A Random Chemistry Algorithm for Identifying Collections of Multiple Contingencies that Initiate Cascading Failure
IEEE Transactions on Power Systems, 27, 1698-1705, 2012
Abstract: This paper describes a stochastic Random Chemistry (RC) algorithm to identify large collections of multiple (n-k) contingencies that initiate large cascading failures in a simulated power system. The method requires only O(log (n)) simulations per contingency identified, which is orders of magnitude faster than random search of this combinatorial space. We applied the method to a model of cascading failure in a power network with n=2896 branches and identify 148243 unique, minimal n-k branch contingencies (2 ≤ k ≤ 5) that cause large cascades, many of which would be missed by using pre-contingency flows, linearized line outage distribution factors, or performance indices as screening factors. Within each n-k collection, the frequency with which individual branches appear follows a power-law (or nearly so) distribution, indicating that a relatively small number of components contribute disproportionately to system vulnerability. The paper discusses various ways that RC generated collections of dangerous contingencies could be used in power systems planning and operations.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).