A robust energy features estimation for detection and classification of power quality disturbances

Recently, energy distribution of wavelet coefficient at different resolution level has been found to be very effective discriminatory feature for classification of power quality (PQ) disturbances. In practice, signals captured by monitoring devices are often corrupted by noise. The presence of noise will change the energy distribution pattern and may result in increased false classification rate. The robustness of the energy features, extracted for classification in the presence of noise and its effect on classification accuracy, which has been rarely discussed, need to be addressed. Recognizing such importance and necessity, this paper discusses the effect of noise on classification accuracy and proposes a low complexity robust denoising scheme in wavelet domain, to extract the required energy features for automatic classification of PQ disturbances. The "noise variance preserving property of Daubechies wavelet across the time-frequency scales, is used to estimate the noise energy at different resolution levels. The proposed approach is demonstrated for various PQ disturbances