizing the investment costs for system hardening and expansion and the total system costs under nominal operating conditions, while incorporating resilience requirements by the p-robustness constraints. Importantly, instead of only mitigating system vulnerability , the proposed model integrates the arranging of the repair sequence of damaged components under limited repair resources into the preevent system planning. The complexity of the proposed model is analyzed, and a hybrid algorithm that combines scenario-based decomposition and variable neighborhood search is developed for its efficient solution. The effectiveness of the approach is illustrated through an application to a real power transmission system. Quantitative analysis can assist managers in decision making regarding investing in different system protection actions and making a tradeoff between desired resilience and budget constraints. Index Terms-Critical infrastructures (CIs), disruption risk, optimization , system planning, system resilience. NOMENCLATURE Indexes, parameters, and sets G(V, L 0) An undirected graph {G} comprising a set of nodes V connected by a set of links L 0 representing the original network system before the disruption.

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International audience; Fatigue crack propagation affects the operational reliability of engine turbo-fan blades. In this article, we integrate a Kriging regression model and a distributed collaborative response surface method (DCRSM) for the reliability assessment of turbo-fan blades, considering the relevant uncertainty. Following a series of deterministic analyses, such as steady-state aerodynamic analysis, harmonic response analysis and Campbell diagram, and based on the assumption that vibration stress is mainly from aerodynamic load, the fatigue strength is calculated for turbo-fan blades under coupling aerodynamic forces, according to a modified Goodman curve of titanium-alloy. Giving consideration to the uncertainty of the resonance frequencies and material properties, the fatigue strength of the turbo-fan blade is evaluated, including probabilistic analysis and sensitivity analysis. In the case study analyzed, the conclusions are that the fatigue strength reliability reaches 96.808% with confidence level of 0.95 for the turbo-fan blade under the coupling aerodynamic forces, and the first three-order resonant frequencies are found to have important influence on the fatigue performance of turbo-fan blades.

International audience; This paper presents a prognostic scheme for estimating the remaining useful life of Lithium-ion batteries. The proposed scheme utilizes a prediction module that aims to obtain precise predictions for both short and long prediction horizons. The prediction module makes use of extreme learning machines for one-step and multi-step ahead predictions , using various prediction strategies, including iterative, direct and DirRec, which use the constant-current experimental capacity data for the estimation of the remaining useful life. The data-driven prognostic approach is highly dependent on the availability of high quantity of quality observations. Insufficient amount of available data can result in unsatisfactory prognostics. In this paper, the prognostics scheme is utilized to estimate the remaining useful life of a battery, with insufficient direct data available, but taking advantage of observations available from a fleet of similar batteries with similar working conditions. Experimental results show that the proposed prognostic scheme provides a fast and efficient estimation of the remaining useful life of the batteries and achieves superior results when compared with various state-of-the-art prediction techniques.

The planning of post-disruption restoration in critical infrastructure systems often relies on deterministic assumptions such as complete information on resources and known duration of the repair tasks. In fact, the uncertainties faced by restoration activities, e.g. stemming from subjective estimates of resources and costs, are rarely considered. Thus, the solutions obtained by a deterministic approach may be suboptimal or even infeasible under specific realizations of the uncertainties. To bridge this gap, this paper investigates the effects of uncertain repair time and resources on the post-disruption restoration of critical infrastructure. Two-stage stochastic optimization provides insights for prioritizing the intensity and time allocation of the repair activities with the objective of maximizing system resilience. The inherent stochasticity is represented via a set of scenarios capturing specific realizations of repair activity durations and available resources, and their probabilities. A multi-mode restoration model is proposed that offers more flexibility than its single-mode counterpart. The restoration framework is applied to the reduced British electric power system and the results demonstrate the added value of using the stochastic model as opposed to the deterministic model. Particularly, the benefits of the proposed stochastic method increase as the uncertainty associated with the restoration process grows. Finally, decision-making under uncertainty largely impacts the optimum repair modes and schedule.

