Wavelet based denoising of power quality events for characterization

Abstract

The effectiveness of wavelet transform (WT) methods for analyzing different power quality (PQ) events with or without noise
has been demonstrated in this paper. Multi-resolution signal decomposition based on discrete WT is used to localize and to
classify different power quality disturbances. The energy distribution at different levels using MRA is unique for a disturbance
and can be used as a feature for automatic classification of the power quality events. The PQ event duration and energy
distribution of pure sine voltage wave, voltage sag, swell, transients, harmonics, impulse, notching, fluctuation and flicker are
obtained using wavelet transform. The presence of noise degrades the detection capability of wavelet based method and
therefore effect of noise on different signal is analyzed. The noise corrupted signal is de-noised using different wavelets and the
effectiveness of the wavelets in denoising is demonstrated.