Tools

LEADER 02804cam 2200565 i 4500

001 on1407189132;

005 20260127093944.0;

006 m o d f ;

007 cr |||||||||||;

008 231018s2022 coua ob f100 0 eng d;

035 a| (Sirsi) o1407189132;

035 a| (OCoLC)1407189132;

043 a| n-us---;

049 a| ERE#;

074 a| 0430-P-04 (online);

086 0 a| E 9.17:NREL/CP-6A40-82116;

100 1 a| Liu, Xuebo,e| author.?| UNAUTHORIZED;

245 12 a| A comparison of machine learning methods for frequency nadir estimation in power systems :b| preprint /c| Xuebo Liu [and six others].;

264 1 a| Golden, CO :b| National Renewable Energy Laboratory,c| 2022.;

300 a| 1 online resource (5 pages) :b| color illustrations.;

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

490 1 a| NREL/CP ;v| 6A40-82116;

500 a| "May 2022.";

500 a| External Note: See NREL/CP-6A40-83783 for paper as published in proceedings;

500 a| "Presented at the IEEE Kansas Power & Energy Conference, April 25-26, 2022, Manhattan, Kansas"--Cover.;

500 a| This is a publication issued by the United States federal government or produced with federal funds.;

500 a| In scope of the U.S. Government Publishing Office Cataloging and Indexing Program (C&I) and Federal Depository Library Program (FDLP).;

504 a| Includes bibliographical references (pages 4-5).;

506 a| Publicly released.;

513 a| Conference paper.;

536 a| Sponsored by the U.S Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy Technologies Officeb| DE-AC36-08GO28308d| 34224d| 37772;

588 a| Description based on online resource; title from PDF title page (NREL, viewed September 17, 2025).;

650 0 a| Electric power system stability.=| ^A103053;

650 0 a| Electric power systemsz| United Statesx| Data processing.=| ^A80405;

650 0 a| Machine learning.=| ^A145211;

655 7 a| Conference papers and proceedings.2| lcgft;

710 2 a| National Renewable Energy Laboratory (U.S.)e| issuing body.=| ^A367790;

830 0 a| Conference paper (National Renewable Energy Laboratory (U.S.)) ;v| NREL/CP-6A40-82116.=| ^A755847;

856 40 u| https://purl.fdlp.gov/GPO/gpo2486517| 0;

856 4 z| Address at time of PURL creationu| https://www.nrel.gov/docs/fy22osti/82116.pdf;

949 a| Click on web addressw| asish| joyner94o| jjlm;

994 a| C0b| GPO;

955 a| bc108 20251118b| 20251118;

922 a| DOCDISCOVERYb| 20251118;

596 a| 1;

998 a| 6653181;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6653181-1001l| JNETm| JOYNERr| Ys| Yt| JNE1DOu| 12/3/2025x| EDOCUMENTz| JERESOURCEo| .STAFF. jjlm;

A comparison of machine learning methods for frequency nadir estimation in power systems : preprint / Xuebo Liu [and six others].

Author/creator	Liu, Xuebo author.
Other author	National Renewable Energy Laboratory (U.S.) issuing body.
Format	Electronic
Publication	Golden, CO : National Renewable Energy Laboratory, 2022.
Description	1 online resource (5 pages) : color illustrations.
Supplemental Content	https://purl.fdlp.gov/GPO/gpo248651
Supplemental Content	Address at time of PURL creation
Subjects	Electric power system stability. Electric power systems--United States. --Data processing. Machine learning.

Series	NREL/CP ; 6A40-82116 Conference paper (National Renewable Energy Laboratory (U.S.)) ; NREL/CP-6A40-82116. ^A755847
General note	"May 2022."
General note	External Note: See NREL/CP-6A40-83783 for paper as published in proceedings
General note	"Presented at the IEEE Kansas Power & Energy Conference, April 25-26, 2022, Manhattan, Kansas"--Cover.
General note	This is a publication issued by the United States federal government or produced with federal funds.
General note	In scope of the U.S. Government Publishing Office Cataloging and Indexing Program (C&I) and Federal Depository Library Program (FDLP).
Bibliography note	Includes bibliographical references (pages 4-5).
Access restriction	Publicly released.
Report note	Conference paper.
Funding information	Sponsored by the U.S Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy Technologies Office DE-AC36-08GO28308 34224 37772
Source of description	Description based on online resource; title from PDF title page (NREL, viewed September 17, 2025).
Genre/form	Conference papers and proceedings.
GPO item number	0430-P-04 (online)
Govt. docs number	E 9.17:NREL/CP-6A40-82116

A comparison of machine learning methods for frequency nadir estimation in power systems : preprint / Xuebo Liu [and six others].

Click here for more information about this title