A Neural Lyapunov Approach to Transient Stability Assessment in Interconnected Microgrids

dc.contributor.author Huang, Tong
dc.contributor.author Gao, Sicun
dc.contributor.author Long, Xun
dc.contributor.author Xie, Le
dc.date.accessioned 2020-12-24T19:41:04Z
dc.date.available 2020-12-24T19:41:04Z
dc.date.issued 2021-01-05
dc.description.abstract We 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.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2021.405
dc.identifier.isbn 978-0-9981331-4-0
dc.identifier.uri http://hdl.handle.net/10125/71020
dc.language.iso English
dc.relation.ispartof Proceedings of the 54th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Resilient Networks
dc.subject interconnected microgrids
dc.subject machine learning
dc.subject neural lyapunov approach
dc.subject power grid resilience
dc.subject transient stability
dc.title A Neural Lyapunov Approach to Transient Stability Assessment in Interconnected Microgrids
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