02140nam 2200505 450 991071388670332120200824102805.0(CKB)5470000002504823(OCoLC)1190590210(EXLCZ)99547000000250482320200824d2020 ua 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierIdentification of worst impact zones for power grids during extreme weather events using Q-learning preprint /Shuva Paul and Fei DingGolden, CO :National Renewable Energy Laboratory,2020.1 online resource (5 pages) illustrations (some color)Conference paper ;NREL/CP-5D00-74737"February 2020.""Presented at the 2020 IEEE Conference on Innovative Smart Grid Technologies (IEEE ISGT), Washington, D.C., February 17-20 2020"--Page 1 of cover.Includes bibliographical references (page 5).Identification of worst impact zones for power grids during extreme weather events using Q-learning Electric power distributionUnited StatesComputer simulationEmergency managementUnited StatesNatural disastersUnited StatesInfrastructure (Economics)United StatesElectric power failuresUnited StatesComputer simulationReinforcement learningUnited StatesElectric power distributionComputer simulation.Emergency managementNatural disastersInfrastructure (Economics)Electric power failuresComputer simulation.Reinforcement learningPaul Shuva1410153Ding FeiNational Renewable Energy Laboratory (U.S.),GPOGPOBOOK9910713886703321Identification of worst impact zones for power grids during extreme weather events using Q-learning3534625UNINA