A comparative study on effective signal processing tools for optimum feature selection in automatic power quality events clustering

The paper presents a comparative study to investigate the optimum feature selection using three signal processing techniques for automatic clustering of power quality events. The techniques include the wavelet transform, the S transform, and the newly introduced forward Clarke transform. The last method has the advantage for monitoring all three phases of a three-phase signal simultaneously. The paper provides unique features for each transformation, and then offers a comparative study that is based on the abilities of selected pairs of features to distinguish power quality events. In the paper, the performance of each signal processing technique is studied and an optimum combination of the most useful features is identified.