Noise Reduction and Anomaly Detection in Power Quality Monitoring Using Wavelet Transforms

Author

Mr. Nitin Sharma

Abstract

 Modern power distribution networks are undergoing a radical shift due to the widespread integration of renewable energy sources, power electronic interfaces, non-linear industrial loads, and high-frequency switching converters. These dynamics significantly corrupt power quality (PQ) signals with additive Gaussian noise, dynamic harmonics, transient spikes, voltage sags/swells, and flicker. Standard Fourier-based spectral analysis fails under non-stationary conditions, masking transient anomalies under high noise floors. This paper proposes a rigorous, comprehensive framework for joint noise reduction and anomaly detection in Power Quality Monitoring (PQM) utilizing Discrete Wavelet Transforms (DWT) and Stationary Wavelet Transforms (SWT). An adaptive VisuShrink/BayesShrink soft-thresholding scheme is established for de-noising high-frequency detail sub-bands without smearing steep transient boundaries. Subsequently, a dynamic Parseval Energy Norm and Wavelet Packet Singular Value Decomposition (WPSVD) feature extraction algorithm is implemented for automated anomaly localization and multi-class PQ event classification. Extensive experimental evaluations on synthetic and field-recorded IEEE 1159 benchmark datasets demonstrate an average Signal-to-Noise Ratio (SNR) improvement of 18.4 dB, a Peak Signal-to-Noise Ratio (PSNR) of 42.1 dB, and an anomaly classification accuracy exceeding 98.7% under extreme low initial SNR conditions (SNR = 5 dB).

Keywords

Power Quality Monitoring (PQM), Wavelet Transform (WT), Noise Reduction, Anomaly Detection, Discrete Wavelet Transform (DWT), Stationary Wavelet Transform (SWT), Parseval Energy, Smart Grid Analytics.

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References

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