A Wavelet-based Denoising Technique for Improved Monitoring and Characterization of Power Quality Disturbances

The excellent time-frequency localization property of the wavelet transform has made it a very promising tool for detection and analysis of the power quality disturbances. Many researchers have shown the adverse effect of noise on wavelet-based power quality monitoring and demonstrated that the performance of the wavelet transform in detecting the power quality disturbance would be greatly degraded due to the difficulty of distinguishing the noise and the disturbances. Practically, the power quality signals are often mixed with electromagnetic noise. This article proposes a denoising scheme of wavelet transform coefficients in noisy environment to avoid the false alarm rate and to increase the detection capability of wavelet transform-based power quality monitoring schemes. Contrary to the threshold-based techniques used so far in the power area for denoising power quality data, the technique used in this article exploits the local structure of wavelet coefficients. The effectiveness of the proposed technique is tested and demonstrated with both simulated and actual power line disturbance data for detection of power quality disturbances; the results obtained are among the best reported in the power quality literature.