Design and implementation of a multi-agent optimized control system for a large-scale fossil-fuel electrical power unit.

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dc.contributor.advisor Lee, Kwang Yun, 1942- Williams, Craig S. (Craig Stevens) 2011-12
dc.description.abstract The problem facing the United Sates electric power industry today can be attributed to society’s ever increasing demand for energy, environmental concerns with reliance on fossil fuels, and uncertainty about an aging infrastructure’s ability to cope with increasing demand for energy. Existing control systems for power plants are rigid and lack the capability to provide optimal operation with increasing amounts of requirements placed on the power plants, prompting the need for a more adaptive, robust control system. The object of this thesis aims to develop and present an optimized control system based on the concept of Multi-Agent Systems (MASs), which have been applied to other complex problems in the power industry. This thesis applies a MAS distributed control methodology to a large-scale power plant optimized control system, improving the overall flexibility, autonomy, and robustness of the control system, which in turn increases the efficiency and operation of the power plant. en_US
dc.publisher en
dc.rights Baylor University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact for inquiries about permission. en_US
dc.subject Power plant model identification. en_US
dc.subject Power plant control. en_US
dc.subject Multi-objective optimization. en_US
dc.subject Reference governor. en_US
dc.subject Multi-agent systems. en_US
dc.title Design and implementation of a multi-agent optimized control system for a large-scale fossil-fuel electrical power unit. en_US
dc.type Thesis en_US M.S.E.C.E. en_US
dc.rights.accessrights Worldwide access. en_US
dc.rights.accessrights Access changed 7/1/13.
dc.contributor.department Engineering. en_US
dc.contributor.schools Baylor University. Dept. of Electrical and Computer Engineering. en_US

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