01542nam 2200385 450 991071726840332120220310155220.0(CKB)5470000002529540(OCoLC)1302888551(EXLCZ)99547000000252954020220310d2021 ua 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierA machine learning framework for bridging the gap between the steady-state scheduling and dynamic security operation for future power grids /Jin Tan[Golden, Colo.] :National Renewable Energy Laboratory,2021.1 online resource (23 pages) color illustrations, color mapsNREL/PR ;5C00-80488Slideshow presentation."7/26/2021; presented at IEEE PES GM 2021."Machine learningElectric power distributionSecurity measuresUnited StatesMachine learning.Electric power distributionSecurity measuresTan Jin(Engineer),1202843National Renewable Energy Laboratory (U.S.),United States.Department of Energy.Solar Energy Technologies Office,GPOGPOBOOK9910717268403321A machine learning framework for bridging the gap between the steady-state scheduling and dynamic security operation for future power grids3445209UNINA