Compression of electrical power signals from waveform records using genetic algorithm and artificial neural network

This paper proposes a methodology for compression of electrical power signals from waveform records in electric systems, using genetic algorithm (GA) and artificial neural network (ANN). The genetic algorithm is used to select and preserve the points that better characterize the waveform contours; and the artificial neural network is used in the compression of other points as well as on the signal reconstruction process. Thus, the data resulting from the proposed methodology are formed by a part of the original signal and by a compressed complementary part in the form of synaptic weights. The proposed methodology selects and preserves a percentage of the original signal samples, which are aspects not explored in the literature. The method was tested using field data obtained from an oscillographic recorder installed in a 230 kV electrical power system. The results presented compression rates ranging from 8.59:1.00 to 24.16:1.00 for preservation rates ranging from 2.5% to 10%, respectively.