Abstract: With the continued deployment of synchronized
Phasor Measurement Units (PMUs), high sample rate data are
rapidly increasing the real time observability of power systems.
Prior research has shown that the statistics of these data can
provide useful information regarding network stability, but it is
not yet known how this statistical information can be actionably
used to improve power system stability. To address this issue, this
paper presents a method that gauges and improves the voltage
stability of a system using the statistics present in PMU data
streams. Leveraging an analytical solver to determine a range of
“critical” bus voltage variances, the presented methods monitor
raw statistical data in an observable load pocket to determine
when control actions are needed to mitigate the risk of voltage
collapse. A simple reactive power controller is then implemented,
which acts dynamically to maintain an acceptable voltage stability
margin within the system. Time domain simulations on 3-bus
and 39-bus test cases demonstrate that the resulting statistical
controller can out-perform more conventional feedback control
systems by maintaining voltage stability margins while loads
simultaneously increase and fluctuate.
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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).