Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/71020

A Neural Lyapunov Approach to Transient Stability Assessment in Interconnected Microgrids

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Title:A Neural Lyapunov Approach to Transient Stability Assessment in Interconnected Microgrids
Authors:Huang, Tong
Gao, Sicun
Long, Xun
Xie, Le
Keywords:Resilient Networks
interconnected microgrids
machine learning
neural lyapunov approach
power grid resilience
show 1 moretransient stability
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Date Issued:05 Jan 2021
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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/71020
ISBN:978-0-9981331-4-0
DOI:10.24251/HICSS.2021.405
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Resilient Networks


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