Distributed Monitoring for Prevention of Cascading Failures in Operational Power Grids

Electrical power grids are vulnerable to cascading failures that can lead to large blackouts. Detection and prevention of cascading failures in power grids is impor- tant. Currently, grid operators mainly monitor the state (loading level) of individual components in power grids. The complex architecture of power grids, with many interdependencies, makes it difficult to aggregate data provided by local compo- nents in a timely manner and meaningful way: monitoring the resilience with re- spect to cascading failures of an operational power grid is a challenge. This paper addresses this challenge. The main ideas behind the paper are that (i) a robustness metric based on both the topology and the operative state of the power grid can be used to quantify power grid robustness and (ii) a new proposed a distributed computation method with self-stabilizing properties can be used to achieving near real-time monitoring of the robustness of the power grid. Our con- tributions thus provide insight into the resilience with respect to cascading failures of a dynamic operational power grid at runtime, in a scalable and robust way. Com- putations are pushed into the network, making the results available at each node, allowing automated distributed control mechanisms to be implemented on top

Power Grid Vulnerability to Geographically Correlated Failures - Analysis and Control Implications

We consider power line outages in the transmission system of the power grid, and specifically those caused by a natural disaster or a large scale physical attack. In the transmission system, an outage of a line may lead to overload on other lines, thereby eventually leading to their outage. While such cascading failures have been studied before, our focus is on cascading failures that follow an outage of several lines in the same geographical area. We provide an analytical model of such failures, investigate the model's properties, and show that it differs from other models used to analyze cascades in the power grid (e.g., epidemic/percolation-based models). We then show how to identify the most vulnerable locations in the grid and perform extensive numerical experiments with real grid data to investigate the various effects of geographically correlated outages and the resulting cascades. These results allow us to gain insights into the relationships between various parameters and performance metrics, such as the size of the original event, the final number of connected components, and the fraction of demand (load) satisfied after the cascade. In particular, we focus on the timing and nature of optimal control actions used to reduce the impact of a cascade, in real time. We also compare results obtained by our model to the results of a real cascade that occurred during a major blackout in the San Diego area on Sept. 2011. The analysis and results presented in this paper will have implications both on the design of new power grids and on identifying the locations for shielding, strengthening, and monitoring efforts in grid upgrades

Self-healing systems and virtual structures

Modern networks are large, highly complex and dynamic. Add to that the mobility of the agents comprising many of these networks. It is difficult or even impossible for such systems to be managed centrally in an efficient manner. It is imperative for such systems to attain a degree of self-management. Self-healing i.e. the capability of a system in a good state to recover to another good state in face of an attack, is desirable for such systems. In this paper, we discuss the self-healing model for dynamic reconfigurable systems. In this model, an omniscient adversary inserts or deletes nodes from a network and the algorithm responds by adding a limited number of edges in order to maintain invariants of the network. We look at some of the results in this model and argue for their applicability and further extensions of the results and the model. We also look at some of the techniques we have used in our earlier work, in particular, we look at the idea of maintaining virtual graphs mapped over the existing network and assert that this may be a useful technique to use in many problem domains

Cascading Link Failure in the Power Grid: A Percolation-Based Analysis

Large-scale power blackouts caused by cascading failure are inflicting enormous socioeconomic costs. We study the problem of cascading link failures in power networks modelled by random geometric graphs from a percolation-based viewpoint. To reflect the fact that links fail according to the amount of power flow going through them, we introduce a model where links fail according to a probability which depends on the number of neighboring links. We devise a mapping which maps links in a random geometric graph to nodes in a corresponding dual covering graph. This mapping enables us to obtain the first-known analytical conditions on the existence and non-existence of a large component of operational links after degree-dependent link failures. Next, we present a simple but descriptive model for cascading link failure, and use the degree-dependent link failure results to obtain the first-known analytical conditions on the existence and non-existence of cascading link failures

Predicting Failures in Power Grids: The Case of Static Overloads

Here we develop an approach to predict power grid weak points, and specifically to efficiently identify the most probable failure modes in static load distribution for a given power network. This approach is applied to two examples: Guam's power system and also the IEEE RTS-96 system, both modeled within the static Direct Current power flow model. Our algorithm is a power network adaption of the worst configuration heuristics, originally developed to study low probability events in physics and failures in error-correction. One finding is that, if the normal operational mode of the grid is sufficiently healthy, the failure modes, also called instantons, are sufficiently sparse, i.e. the failures are caused by load fluctuations at only a few buses. The technique is useful for discovering weak links which are saturated at the instantons. It can also identify generators working at the capacity and generators under capacity, thus providing predictive capability for improving the reliability of any power network.Comment: 11 pages, 10 figure

Cascading Failures in Finite-Size Random Geometric Networks

The problem of cascading failures in cyber-physical systems is drawing much attention in lieu of different network models for a diverse range of applications. While many analytic results have been reported for the case of large networks, very few of them are readily applicable to finite-size networks. This paper studies cascading failures in finite-size geometric networks where the number of nodes is on the order of tens or hundreds as in many real-life networks. First, the impact of the tolerance parameter on network resiliency is investigated. We quantify the network reaction to initial disturbances of different sizes by measuring the damage imposed on the network. Lower and upper bounds on the number of failures are derived to characterize such damages. Such finite-size analysis reveals the decisiveness and criticality of taking action within the first few stages of failure propagation in preventing a cascade. By studying the trend of the bounds as the number of nodes increases, we observe a phase transition phenomenon in terms of the tolerance parameter. The critical value of the tolerance parameter, known as the threshold, is further derived. The findings of this paper, in particular, shed light on how to choose the tolerance parameter appropriately such that a cascade of failures could be avoided

