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AUTOMATIC CLASSIFICATION OF VOLTAGE EVENTS USING THE SUPPORT VECTOR MACHINE METHOD

Peter G.V. Axelberg (Institutionen för signaler och system, Signalbehandling) ; Irene Y.H. Gu (Institutionen för signaler och system, Signalbehandling) ; Math H.J. Bollen
19th International Conference on Electricity Distribution (SIRED 2007) , Vienna, Austria, 21-24 May, 2007 (2007)
[Konferensbidrag, refereegranskat]

Statistically based classification systems need to be trained on a large number of training data in order to classify unseen data accurately. However, it is difficult to gather enough voltage events for the training purpose from real recordings. Therefore, a classification system trained to accurately classify real voltage events, but based on synthetic training data is highly in demand. This paper therefore proposes the design of a statistically based classification system trained on synthetic data. The paper gives also the results of conducted performance tests when the proposed classification system was trained to classify seven common types of voltage events. The experiments showed an overall detection rate of 81.6%, 91.9% and 99.5% respectively.

Nyckelord: Support vector machines, classification, electrical power quality, statistical learning



Denna post skapades 2007-01-19.
CPL Pubid: 25873

 

Institutioner (Chalmers)

Institutionen för signaler och system, Signalbehandling

Ämnesområden

Signalbehandling
Elkraftteknik

Chalmers infrastruktur