Artificial Intelligence for Natural Hazards Risk Analysis: Potential, Challenges, and Research Needs

Artificial intelligence (AI) methods have seen increasingly widespread use in everything from consumer products and driverless cars to fraud detection and weather forecasting. The use of AI has transformed many of these application domains. There are ongoing efforts at leveraging AI for disaster risk analysis. This article takes a critical look at the use of AI for disaster risk analysis. What is the potential? How is the use of AI in this field different from its use in nondisaster fields? What challenges need to be overcome for this potential to be realized? And, what are the potential pitfalls of an AI‐based approach for disaster risk analysis that we as a society must be cautious of?Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155885/1/risa13476_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155885/2/risa13476.pd

Fragility Curves for Assessing the Resilience of Electricity Networks Constructed from an Extensive Fault Database

Robust infrastructure networks are vital to ensure community resilience; their failure leads to severe societal disruption and they have important postdisaster functions. However, as these networks consist of interconnected, but geographically-distributed, components, system resilience is difficult to assess. In this paper the authors propose the use of an extension to the catastrophe (CAT) risk modeling approach, which is primarily used to perform risk assessments of independent assets, to be adopted for these interdependent systems. To help to achieve this, fragility curves, a crucial element of CAT models, are developed for overhead electrical lines using an empirical approach to ascribe likely failures due to wind storm hazard. To generate empirical fragility curves for electrical overhead lines, a dataset of over 12,000 electrical failures is coupled to a European reanalysis (ERA) wind storm model, ERA-Interim. The authors consider how the spatial resolution of the electrical fault data affects these curves, generating a fragility curve with low resolution fault data with a R2R2 value of 0.9271 and improving this to a R2R2 value of 0.9889 using higher spatial resolution data. Recommendations for deriving similar fragility curves for other infrastructure systems and/or hazards using the same methodological approach are also made. The authors argue that the developed fragility curves are applicable to other regions with similar electrical infrastructure and wind speeds, although some additional calibration may be required

Topological Performance Measures as Surrogates for Physical Flow Models for Risk and Vulnerability Analysis for Electric Power Systems

Critical infrastructure systems must be both robust and resilient in order to ensure the functioning of society. To improve the performance of such systems, we often use risk and vulnerability analysis to find and address system weaknesses. A critical component of such analyses is the ability to accurately determine the negative consequences of various types of failures in the system. Numerous mathematical and simulation models exist which can be used to this end. However, there are relatively few studies comparing the implications of using different modeling approaches in the context of comprehensive risk analysis of critical infrastructures. Thus in this paper, we suggest a classification of these models, which span from simple topologically-oriented models to advanced physical flow-based models. Here, we focus on electric power systems and present a study aimed at understanding the tradeoffs between simplicity and fidelity in models used in the context of risk analysis. Specifically, the purpose of this paper is to compare performances measures achieved with a spectrum of approaches typically used for risk and vulnerability analysis of electric power systems and evaluate if more simplified topological measures can be combined using statistical methods to be used as a surrogate for physical flow models. The results of our work provide guidance as to appropriate models or combination of models to use when analyzing large-scale critical infrastructure systems, where simulation times quickly become insurmountable when using more advanced models, severely limiting the extent of analyses that can be performed

Quantifizierung der Zuverlässigkeit und Komponentenbedeutung von Infrastrukturen unter Berücksichtigung von Naturkatastropheneinwirkung

The central topic is the quantification of the reliability of infrastructure networks subject to extreme wind loads. Random fields describe the wind distributions and calibrated fragility curves yield the failure probabilities of the components as a function of the wind speed. The network damage is simulated taking into account possible cascading component failures. Defined "Importance Measures" prioritize the components based on their impact on system reliability - the basis for system reliability improvement measures.Zentrales Thema ist die Quantifizierung der Zuverlässigkeit von Infrastrukturnetzen unter Einwirkung extremer Windlasten. Raumzeitliche Zufallsfelder beschreiben die Windverteilungen und spezifisch kalibrierte Fragilitätskurven ergeben die Versagenswahrscheinlichkeiten der Komponenten. Der Netzwerkschaden wird unter Berücksichtigung von kaskadierenden Komponentenausfällen simuliert. Eigens definierte „Importance Measures“ priorisieren die Komponenten nach der Stärke ihres Einflusses auf die Systemzuverlässigkeit - die Basis für Verbesserungen der Systemzuverlässigkeit

