Optimal Operator Training Reference Models for Human-in-the-loop Systems

Simple item page
dc.contributor.authorHu, Wan-Lin
dc.contributor.authorRivetta, Claudio
dc.contributor.authorMacDonald, Erin
dc.contributor.authorChassin, David P.
dc.date.accessioned2019-01-03T00:16:40Z
dc.date.available2019-01-03T00:16:40Z
dc.date.issued2019-01-08
dc.description.abstractThe human operator is an integral part of a stable and safe power system. While there is increasing attention paid to automation improvements, the importance of understanding and training human operators may be understated. This paper discusses a project to enhance operator training programs by evaluating human performance relative to a reference operator model identified using optimal control theory. Along with establishing a simple computer-based operator workstation for future training purpose, this paper describes the optimal control response design methodology for a human-in-the-loop power system experiment. The overall system model is presented. An optimal controller synthesis methodology is applied to the model system and the optimal controller is designed. The performance of the optimal controller is then compared to human subject performance.
dc.format.extent8 pages
dc.identifier.doi10.24251/HICSS.2019.426
dc.identifier.isbn978-0-9981331-2-6
dc.identifier.urihttp://hdl.handle.net/10125/59788
dc.language.isoeng
dc.relation.ispartofProceedings of the 52nd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectResilient Networks
dc.subjectElectric Energy Systems
dc.subjecthuman-in-the-loop, operator training, optimal control, power system
dc.titleOptimal Operator Training Reference Models for Human-in-the-loop Systems
dc.typeConference Paper
dc.type.dcmiText

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
0348.pdf
Size:
1.09 MB
Format:
Adobe Portable Document Format
Download

Collections

Resilient Networks