Optimal feature selection for classification of power quality disturbances using wavelet packet-based fuzzy k-nearest neighbour algorithm

The authors present an automatic classification of different power quality (PQ) disturbances using wavelet packet transform (WPT) and fuzzy k-nearest neighbour (FkNN) based classifier. The training data samples are generated using parametric models of the PQ disturbances. The features are extracted using some of the statistical measures on the WPT coefficients of the disturbance signal when decomposed up to the fourth level. These features are given to the fuzzy k-NN for effective classification. The genetic algorithm-based feature vector selection is done to ensure good classification accuracy by selecting 16 better features from all 96 features generated from the WPT coefficients. The necessity of selecting the best feature is to remove the redundant or irrelevant features, which may reduce the performance of the classification. It also reduces the computation time since it uses only 16 features instead of 96 features. The experimental analysis for the validation of the proposed algorithm is carried out in two stages. At the first stage, the sample data set is generated by varying the parameters in models in regular intervals and the proposed algorithm is applied to select the best features to obtain high accuracy. In the second stage, a new data set is generated by choosing the parameter values, which are not used in the first case and used to test the accuracy of the classifier with the same selected features as in stage one. The noisy and practical signals are also considered for the classification process to show the effectiveness of the proposed method.