Transformer differential protection using fault-generated highfrequency transient components

Power transformers are the most important components of a power system, so their protection is a critical issue. This paper proposes a novel and efficient algorithm based on the high-frequency components of the differential current signal to discriminate between the magnetizing inrush currents and the internal faults. After detecting the over-current in the differential current signals, samples of a quarter of a cycle of the signal are recorded. Then, discrete wavelet transform (DWT) is applied to the recorded signals, and the details of the wavelet transform output are extracted. Because of the existence of the high-frequency transients in the internal fault current signals, the wavelet transform outputs of the internal fault signals have more fluctuations than that of the inrush current signals. By calculating the standard deviation of the wavelet transform output, the fluctuations can be quantified. Therefore, the standard deviation of the wavelet transform output can be used as a criterion to discriminate between the internal faults and the magnetizing inrush currents. The proposed algorithm has a very low computational burden, and it uses only a quarter of a cycle of the differential current signals. This guarantees the high speed of the proposed algorithm. The proposed algorithm is tested by different conditions of the internal faults and the inrush situations, and it successfully identifies the true situation with high accuracy in all conditions. The simulation results show the superior specifications of the proposed algorithm.