International audience; The introduction of pervasive telecommunication devices, in the scope of smart grids (SGs), has accentuated interest in the distribution network, which integrates a huge portion of new grid applications. High impact low probability (HILP) events, such as natural hazards, manmade errors, and cyber-attacks, as well as the inherent fragility of the distribution grid have propelled the development of effective resilience tools and methods for the power distribution network (PDN) to avoid catastrophic infrastructural and economical losses. Multiple resilience evaluation frameworks are proposed in the literature in order to assist distribution system operators (DSOs) in managing their networks when faced with exogenous threats. We conduct detailed analysis of existing quantitative resilience studies in both electric and telecommunication domains of a PDN, focusing on event type, metrics, temporal phases, uncertainty, and critical load. Our work adopts the standpoint of a DSO, whose target is to identify feasible resilience assessment frameworks, which apply to pre-defined requirements in terms of resilience evaluation objectives (planning, reactive response, or simple assessment), time of evaluation, and available enhancement strategies. Finally, results and observations on selected works are presented, followed by discussion of identified challenges and opportunities.

Liquefied natural gas (LNG) is used as fuel in various kinds of vessels, e.g., passenger ship, ferry, cargo vessel and platform supply vessel (PSV). It is an eco-friendly bunker fuel with many advantages, like decreasing the emissions of SOX and particulate materials (PM) and meeting the international maritime organization (IMO) MARPOL Annex VI requirements on NOX emissions, and economic benefits compared to heavy fuel oil (HFO). However, the leakage of LNG-fuel is a threat for the safety of LNG-fueled vessels, due to its inflammable and explosive characteristics. This paper illustrates a framework for the quantitative risk assessment of LNG-fueled vessels with respect to potential leakage. For illustration purposes, reference is made to a typical LNG-fueled ship, as a representative case. Event tree analysis (ETA) and computational fluid dynamics (CFD) simulation are integrated for the investigation of the hazard, the analysis of the consequences, and the quantification the risk of the LNG leakage. The results of the study are used to provide risk control options (RCOs), in terms of optimal risk mitigation for LNG-fueled vessels.

Link to publishers version: 10.1016/j.ress.2016.03.004 We consider the assessment of the availability of oil and gas processing facilities operating under Arctic conditions. The novelty of the work lies in modelling the time-dependent effects of environmental conditions on the components failure and repair rates. This is done by introducing weather-dependent multiplicative factors, which can be estimated by expert judgements given the scarce data available from Arctic offshore operations. System availability is assessed considering the equivalent age of the components to account for the impacts of harsh operating conditions on component life history and maintenance duration. The application of the model by direct Monte Carlo simulation is illustrated on an oil processing train operating in Arctic offshore. A scheduled preventive maintenance task is considered to cope with the potential reductions in system availability under harsh operating conditions

International audience; Sensitivity Analysis (SA) is performed to gain fundamental insights on a system behavior that is usually reproduced by a model and to identify the most relevant input variables whose variations affect the system model functional response. For the reliability analysis of passive safety systems of Nuclear Power Plants (NPPs), models are Best Estimate (BE) Thermal Hydraulic (TH) codes, that predict the system functional response in normal and accidental conditions and, in this paper, an ensemble of three alternative invariant SA methods is innovatively set up for a SA on the TH code input variables. The ensemble aggregates the input variables raking orders provided by Pearson correlation ratio, Delta method and Beta method. The capability of the ensemble is shown on a BE-TH code of the Passive Containment Cooling System (PCCS) of an Advanced Pressurized water reactor AP1000, during a Loss Of Coolant Accident (LOCA), whose output probability density function (pdf) is approximated by a Finite Mixture Model (FMM), on the basis of a limited number of simulations.

The impact of energy production, transformation and use on the environmental resources encourage to understand the mechanisms of resource degradation and to develop proper analyses to reduce the impact of the energy systems on the environment. At the technical level, most attempts for reducing the environmental impact of energy systems focus on the improvement of process efficiency. One way toward an integrated approach is that of adopting exergy analysis for assessing efficiency and test improving design and operation solutions. The paper presents an exergy based analysis for improving efficiency and safety of energy systems, named Thermorisk analysis. The purpose of the Thermorisk analysis is to supply information to control, and eventually reduce, the risk of the systems (i.e. risk of accidents) by acting on the thermodynamic parameters and safety characteristics in the same frame. The proper combination of exergy and risk analysis allows monitoring the effects of efficiency improvement on the safety of the systems analyzed. A case study is presented, showing the potential of the analysis to identify the relation between the exergy efficiency and the risk of the system analyzed, and the contribution of inefficiencies on the safety of the process. Possible modifications in the process are indicated to improve the safety of the system.