Feasibility study of PRA for critical infrastructure risk analysis

Probabilistic Risk Analysis (PRA) has been commonly used by NASA and the nuclear power industry to assess risk since the 1970s. However, PRA is not commonly used to assess risk in networked infrastructure systems such as water, sewer and power systems. Other methods which utilise network models of infrastructure such as random and targeted attack failure analysis, N-k analysis and statistical learning theory are instead used to analyse system performance when a disruption occurs. Such methods have the advantage of being simpler to implement than PRA. This paper explores the feasibility of a full PRA of infrastructure, that is one that analyses all possible scenarios as well as the associated likelihoods and consequences. Such analysis is resource intensive and quickly becomes complex for even small systems. Comparing the previously mentioned more commonly used methods to PRA provides insight into how current practises can be improved, bringing the results closer to those that would be presented from PRA. Although a full PRA of infrastructure systems may not be feasible, PRA should not be discarded. Instead, analysis of such systems should be carried out using the framework of PRA to include vital elements such as scenario likelihood analysis which are often overlooked.publishedVersio

A Two-Stage Resilience Enhancement for Distribution Systems Under Hurricane Attacks

Hurricane events can cause severe consequences to the secure supply of electricity systems. This article designs a novel two-stage approach to minimize hurricane impact on distribution networks by automatic system operation. A dynamic hurricane model is developed, which has a variational wind intensity and moving path. The article then presents a two-stage resilience enhancement scheme that considers predisaster strengthening and postcatastrophe system reconfiguration. The pre-disaster stage evaluates load importance by an improved PageRank algorithm to help deploy the strengthening scheme precisely. Then, a combined soft open point and networked microgrid strategy is applied to enhance system resilience. Load curtailment is quantified considering both power unbalancing and the impact of line overloading. To promote computational efficiency, particle swarm optimization is applied to solve the designed model. A 33-bus electricity system is employed to demonstrate the effectiveness of the proposed method. The results clearly illustrate that the impact of hurricanes on load curtailment, which can be significantly reduced by appropriate network reconfiguration strategies. This model provides system operators a powerful tool to enhance the resilience of distribution systems against extreme hurricane events, reducing load curtailment

RISK-BASED ASSESSMENT AND STRENGTHENING OF ELECTRIC POWER SYSTEMS SUBJECTED TO NATURAL HAZARDS

Modern economic and social activities are dependent on a complex network of infrastructure systems that are highly interdependent. Electric power systems form the backbone of such complex network as most civil infrastructure systems cannot function properly without reliable power supply. Electric power systems are vulnerable to extensive damage due to natural hazards, as evident in recent hazard events. Hurricanes, earthquakes, floods, tornados and other natural hazards have caused billions of dollars in direct losses due to damage to power systems and indirect losses due to power outages, as well as social disruption. There is, therefore, a need for a comprehensive framework to assess and mitigate the risk posed by natural hazards to electric power systems. Electric power systems rely on various components that work together to deliver power from generating units to customers. Consequently, any reliable risk assessment methodology needs to take into account how the different components interact. This requires a system-level risk assessment approach. This research presents a framework for system-level risk assessment and management for electric power systems subjected to natural hazards. Specifically, risk due to hurricanes and earthquakes, as well as the combined effect of both is considered. The framework incorporates a topological-based system reliability model, probabilistic and scenario-based hazard analysis, climate change modeling, component vulnerability, component importance measure, multi-hazard risk assessment, and cost analysis. Several risk mitigation strategies are proposed; their efficiency and cost-effectiveness are studied. The developed framework is intended to assist utility companies and other stakeholders in making a risk-informed decision regarding short- and long-term investment in natural hazard risk mitigation for electric power systems. The framework can be used to identify certain parts of the system to strengthen, compare the efficiency and cost-effectiveness of various risk mitigation strategies using life-cycle cost analysis, compare risks posed by different natural hazards, and prioritize investment in the face of limited resources

