AUTOMATIC CLASSIFICATION OF VOLTAGE EVENTS USING THE SUPPORT VECTOR MACHINE METHOD
Paper i proceeding, 2007

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.

Support vector machines

statistical learning

classification

electrical power quality

Författare

Peter G.V. Axelberg

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Irene Yu-Hua Gu

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Math H.J. Bollen

19th International Conference on Electricity Distribution (SIRED 2007) , Vienna, Austria, 21-24 May, 2007

Ämneskategorier

Signalbehandling

Annan elektroteknik och elektronik

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2017-10-06