Power quality disturbance data compression using wavelet transform methods

TLDR

The authors propose a wavelet‑based compression method for power quality disturbance data. The method decomposes the signal, thresholds wavelet coefficients using a scale‑dependent weighting of the maximum absolute value, discards coefficients below the threshold, and reconstructs the signal from the retained coefficients and their temporal locations. Applied to real disturbance data, the technique reduces file size to one‑sixth to one‑third of the original, significantly lowering storage and transmission costs.

Abstract

In this paper, the authors present a wavelet compression technique for power quality disturbance data. The compression technique is performed through signal decomposition, thresholding of wavelet transform coefficients and signal reconstruction. Threshold values are determined by weighting the absolute maximum value at each scale. Wavelet transform coefficients whose values are below the threshold are discarded, while those that are above the threshold are kept along with their temporal locations. The authors show the efficacy of the technique by compressing actual disturbance data. The file size of the compressed data is only one-sixth to one-third that of the original data. Therefore, the cost related to storing and transmitting the data is significantly reduced.