Abstract
This research proposes a high-performance algorithm for the compression rate of electrical power quality signals, using wavelet transformation. To manage the massive amount of data the telecommunications networks are constantly acquiring it is necessary to study techniques for data compression, which will save bandwidth and reduce costs extensively by avoiding having massive data storage facilities. First biorthogonal wavelet level six transform is applied, however after compression, the reconstructed signal will have a different amplitude and it will be shifted when compared to the original one. Then, normalization is used (for amplitude correction between the original signal and reconstructed one) by multiplying the reconstructed signal by the result of the division between the original signal maximum magnitude and the reconstructed signal maximum magnitude. Thirdly, the ripple in the reconstructed signal is eliminated by applying a moving average filter. Finally, the shifting is corrected by finding the difference between the maximum points in a cycle of the original signal and the reconstructed one. After the compression algorithm was performed the best rates are 99.803% for compression rate, RTE 99.9479%, NMSE 0.000434, and Cross-Correlation 0.999925. Finally, this works presents two new performance criteria, compression time and recovery time, both of them in a real scenario will determinate how fast the algorithm can perform.
Highlights
Human migration from rural places to urban areas has been a constant phenomenon throughout human history
Among the results found in the literature review, retained energy percentage (RTE) varies from 97.80% to Besides, the compression process must analyze the quality of the processed signals after the compression is done, the best and most common approach is to normalize the mean square error, a low Normalized Mean Square Error (NMSE) corresponds to a small error between the reconstructed and the original one
Most of the authors concluded at first glance, that the reconstructed signal is similar to the original one, they have not taken into account aspects such as the change in amplitude, the ripple generated or the shift in the signal, all caused by characteristics of wavelets, this research eliminates those effects as it is described in Algorithm 2
Summary
Human migration from rural places to urban areas has been a constant phenomenon throughout human history. As a result of the massive growth of urbanization, a significant number of nonlinear loads are integrated into the power systems which reduce the predictability For this reason, monitoring and analysis are the focus (regarding power quality) as they are necessary to detect and classify disturbances at any particular point of the power system [3, 5, 10]. As previous works related to data compression techniques used for electrical signals in power quality management, the most important are listed as follows: In [5], the author analyzes that flickers, harmonics, and transients provide non-stationary characteristics to the electrical power system, Fourier transform is not enough for non-stationary electrical signals analysis.
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