Abstract: Sales of privately-owned plug-in electric vehicles (PEVs) are projected to increase dramatically in coming years and their charging will impact residential service transformer loads. Transformer life expectancy is strongly related to the cumulative effects of internal winding temperatures, which are a function of loading. Thermal models exist (e.g., IEEE Standard C57.91) for predicting these internal temperatures, the most sophisticated being the Annex G model. While this model has been validated with measurements from large power transformers, small residential service transformers have been given less attention. Given increasing PEV loads, a better understanding of service transformer aging could be useful in replacement planning processes. Empirical data from this paper indicate that the Annex G model over-estimates internal temperatures in small 25 kVA 65 °C rise mineral-oil-immersed transformers. This paper presents an alternative model to Annex G by using a genetic program. Empirical results using a thermally-instrumented transformer suggest that this model is both simpler and more accurate at tracking empirical transformer data. We conclude that one can use a simple thermal model in combination with data from advanced metering infrastructure to more accurately estimate service transformer lifetimes, and thus better plan for transformer replacement.
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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).