Improved Transient Data Compression Algorithm Based on Wavelet Spectral Quantization Models

In this work a dedicated encoding algorithm is presented for current and voltage transient signals digitalized from electrical networks. New models for dynamic bit allocation based on adaptive and fixed spectral envelope estimation models for transformed coefficients are proposed. The fixed profiles adjust the number of bits to be used by the wavelet transform decomposition level, which is used in the transformed vector quantization. The models proposed to estimate the adaptive spectral envelope have the transformed spectrum segmented by subbands or by decomposition levels of the wavelet transform. The dynamic bit allocation is implemented according to the signal spectral behavior. The quantized coefficient vector is encoded using an entropy coder, and it is asynchronously packaged in a compressed file. A parametric configuration for optimization algorithm is also proposed for the fixed spectral profile models in order to improve performance. Simulation results are presented using a signal data bank with a set of reported events of electric power networks. Performance comparisons with other work are also presented.