Enhancement of auxiliary overcurrent protection in transformers using novel feature extraction algorithms

Abstract

SUMMARYIn this paper, a feature extraction approach embedded with wavelet computation is applied to enhance the auxiliary overcurrent protection, by which the transformer inrush current discriminating from internal fault can be achieved. This proposed method starts with a feature extraction of acquired differential current waveform following the execution of wavelet computation. Then, the extracted features are expressed as a group of quantitative turning points to serve as indicators for overcurrent recognition. It is found that by this way of design, the inrush current can be effectively discriminated from the fault current. To validate the effectiveness of the method, it was applied to various simulated scenarios as well as employed to inspect a 60 MVA, 161/23.9 kV transformer connected to a 161 kV power system in Taiwan. Test results help support the feasibility of the method for the application considered.