Development and implementation of a multi-agent system for intelligent optimized power plant control.

DSpace/Manakin Repository

BEARdocs is currently undergoing a scheduled upgrade. We expect the upgrade to be completed no later than Monday, March 2nd, 2015. During this time you will be able to access existing documents, but will not be able to log in or submit new documents.

Show simple item record

dc.contributor.advisor Lee, Kwang Yun, 1942- Head, Jason D. 2012-05
dc.description.abstract As the demand for electric power grows and regulations on power plant operation become stricter, the size, and therefore complexity, of new power plant units is increasing while the intricacies of the multiple simultaneous processes that take place to generate electricity require tighter control. In order to provide a solution to some of the associated operational challenges arising from this situation, control techniques have been developed to allow optimized power plant control while considering non-fixed operating goals. Each of these techniques is computationally intensive, requiring a distributed, parallel control framework to implement each technique simultaneously in distributed subsystem environments. For these reasons, previous research has studied multi-agent systems as a means to implement such a control system. Therefore, the goal of this thesis is to fully develop a multi-agent system to coordinate and implement these techniques to control a third order fossil fuel power plant model. 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 control. en_US
dc.subject Multi-agent systems. en_US
dc.subject Multi-objective optimization. en_US
dc.subject Neural networks. en_US
dc.subject Model predictive control. en_US
dc.title Development and implementation of a multi-agent system for intelligent optimized power plant control. 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 1/13/14.
dc.contributor.department Engineering. en_US
dc.contributor.schools Baylor University. Dept. of Electrical and Computer Engineering. en_US

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search BEARdocs

Advanced Search


My Account