GIS-based method to assess seismic vulnerability of interconnected infrastructure: A case of EU gas and electricity networks

Our study concerns the interconnected European Electricity and Gas transmission grid where we address two important issues of these interdependent critical infrastructures. First we assessed the response under seismic hazard for each independent network; then we analysed the increased vulnerability due to coupling between these two heterogeneous networks. We developed a probability reliability model that encompasses the spatial distribution of the network structures using a Geographic Information System (GIS). We applied the seismic risk assessment of individual network facilities and presented the results in the form of the system fragility curves of the (independent and dependant) networks in terms of various performance measures - connectivity loss, power loss, and impact on the population. We characterized the coupling behaviour between the two networks as a physical dependency: here the electricity grid, in part, depends on the gas network due to the generation capacity of gas-fired power plants. The dependence of one network on the other is modelled with an interoperability matrix, which is defined in terms of the strength of coupling; additionally we consider how the mechanical-structural fragility of the pipelines of the gas-source supply stream contributes to this dependence. In addition to network-wide assessment, damage was also evaluated at a local level by examining the performance status of each and every electricity distribution substation in the electricity grid. Finally, the comprehensive geographical distributions of performance loss at the European level can be visualized on a GIS tool; showing, as expected, that the highest direct damage in southeast Europe.JRC.DG.G.5-European laboratory for structural assessmen

Reliability Evaluation of Common-Cause Failures and Other Interdependencies in Large Reconfigurable Networks

This work covers the impact of Interdependencies and CCFs in large repairable networks with possibility of "re-configuration" after a fault and the consequent disconnection of the faulted equipment. Typical networks with these characteristics are the Utilities, e.g. Power Transmission and Distribution Systems, Telecommunication Systems, Gas and Water Utilities, Wi Fi networks. The main issues of the research are: (a) Identification of the specific interdependencies and CCFs in large repairable networks, and (b)Evaluation of their impact on the reliability parameters (load nodes availability, etc.). The research has identified (1) the system and equipment failure modes that are relevant to interdependencies and CCF, and their subsequent effects, and (2) The hidden interdependencies and CCFs relevant to control, supervision and protection systems, and to the automatic change-over systems, that have no impact in normal operation, but that can cause relevant out-of-service when the above automatic systems are called to operate under and after fault conditions. Additionally methods were introduced to include interdependencies and CCFs in the reliability and availability models. The results of the research include a new generalized approach to model the repairable networks for reliability analysis, including Interdependencies/CCFs as a main contributor. The method covers Generalized models for Nodes, Branches and Load nodes; Interdependencies and CCFs on Networks / Components; System Interdependencies/CCFs; Functional Interdependencies/CCFs; Simultaneous and non-simultaneous Interdependencies/CCFs. As an example detailed Interdependency/CCFs analysis and generalized model of an important network structure (a "RING" with load nodes) has been analyzed in detail

Experience with Restoration of Asia Pacific Network Failures from Taiwan Earthquake

We explain how network failures were caused by a natural disaster, describe the restoration steps that were taken, and present lessons learned from the recovery. At 21:26 on December 26th (UTC+9), 2006, there was a serious undersea earthquake off the coast of Taiwan, which measured 7.1 on the Richter scale. This earthquake caused significant damage to submarine cable systems. The resulting fiber cable failures shut down communications in several countries in the Asia Pacific networks. In the first post-earthquake recovery step, BGP routers detoured traffic along redundant backup paths, which provided poor quality connection. Subsequently, operators engineered traffic to improve the quality of recovered communication. To avoid filling narrow-bandwidth links with detoured traffic, the operators had to change the BGP routing policy. Despite the routing-level first aid, a few institutions could not be directly connected to the R&E network community because they had only a single link to the network. For these single-link networks, the commodity link was temporarily used for connectivity. Then, cable connection configurations at the switches were changed to provide high bandwidth and next-generation Internet service. From the whole restoration procedure, we learned that redundant BGP routing information is useful for recovering connectivity but not for providing available bandwidth for the re-routed traffic load and that collaboration between operators is valuable in solving traffic engineering issues such as poor-quality re-routing and lost connections of single-link networks

Impact Assessment of Hypothesized Cyberattacks on Interconnected Bulk Power Systems

The first-ever Ukraine cyberattack on power grid has proven its devastation by hacking into their critical cyber assets. With administrative privileges accessing substation networks/local control centers, one intelligent way of coordinated cyberattacks is to execute a series of disruptive switching executions on multiple substations using compromised supervisory control and data acquisition (SCADA) systems. These actions can cause significant impacts to an interconnected power grid. Unlike the previous power blackouts, such high-impact initiating events can aggravate operating conditions, initiating instability that may lead to system-wide cascading failure. A systemic evaluation of "nightmare" scenarios is highly desirable for asset owners to manage and prioritize the maintenance and investment in protecting their cyberinfrastructure. This survey paper is a conceptual expansion of real-time monitoring, anomaly detection, impact analyses, and mitigation (RAIM) framework that emphasizes on the resulting impacts, both on steady-state and dynamic aspects of power system stability. Hypothetically, we associate the combinatorial analyses of steady state on substations/components outages and dynamics of the sequential switching orders as part of the permutation. The expanded framework includes (1) critical/noncritical combination verification, (2) cascade confirmation, and (3) combination re-evaluation. This paper ends with a discussion of the open issues for metrics and future design pertaining the impact quantification of cyber-related contingencies

