A Neural Lyapunov Approach to Transient Stability Assessment in Interconnected Microgrids
A Neural Lyapunov Approach to Transient Stability Assessment in Interconnected Microgrids
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Date
2021-01-05
Authors
Huang, Tong
Gao, Sicun
Long, Xun
Xie, Le
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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.
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Keywords
Resilient Networks,
interconnected microgrids,
machine learning,
neural lyapunov approach,
power grid resilience,
transient stability
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