LEADER 01548oam  2200409I  450 
001 9910711921003321
005 20190822092040.0
035   $a(CKB)5470000002488885
035   $a(OCoLC)1089929542
035   $a(OCoLC)995470000002488885
035   $a(EXLCZ)995470000002488885
100   $a20190315d2019      ua             0
101 0 $aeng
135   $aur|||||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aApplication of the Koopman operator-theoretic framework to power system dynamic state estimation /$fMarcos Netto [and four others]
210  1$a[Golden, Colorado] :$cNational Renewable Energy Laboratory,$d[2019].
215   $a1 online resource (1 page) $ccolor illustrations
225 1 $aNREL/PO ;$v5D00-73236
300   $a"Operator Theoretic Methods in Dynamic Data Analysis and Control Institute for Pure and Applied Mathematics (IPAM) - Los Angeles, CA, February 11-15, 2019."
320   $aIncludes bibliographical references (page 1).
606   $aElectric power systems
606   $aElectric power systems$2fast
615  0$aElectric power systems.
615  7$aElectric power systems.
700   $aNetto$b Marcos$01401224
712 02$aNational Renewable Energy Laboratory (U.S.),
801  0$bGPO
801  1$bGPO
801  2$bOCLCF
801  2$bGPO
906   $aBOOK
912   $a9910711921003321
996   $aApplication of the Koopman operator-theoretic framework to power system dynamic state estimation$93526506
997   $aUNINA