Automatic Classification of Power Quality Events Using Balanced Neural Tree

This paper proposes an empirical-mode decomposition (EMD) and Hilbert transform (HT)-based method for the classification of power quality (PQ) events. Nonstationary power signal disturbance waveforms are considered as the superimposition of various undulating modes, and EMD is used to separate out these intrinsic modes known as intrinsic mode functions (IMFs). The HT is applied on all the IMFs to extract instantaneous amplitude and frequency components. This time-frequency analysis results in the clear visual detection, localization, and classification of the different power signal disturbances. The required feature vectors are extracted from the time-frequency distribution to perform the classification. A balanced neural tree is constructed to classify the power signal patterns. Finally, the proposed method is compared with an S-transform-based classifier to show the efficacy of the proposed technique in classifying the PQ disturbances.