Abstract
In this paper, a wavelet based power transformer protection algorithm, which is one of the most important components of power system, is proposed. Traditionally, power transformers (rated power greater than 1.5MVA) are protected by differential protection scheme. Its function supported by discrete Fourier transform is fundamentally based on comparison of primary and secondary currents of the transformer and dedicated to discriminate internal faults and magnetizing inrush phenomena. In this way, the relay (differential protection relay) is ensured to be more reliable and selective. Nevertheless, the concept of transformer protection has been keeping up-to-date and several numerical protection algorithms have been suggested for this purpose. In this work, maximal overlap discrete wavelet transform (MODWT) based transformer differential protection technique is proposed. The features vectors obtained by MODWT are then processed by artificial neural network (ANN) to classify the normal, magnetizing, and internal fault conditions. The obtained results are also compared to traditional discrete wavelet transform (DWT) and interpreted using performance curves.
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