Recent challenges from severe weather events call for an adaptation in power systems planning methods, to ensure the sustainability of the resulting plans and the resilience of the power system against natural threats. Whereas studies have shown the importance of considering operational flexibility as an integral part of the design of power systems with a high share of renewable energy sources, very few works have incorporated the resilience against severe weather threats directly into the system design phase. We propose a comprehensive flexibility and resilience modeling and optimization framework for power systems planning considering extreme weather scenarios. A set of piece-wise linear models are developed to calculate the impact of extreme heat waves and drought events on the performance of the power generation units and on the system load. We propose a method to integrate the extreme climate impact within the power system planning model and investigate the results obtained compared to conventional planning methods under real future climate projections.

With the increasing penetration of electric vehicles in the transportation system, more and more interactions appear between the transportation and power systems. This requires considering the impact of disturbances in the electrified transportation system onto the stability of the power system. This paper addresses this issue by proposing a method based on a cell transmission model (CTM) of the electrified transportation system and an alternative current (AC) model of the power system. Specifically, CTM is used to simulate the dynamic realtime traffic under congestion disturbances and evaluate the charging demands in the areas of the electrified transportation system. The charging demands are input to the AC model of the power system to calculate the fluctuations in power flow distributions. The proposed method can simulate the dynamic interactions between the electrified transportation system and the power system, and quantitatively measure the impacts of traffic disturbances on the stability of the power system. A numerical example is used to illustrate the proposed method.

International audience; The reliability of steam generators in nuclear power plants has always been a challenging issue. Various diagnostic models have been proposed in the literature. However, no work has been reported on the development of a robust prediction model for forecasting the future health state of steam generators. In this paper, we propose an ARIMA-based prognostic approach for tracking the degradation evolution in a steam generator and further predicting its Remaining Useful Life (RUL) before breakdown. A case study concerning real degradation datasets from different steam generators is extensively investigated to validate the performance of the proposed model.

The idea of coupling social networks with power grids to engage end users has attracted much attention. In this paper, we model a social network-coupled power grid system and analyze its vulnerability to false price attacks initiated on social networks. The false price spreading process is depicted by a price-based multi-level influence propagation model and the corresponding behavior of customers is based on their influence levels. The residential capacity of power lines is adopted to quantify the impact on power systems. A modified IEEE 13 node test feeder and a notional social network are considered as a case study. The vulnerability of the system under different attack scenarios is analyzed.

International audience; In this paper, an optimization model is developed for optimal design of the safety barriers in a nuclear power plant. By applying the developed model, safety barriers of different natures, i.e., the prevention, mitigation, emergency and recovery measures, can be optimized jointly for business continuity. A hierarchical numerical optimization method based on golden search and genetic algorithm is developed to obtain the optimal solutions. A numerical case study regarding the allocation of resources among the safety barriers prevention, mitigation and emergency in a nuclear power plant is worked out to maximize business continuity against the threat of steam generator tube ruptures.

International audience; Data-driven methods for direct prognostic map the relationship between monitored parameters and equipment Remaining Useful Life (RUL). They typically require the availability of a set of run-to-failure degradation trajectories for model training. Yet, in many industrial applications, equipment is often replaced before they fail to avoid catastrophic consequences on production and safety. Then also, incomplete degradation trajectories are available. In this work, we develop a method for predicting equipment RUL, and the related uncertainty based on both complete and incomplete degradation trajectories. The method is based on the combined use of a similarity measure and Evidence Theory (EvT). Application of the method on two case studies shows that it provides accurate RUL predictions, also in comparison with a similarity-based regression method of literature.