I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
A C-DCGAN-based method for generating extreme risk scenario..:
, In:
2023 3rd International Conference on Intelligent Power and Systems (ICIPS)
,
Li, Yanchun
;
Li, Peng
;
Yang, Tianmeng
... - p. 588-593 , 2023
Link:
https://doi.org/10.1109/ICIPS59254.2023.10405272
RT T1 <cTitle>2023 3rd International Conference on Intelligent Power and Systems (ICIPS)</cTitle>: T1 <Title>A C-DCGAN-based method for generating extreme risk scenarios of high percentage new energy systems</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10405272&Exemplar=1&LAN=DE A1 Li, Yanchun A1 Li, Peng A1 Yang, Tianmeng A1 Chen, Zelong A1 Song, Xuan A1 Zhao, Yumin A1 Liu, Jicheng A1 Feng, Wei YR 2023 K1 Training K1 Measurement K1 Renewable energy sources K1 Power system stability K1 Stability analysis K1 Scenario generation K1 Load modeling K1 High proportional renewable energy systems K1 source-load risk scenarios K1 conditional adversarial generation networks K1 resilience enhancement K1 emergency recovery SP 588 OP 593 LK http://dx.doi.org/https://doi.org/10.1109/ICIPS59254.2023.10405272 DO https://doi.org/10.1109/ICIPS59254.2023.10405272 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