Impact Assessment, Detection, and Mitigation of False Data Attacks in Electrical Power Systems

The global energy market has seen a massive increase in investment and capital flow in the last few decades. This has completely transformed the way power grids operate - legacy systems are now being replaced by advanced smart grid infrastructures that attest to better connectivity and increased reliability. One popular example is the extensive deployment of phasor measurement units, which is referred to PMUs, that constantly provide time-synchronized phasor measurements at a high resolution compared to conventional meters. This enables system operators to monitor in real-time the vast electrical network spanning thousands of miles. However, a targeted cyber attack on PMUs can prompt operators to take wrong actions that can eventually jeopardize the power system reliability. Such threats originating from the cyber-space continue to increase as power grids become more dependent on PMU communication networks. Additionally, these threats are becoming increasingly efficient in remaining undetected for longer periods while gaining deep access into the power networks. An attack on the energy sector immediately impacts national defense, emergency services, and all aspects of human life. Cyber attacks against the electric grid may soon become a tactic of high-intensity warfare between nations in near future and lead to social disorder. Within this context, this dissertation investigates the cyber security of PMUs that affects critical decision-making for a reliable operation of the power grid. In particular, this dissertation focuses on false data attacks, a key vulnerability in the PMU architecture, that inject, alter, block, or delete data in devices or in communication network channels. This dissertation addresses three important cyber security aspects - (1) impact assessment, (2) detection, and (3) mitigation of false data attacks. A comprehensive background of false data attack models targeting various steady-state control blocks is first presented. By investigating inter-dependencies between the cyber and the physical layers, this dissertation then identifies possible points of ingress and categorizes risk at different levels of threats. In particular, the likelihood of cyber attacks against the steady-state power system control block causing the worst-case impacts such as cascading failures is investigated. The case study results indicate that false data attacks do not often lead to widespread blackouts, but do result in subsequent line overloads and load shedding. The impacts are magnified when attacks are coordinated with physical failures of generators, transformers, or heavily loaded lines. Further, this dissertation develops a data-driven false data attack detection method that is independent of existing in-built security mechanisms in the state estimator. It is observed that a convolutional neural network classifier can quickly detect and isolate false measurements compared to other deep learning and traditional classifiers. Finally, this dissertation develops a recovery plan that minimizes the consequence of threats when sophisticated attacks remain undetected and have already caused multiple failures. Two new controlled islanding methods are developed that minimize the impact of attacks under the lack of, or partial information on the threats. The results indicate that the system operators can successfully contain the negative impacts of cyber attacks while creating stable and observable islands. Overall, this dissertation presents a comprehensive plan for fast and effective detection and mitigation of false data attacks, improving cyber security preparedness, and enabling continuity of operations

Impact Assessment, Detection, And Mitigation Of False Data Attacks In Electrical Power Systems

The global energy market has seen a massive increase in investment and capital flow in the last few decades. This has completely transformed the way power grids operate - legacy systems are now being replaced by advanced smart grid infrastructures that attest to better connectivity and increased reliability. One popular example is the extensive deployment of phasor measurement units, which is referred to PMUs, that constantly provide time-synchronized phasor measurements at a high resolution compared to conventional meters. This enables system operators to monitor in real-time the vast electrical network spanning thousands of miles. However, a targeted cyber attack on PMUs can prompt operators to take wrong actions that can eventually jeopardize the power system reliability. Such threats originating from the cyber-space continue to increase as power grids become more dependent on PMU communication networks. Additionally, these threats are becoming increasingly efficient in remaining undetected for longer periods while gaining deep access into the power networks. An attack on the energy sector immediately impacts national defense, emergency services, and all aspects of human life. Cyber attacks against the electric grid may soon become a tactic of high-intensity warfare between nations in near future and lead to social disorder. Within this context, this dissertation investigates the cyber security of PMUs that affects critical decision-making for a reliable operation of the power grid. In particular, this dissertation focuses on false data attacks, a key vulnerability in the PMU architecture, that inject, alter, block, or delete data in devices or in communication network channels. This dissertation addresses three important cyber security aspects - (1) impact assessment, (2) detection, and (3) mitigation of false data attacks. A comprehensive background of false data attack models targeting various steady-state control blocks is first presented. By investigating inter-dependencies between the cyber and the physical layers, this dissertation then identifies possible points of ingress and categorizes risk at different levels of threats. In particular, the likelihood of cyber attacks against the steady-state power system control block causing the worst-case impacts such as cascading failures is investigated. The case study results indicate that false data attacks do not often lead to widespread blackouts, but do result in subsequent line overloads and load shedding. The impacts are magnified when attacks are coordinated with physical failures of generators, transformers, or heavily loaded lines. Further, this dissertation develops a data-driven false data attack detection method that is independent of existing in-built security mechanisms in the state estimator. It is observed that a convolutional neural network classifier can quickly detect and isolate false measurements compared to other deep learning and traditional classifiers. Finally, this dissertation develops a recovery plan that minimizes the consequence of threats when sophisticated attacks remain undetected and have already caused multiple failures. Two new controlled islanding methods are developed that minimize the impact of attacks under the lack of, or partial information on the threats. The results indicate that the system operators can successfully contain the negative impacts of cyber attacks while creating stable and observable islands. Overall, this dissertation presents a comprehensive plan for fast and effective detection and mitigation of false data attacks, improving cyber security preparedness, and enabling continuity of operations