FAST SVMS FOR POWER QUALITY DATA MINING

Abstract

Recognition of the presence of any disturbance and classifying any existing disturbance into a particular type is the first step in combating the power quality problem. Support Vector Machines (SVMs) have gained wide acceptance because of the high generalization ability for a wide range of classification applications. Although SVMs have shown potential and promising performance in power disturbances classification, they have been limited by speed particularly when the training data set is large. The hyper plane constructed by SVM is dependent on only a portion of the training samples called support vectors that lie close to the decision boundary (hyper plane). Thus, removing any training samples that are not relevant to support vectors might have no effect on building the proper decision function. We propose the use of clustering techniques such as K-mean to find initial clusters that are further altered to identify non-relevant samples in deciding the decision boundary for SVM. This will help to reduce the number of training samples for SVM without degrading the classification result and classification time can be significantly reduced.