Power quality disturbance classification using the inductive inference approach

This paper presents a novel approach for the classification of power quality disturbances. The approach is based on inductive learning by using decision trees. The wavelet transform is utilized to produce representative feature vectors that can accurately capture the unique and salient characteristics of each disturbance. In the training phase, a decision tree is developed for the power quality disturbances. The decision tree is obtained based on the features produced by the wavelet analysis through inductive inference. During testing, the signal is recognized using the rules extracted from the decision tree. The classification accuracy of the decision tree is not only comparable with the classification accuracy of artificial Neural networks, but also accounts for the explanation of the disturbance classification via the produced if... then rules.