Divergence Distance Based Index for Discriminating Inrush and Internal Fault Currents in Power Transformers

Abstract

This article puts forward a new algorithm based on Kullback–Leibler divergence (KLD) for discriminating inrush and internal fault currents in power transformers. Specifically, the main idea of this algorithm is to utilize the current signal discrepancy of the distribution with ideal fault current waveforms. To such an aim, the proposed index reproduces the differential current signal. Utilizing fast modified least squares technique, the differential current signal is generated. Applying the reconstructed differential current and ideal sinusoidal waveforms to the KLD indicator, the distribution discrepancy of the current signal from ideal sinusoidal waveform discriminates inrush and internal fault currents. Also, the internal/external identification index is introduced that utilizes the phase content of the signals from current transformers (CTs) on both sides to distinguish internal and external faults, especially the faults accompanied with CT saturation. As a result, the proposed method can distinguish inrush currents, internal and external fault currents with/without CT saturation, and internal fault currents during transformer energization. The performance of the proposed index is evaluated using practically recorded data and is further compared with the state of the art.