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181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 12$aA machine learning framework for bridging the gap between the steady-state scheduling and dynamic security operation for future power grids /$fJin Tan
210  1$a[Golden, Colo.] :$cNational Renewable Energy Laboratory,$d2021.
215   $a1 online resource (23 pages) $ccolor illustrations, color maps
225 1 $aNREL/PR ;$v5C00-80488
300   $aSlideshow presentation.
300   $a"7/26/2021; presented at IEEE PES GM 2021."
606   $aMachine learning
606   $aElectric power distribution$xSecurity measures$zUnited States
615  0$aMachine learning.
615  0$aElectric power distribution$xSecurity measures
700   $aTan$b Jin$c(Engineer),$01202843
712 02$aNational Renewable Energy Laboratory (U.S.),
712 02$aUnited States.$bDepartment of Energy.$bSolar Energy Technologies Office,
801  0$bGPO
801  1$bGPO
906   $aBOOK
912   $a9910717268403321
996   $aA machine learning framework for bridging the gap between the steady-state scheduling and dynamic security operation for future power grids$93445209
997   $aUNINA