Resilience Enhancement Strategies for Modern Power Systems

The frequency of extreme events (e.g., hurricanes, earthquakes, and floods) and man-made attacks (cyber and physical attacks) has increased dramatically in recent years. These events have severely impacted power systems ranging from long outage times to major equipment (e.g., substations, transmission lines, and power plants) destructions. Also, the massive integration of information and communication technology to power systems has evolved the power systems into what is known as cyber-physical power systems (CPPSs). Although advanced technologies in the cyber layer improve the operation and control of power systems, they introduce additional vulnerabilities to power system performance. This has motivated studying power system resilience evaluation and enhancements methods. Power system resilience can be defined as ``The ability of a system to prepare for, absorb, adapt to, and recover from disruptive events''. Assessing resilience enhancement strategies requires further and deeper investigation because of several reasons. First, enhancing the operational and planning resilience is a mathematically involved problem accompanied with many challenges related to modeling and computation methods. The complexities of the problem increases in CPPSs due to the large number and diverse behavior of system components. Second, a few studies have given attention to the stochastic behavior of extreme events and their accompanied impacts on the system resilience level yielding less realistic modeling and higher resilience level. Also, the correlation between both cyber and physical layers within the context of resilience enhancement require leveraging sophisticated modeling approaches which is still under investigation. Besides, the role of distributed energy resources in planning-based and operational-based resilience enhancements require further investigation. This calls for developing enhancement strategies to improve resilience of power grids against extreme events. This dissertation is divided into four parts as follows. Part I: Proactive strategies: utilizing the available system assets to prepare the power system prior to the occurrence of an extreme event to maintain an acceptable resilience level during a severe event. Various system generation and transmission constraints as well as the spatiotemporal behavior of extreme events should be properly modeled for a feasible proactive enhancement plan. In this part, two proactive strategies are proposed against weather-related extreme events and cyber-induced failure events. First, a generation redispatch strategy is formulated to reduce the amount of load curtailments in transmission systems against hurricanes and wildfires. Also, a defensive islanding strategy is studied to isolate vulnerable system components to cyber failures in distribution systems. Part II: Corrective strategies: remedial actions during an extreme event for improved performance. The negative impacts of extreme weather events can be mitigated, reduced, or even eliminated through corrective strategies. However, the high stochastic nature of resilience-based problem induces further complexities in modeling and providing feasible solutions. In this part, reinforcement learning approaches are leveraged to develop a control-based environment for improved resilience. Three corrective strategies are studied including distribution network reconfiguration, allocating and sizing of distributed energy resources, and dispatching reactive shunt compensators. Part III: Restorative strategies: retain the power service to curtailed loads in a fast and efficient means after a diverse event. In this part, a resilience enhancement strategy is formulated based on dispatching distributed generators for minimal load curtailments and improved restorative behavior. Part IV: Uncertainty quantification: Impacts of uncertainties on modeling and solution accuracy. Though there exist several sources of stochasticity in power systems, this part focuses on random behavior of extreme weather events and the associated impacts on system component failures. First, an assessment framework is studied to evaluate the impacts of ice storms on transmission systems and an evaluation method is developed to quantify the hurricane uncertainties for improved resilience. Additionally, the role of unavailable renewable energy resources on improved system resilience during extreme hurricane events is studied. The methodologies and results provided in this dissertation can be useful for system operators, utilities, and regulators towards enhancing resilience of CPPSs against weather-related and cyber-related extreme events. The work presented in this dissertation also provides potential pathways to leverage existing system assets and resources integrated with recent advanced computational technologies to achieve resilient CPPSs