Support Vector Machine for Classification of Voltage Disturbances
Journal article, 2007
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.
statistical learning theory
Support Vector Machines
voltage disturbance classification