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Support Vector Machine for Classification of Voltage Disturbances

Peter G.V. Axelberg (Institutionen för signaler och system, Signalbehandling) ; Irene Y.H. Gu (Institutionen för signaler och system, Signalbehandling) ; Math H.J. Bollen
accepted for publication in IEEE Transactions on Power Delivery Vol. 22 (2007), 3, p. 1297-1303, July, 2007.
[Artikel, refereegranskad vetenskaplig]

The Support Vector Machine (SVM) is a powerful method for statistical classification of data used in a number of different applications. However, the usefulness of the method in a commercial available system is very much dependent on whether the SVM classifier can be pre-trained from a factory since it is not realistic that the SVM classifier must be trained by the customers themselves before it can be used. We first propose a novel SVM classification system for voltage disturbances. Our aim also includes investigating the performance of the proposed SVM classifier when the voltage disturbance data used for training and testing are originated from different sources. The data used in the experiments were originated from both real disturbances recorded in two different power networks and from synthetic data. The experimental results have shown excellent accuracy in classification when training data were originated from one power network and unseen testing data from another. High accuracy was also achieved when the SVM classifier was trained on data from a real power network and test data originated from synthetic data. Slightly less accuracy was achieved when the SVM classifier was trained on synthetic data and test data were originated from the power network.

Nyckelord: Power quality, statistical learning theory, Support Vector Machines, voltage disturbance classification, voltage event.

Denna post skapades 2006-08-25. Senast ändrad 2008-01-03.
CPL Pubid: 22059


Institutioner (Chalmers)

Institutionen för signaler och system, Signalbehandling (1900-2017)



Chalmers infrastruktur