Empirical-Mode Decomposition With Hilbert Transform for Power-Quality Assessment

The aim of this paper is to develop a method based on combination of empirical-mode decomposition (EMD) and Hilbert transform for assessment of power quality events. A distorted waveform can be conceived as superimposition of various oscillating modes and EMD is used to separate out these intrinsic modes known as intrinsic mode functions (IMF). Hilbert transform is applied to first three IMF to obtain instantaneous amplitude and phase which are then used for constructing feature vector. The work evaluates the detection capability of the methodolpogy and a comparison with S-transform is made to show the superiority of the technique in detecting the PQ disturbance like voltage spike and notch. A probabilistic neural network is used as a mapping function for identifying the various disturbance classes. Results show a better classification accuracy of the methodology.