A Neural Lyapunov Approach to Transient Stability Assessment in Interconnected Microgrids
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Date
2021-01-05
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3330
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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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Resilient Networks, interconnected microgrids, machine learning, neural lyapunov approach, power grid resilience, transient stability
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10 pages
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Proceedings of the 54th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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