Physics Informed Reinforcement Learning for Power Grid Control using Augmented Random Search

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dc.contributor.authorMahapatra, Kaveri
dc.contributor.authorFan, Xiaoyuan
dc.contributor.authorLi, Xinya
dc.contributor.authorHuang, Yunzhi
dc.contributor.authorHuang, Qiuhua
dc.date.accessioned2021-12-24T17:50:04Z
dc.date.available2021-12-24T17:50:04Z
dc.date.issued2022-01-04
dc.description.abstractWide adoption of deep reinforcement learning in energy system domain needs to overcome several challenges , including scalability, learning from limited samples, and high-dimensional continuous state and action spaces. In this paper, we integrated physics-based information from the generator operation state formula, also known as Swing Equation, into the reinforcement learning agent's neural network loss function, and applied an augmented random search agent to optimize the generator control under dynamic contingency. Simulation results demonstrated the reliability performance improvements in training speed, reward convergence, and future potentials in its transferability and scalability.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2022.427
dc.identifier.isbn978-0-9981331-5-7
dc.identifier.urihttp://hdl.handle.net/10125/79762
dc.language.isoeng
dc.relation.ispartofProceedings of the 55th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectMonitoring, Control, and Protection
dc.subjectaugmented random search
dc.subjectphysics informed machine learning
dc.subjectphysics informed neural network
dc.subjectpower grid control
dc.titlePhysics Informed Reinforcement Learning for Power Grid Control using Augmented Random Search
dc.type.dcmitext

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