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Learning-Based, Runtime Reachability Analysis of Microgrid Dynamics
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DOI：10.1109/TPWRS.2024.3498447.png)
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Abstract
En 中文
Reachable dynamics (ReachDyn) is a powerful tool for verifying microgrid dynamics under extensive uncertainties, which, however, faces significant challenges in runtime efficiency and numerical stability. This paper devises Neural-ReachDyn, a learning-based reachable dynamics approach to support the runtime uncertain dynamic analysis of microgrids. Our contributions include: (1) set-based Neural-ReachDyn formulation, which establishes neural network-represented ellipsoids for enclosing possible microgrid dynamics under uncertainties in a data-driven manner; (2) set-based Neural-ReachDyn training, which develops an axial length-based loss function to train the reachable set towards conservativeness and tightness with enhanced robustness. Case studies in a typical droop-based microgrid validate the accuracy, efficiency, and adaptability of the devised method under different uncertainties and operating scenarios.
Keywords:
Microgrid
reachability analysis
data-driven dynamic analysis
data-driven dynamic analysis
uncertainty
uncertainty
machine learning
machine learning
machine learning
Journal
IF:
7.2
Papers:
1.1W
Citations:
5.0W