Machine Learning Based Energy Management System for Improvement of Critical Reliability

dc.contributor.ORCID0000-0002-3577-0633 (Maharjan, L)
dc.contributor.advisorFahimi, Babak
dc.contributor.committeeMemberBalsara, Poras T.
dc.contributor.committeeMemberPrakash, Ravi
dc.contributor.committeeMemberBhatia, Dinesh
dc.creatorMaharjan, Lizon
dc.date.accessioned2019-10-03T18:59:49Z
dc.date.available2019-10-03T18:59:49Z
dc.date.created2019-05
dc.date.issued2019-05
dc.date.submittedMay 2019
dc.date.updated2019-10-03T18:59:49Z
dc.description.abstractCurrent power distribution system faces challenges that originate from aging infrastructure, DER integration, and frequent natural disasters. At the same time, the contingencies are one of the leading causes of economic loss and loss of life during natural disasters. Hence, improvement of reliability of power to critical loads during such emergency scenarios is one of the key features to be addressed during the development of a future power distribution system. An ideal solution will include a retro-fit device that can utilize existing infrastructure and integrate DERs advantageously. One of such solutions has been visualized using Advanced Micro Grid systems. The presented study utilizes Machine learning algorithms to create energy management systems for such micro-grids with the goal of maximizing power availability to the critical loads of the community. The residential level electronics with integrated artificial intelligence have been designed, developed, and tested as a part of this effort.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10735.1/6959
dc.language.isoen
dc.rights©2019 Lizon Maharjan
dc.subjectPower electronics
dc.subjectElectric power systems
dc.subjectMicrogrids (Smart power grids)
dc.subjectMachine learning
dc.subjectReliability (Engineering)
dc.titleMachine Learning Based Energy Management System for Improvement of Critical Reliability
dc.typeDissertation
dc.type.materialtext
thesis.degree.departmentElectrical Engineering
thesis.degree.grantorThe University of Texas at Dallas
thesis.degree.levelDoctoral
thesis.degree.namePHD

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