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Verfasst von:Zhang, Wei   i
 Song, Chen [VerfasserIn]   i
 Heuveline, Vincent [VerfasserIn]   i
Titel:Extraction of spatial-temporal features of bus loads in electric grids through clustering in a dynamic model space
Verf.angabe:Wei Zhang, Gang Mu, Chen Song, Gangui Yan, and Vincent Heuveline
Jahr:2020
Jahr des Originals:2019
Umfang:10 S.
Fussnoten:date of publication December 30, 2019 ; Gesehen am 27.04.2020
Titel Quelle:Enthalten in: Institute of Electrical and Electronics EngineersIEEE access
Ort Quelle:New York, NY : IEEE, 2013
Jahr Quelle:2020
Band/Heft Quelle:8(2020), Seite 5852-5861
ISSN Quelle:2169-3536
Abstract:Bus loads in electric grids have inherently a spatial-temporal behavior and also a certain degree of randomness. The spatial-temporal feature based bus load forecasting, which provides additional information on the spatial distribution and the uncertainty of future electric loads, is of importance to power systems dispatching and planning, in particular, with intermittent renewable power generation. In this paper, a method for extracting spatial-temporal features, including abnormal states of multiple bus loads in electric grids, is proposed. The abnormal spatial load states are firstly identified by using one-class support vector machine. Then, only the load fluctuations of normal states are mapped into a dynamic model space supported by polynomials in order to approximate the time series of bus loads. The parameters of polynomials are clustered by the Dirichlet process mixture model for deriving the patterns of load state evolution. As a result, the extracted spatial-temporal patterns are a set of different distributions of bus loads with static features and dynamic features displayed explicitly. The method is tested against the bus loads of an electric grid in a city in the Northeast China. The proposed methodology is validated with respect to the bus loads in time slots of the future 10 days.
DOI:doi:10.1109/ACCESS.2019.2963071
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext: https://doi.org/10.1109/ACCESS.2019.2963071
 DOI: https://doi.org/10.1109/ACCESS.2019.2963071
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Dirichlet process mixture model
 dynamic model space clustering
 electric grid
 feature extraction
 intermittent renewable power generation
 load forecasting
 Load forecasting
 load state evolution
 mixture models
 multiple bus loads
 Northeast China
 one-class support vector machine
 pattern clustering
 polynomials
 power engineering computing
 power grids
 power system dispatching
 power system planning
 spatial-temporal feature
 spatial-temporal feature based bus load forecasting
 spatial-temporal feature extraction
 support vector machines
 time 10.0 d
 time series
K10plus-PPN:1696244692
Verknüpfungen:→ Zeitschrift

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