Applications of fuzzy-logic-wavelet-based techniques for transformers inrush currents identification and power systems faults classification

The advent of wavelet transforms (WTs) and fuzzy-inference mechanisms (FIMs) with the ability of the first to focus on system transients using short data windows and of the second to map complex and nonlinear power system configurations provide an excellent tool for high speed digital relaying. This work presents a new approach to real-time fault classification in power transmission systems, and identification of power transformers magnetising inrush currents using fuzzy-logic-based multicriteria approach Omar A.S. Youssef [2004, 2003] with a wavelet-based preprocessor stage Omar A.S. Youssef [2003, 2001]. Three inputs, which are functions of the three line currents, are utilised to detect fault types such as LG, LL, LLG as well as magnetising inrush currents. The technique is based on utilising the low-frequency components generated during fault conditions on the power system and/or magnetising inrush currents. These components are extracted using an online wavelet-based preprocessor stage with data window of 16 samples (based on 1.0 kHz sampling rate and 50 Hz power frequency). Generated data from the simulation of an 330 Δ/33Y kV, step-down transformer connected to a 330 kV model power system using EMTP software were used by the MATLAB program to test the performance of the technique as to its speed of response, computational burden and reliability. Results are shown and they indicate that this approach can be used as an effective tool for high-speed digital relaying, and that computational burden is much simpler than the recently postulated fault classification.