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- W4387006057 abstract "Management of the nation’s power grid over the coming decades will need to consider multiple climate-driven threats to the power system that can be stressful or even detrimental to its operations. During disasters, grid operators suffer from cognitive overload where the electric grid is severely impacted by the weather, yet grid operators have limited awareness of these factors. Meanwhile, the electric grid is facing an explosion of data coming from a variety of sources which can enable the operator to evaluate the risks and develop mitigation strategies against hazardous events. In this work, we processed heterogeneous environmental and power grid data to learn and model grid behaviour caused by extreme weather events. In this study, we focused on two weather-driven hazards, hurricanes and wildfires, which were analysed for the electric grid of Texas. We then used this data to train a Reinforcement Learning (RL) agent by analysing and predicting future behaviour of the grid during those hazard events. All these parts are incorporated in one framework to have a geophysically informed power simulators that can be used to train RL agents." @default.
- W4387006057 created "2023-09-26" @default.
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- W4387006057 date "2023-07-16" @default.
- W4387006057 modified "2023-09-26" @default.
- W4387006057 title "Developing a Disaster-Ready Power Grid Agent Through Geophysically-Informed Fault Event Scenarios" @default.
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- W4387006057 doi "https://doi.org/10.1109/pesgm52003.2023.10252367" @default.
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