Critical Utility Infrastructural Resilience

The paper refers to CRUTIAL, CRitical UTility InfrastructurAL Resilience, a European project within the research area of Critical Information Infrastructure Protection, with a specific focus on the infrastructures operated by power utilities, widely recognized as fundamental to national and international economy, security and quality of life. Such infrastructures faced with the recent market deregulations and the multiple interdependencies with other infrastructures are becoming more and more vulnerable to various threats, including accidental failures and deliberate sabotage and malicious attacks. The subject of CRUTIAL research are small scale networked ICT systems used to control and manage the electric power grid, in which artifacts controlling the physical process of electricity transportation need to be connected with corporate and societal applications performing management and maintenance functionality. The peculiarity of such ICT-supported systems is that they are related to the power system dynamics and its emergency conditions. Specific effort need to be devoted by the Electric Power community and by the Information Technology community to influence the technological progress in order to allow commercial intelligent electronic devices to be effectively deployed for the protection of citizens against cyber threats to electric power management and control systems. A well-founded know-how needs to be built inside the industrial power sector to allow all the involved stakeholders to achieve their service objectives without compromising the resilience properties of the logical and physical assets that support the electric power provision

Modeling and Analysis of Cascading Failures in Interdependent Cyber-Physical Systems

Integrated cyber-physical systems (CPSs), such as the smart grid, are increasingly becoming the underpinning technology for major industries. A major concern regarding such systems are the seemingly unexpected large-scale failures, which are often attributed to a small initial shock getting escalated due to intricate dependencies within and across the individual counterparts of the system. In this paper, we develop a novel interdependent system model to capture this phenomenon, also known as cascading failures. Our framework consists of two networks that have inherently different characteristics governing their intra-dependency: i) a cyber-network where a node is functional as long as it belongs to the largest connected (i.e., giant) component; and ii) a physical network where nodes are given an initial flow and a capacity, and failure of a node results with redistribution of its flow to the remaining nodes, upon which further failures might take place due to overloading (i.e., the flow of a node exceeding its capacity). Furthermore, it is assumed that these two networks are inter-dependent. For simplicity, we consider a one-to-one interdependency model where every node in the cyber-network is dependent upon and supports a single node in the physical network, and vice versa. We provide a thorough analysis of the dynamics of cascading failures in this interdependent system initiated with a random attack. The system robustness is quantified as the surviving fraction of nodes at the end of cascading failures, and is derived in terms of all network parameters involved (e.g., degree distribution, load/capacity distribution, failure size, etc.). Analytic results are supported through an extensive numerical study. Among other things, these results demonstrate the ability of our model to capture the unexpected nature of large-scale failures and provide insights on improving system robustness

Randomized Distributed Configuration Management of Wireless Networks: Multi-layer Markov Random Fields and Near-Optimality

Distributed configuration management is imperative for wireless infrastructureless networks where each node adjusts locally its physical and logical configuration through information exchange with neighbors. Two issues remain open. The first is the optimality. The second is the complexity. We study these issues through modeling, analysis, and randomized distributed algorithms. Modeling defines the optimality. We first derive a global probabilistic model for a network configuration which characterizes jointly the statistical spatial dependence of a physical- and a logical-configuration. We then show that a local model which approximates the global model is a two-layer Markov Random Field or a random bond model. The complexity of the local model is the communication range among nodes. The local model is near-optimal when the approximation error to the global model is within a given error bound. We analyze the trade-off between an approximation error and complexity, and derive sufficient conditions on the near-optimality of the local model. We validate the model, the analysis and the randomized distributed algorithms also through simulation.Comment: 15 pages, revised and submitted to IEEE Trans. on Networkin

Communication Networks: Dynamic Traffic Distribution and Spatial Diffusion Disruptions

This thesis concerns robust load allocation in communication networks. The main goal of this work is to avoid the situation in which the failure of a node (or nodes) causes a cascade of failures through an entire network, with a sequence of healthy nodes becoming overloaded and failing from picking up the slack from previously failed nodes. The network should remain functional even after some of the nodes have failed. In the dissertation we present a new methodology for dynamically distributing the load across a network so as to avoid the overloading of any of the networks nodes. A numerical solution is proposed to solve this model and build a simulation tool. This numerical method adjusts the classic explicit form of Runge-Kutta 4th order in order to integrate graph principles and produce synchronized numerical solutions for each network element. Unlike most solutions in the literature, as for example, Motter et al. [2002], Motter and Adilson [2004], Liang et al. [2004], Schafer et al. [2006], Wang and Kim [2007] and Ping et al. [2007] our methodology is generic in the sense that it works on any network topology. This means not only that it is applicable to a large range of networks, but also that it continues to be relevant after failure has destroyed part of a network, thereby changing the topology. In particular, geographical catastrophes can be of both random and intended types, taking place within a heterogeneous physical environment, on a civil (populated) area. Unlike most fault methodologies in the literature our methodology is generic in the sense that it simulates real-world geographic failure propagation towards any type of network which can be embedded to a two dimenional metric system [Chen and He, 2004], Liu et al. [2000], Callaway et al. [2000], Albert and Barabasi [2000]. It describes how physical one dimensional catastrophic waves spread in heterogeneous environments and how built–in resilience, within each network element determines its percentage of damage. We have tested our system on various randomly generated graphs with faults injected according to a model we have developed that simulates real-world geographic failure propagation. We present results from our dynamic traffic distribution methodology applied to networks, which have been either under attack or not. Throughout our case studies we prove that as soon as the topology is assigned the appropriate resources comparing to the load that it is to serve, our methodology successfully redistributes the load across the network and prevents a potential cascade failure. We either prevent the propagation of cascading failures or suggest recovery strategies after an unavoidable failure. Therefore, our methodology is instrumental in designing and testing reliable and robust networks