A Neural Lyapunov Approach to Transient Stability Assessment in Interconnected Microgrids

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dc.contributor.authorHuang, Tong
dc.contributor.authorGao, Sicun
dc.contributor.authorLong, Xun
dc.contributor.authorXie, Le
dc.date.accessioned2020-12-24T19:41:04Z
dc.date.available2020-12-24T19:41:04Z
dc.date.issued2021-01-05
dc.description.abstractWe propose a neural Lyapunov approach to assessing transient stability in power electronic-interfaced microgrid interconnections. The problem of transient stability assessment is cast as one of learning a neural network-structured Lyapunov function in the state space. Based on the function learned, a security region is estimated for monitoring the security of interconnected microgrids in real-time operation. The efficacy of the approach is tested and validated in a grid-connected microgrid and a three-microgrid interconnection. A comparison study suggests that the proposed method can achieve a less conservative characterization of the security region, as compared with a conventional approach.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2021.405
dc.identifier.isbn978-0-9981331-4-0
dc.identifier.urihttp://hdl.handle.net/10125/71020
dc.language.isoEnglish
dc.relation.ispartofProceedings of the 54th 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.subjectinterconnected microgrids
dc.subjectmachine learning
dc.subjectneural lyapunov approach
dc.subjectpower grid resilience
dc.subjecttransient stability
dc.titleA Neural Lyapunov Approach to Transient Stability Assessment in Interconnected Microgrids
prism.startingpage3330

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