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dc.contributor.authorVenayagamoorthy, Ganesh Kumareng
dc.contributor.corporatenameUniversity of Missouri (System)eng
dc.contributor.meetingnameMissouri Energy Summit (2009 : University of Missouri--Columbia)eng
dc.date.issued2009eng
dc.descriptionOnly abstract of poster available.eng
dc.descriptionTrack III: Energy Infrastructureeng
dc.description.abstractThe electric power network of today is a complex adaptive system under semi-autonomous distributed control at the generation, transmission and distribution systems. It is spatially and/or temporally complex, nonconvex, nonlinear, nonstationary with uncertainties. The integration of renewable energy sources such as wind farms, solar farms and plug-in hybrid vehicles further adds complexity and challenges to the various controllers at all levels of the power network. A lot of efforts have gone into the development of a smart grid to align the interests of the electric utilities, consumers and environmentalists. Intelligent computational methods are required for planning and optimization, fast control of power system elements, processing of field data and coordination across the grid. Distributed and coordinated intelligence at all levels and across levels of the electric power grid - generation, transmission and distribution is inevitable if a true smart grid is to be reality. The traditional way of modeling, control and optimization needs to be augmented, or even replaced in some cases, with intelligent techniques capable of rapid adaptation, having dynamic foresight, being fault-tolerant and robust to disturbances and randomness. Computational intelligence (CI) is the study of adaptive mechanisms to enable or facilitate intelligent behavior in complex, uncertain and changing environments. These adaptive mechanisms include those artificial intelligence paradigms that exhibit an ability to learn or adapt to new situations, to generalize, abstract, discover and associate. The paradigms of CI mimic nature for solving complex problems. CI is successor of artificial intelligence and is the way of the future computing. This paper describes the potentials and promises of the computational intelligence for smart grid operation and control.eng
dc.description.sponsorshipNames of PI and co-PIs: G. Kumar Venayagamoorthy (PI), R Harley, S Potter and D Wunsch Title of Project: EFRI-COPN: Neuroscience and Neural Networks for Engineering the Future Intelligent Electric Power Grid Funding Agency: NSF Amount and Period of Performance: Nov. 1, 2008 to Oct. 31, 2012 Names of PI: G. Kumar Venayagamoorthy Title of Project: NSF CAREER: Scalable Learning and Adaptation with Intelligent Techniques and Neural Networks for Reconfiguration and Survivability of Complex Systems Funding Agency: NSF Amount and Period of Performance: Jun. 1, 2004 to May 31, 2010eng
dc.identifier.urihttp://hdl.handle.net/10355/1107
dc.languageEnglisheng
dc.relation.ispartofPosters (Missouri Energy Summit 2009)eng
dc.relation.ispartofcommunityUniversity of Missouri System. Missouri Summits. Missouri Energy Summit 2009eng
dc.subjectEnergy Infrastructureeng
dc.subjectelectric power networkeng
dc.subjectCIeng
dc.subject.lcshElectric power distribution -- Data processingeng
dc.subject.lcshComputational intelligenceeng
dc.titleThe Potentials and Promises of the Future Way of Computing for Smart Grids [abstract]eng
dc.typeAbstracteng


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