SUMMARYThis paper proposes a hybrid method of deterministic annealing (DA) and fuzzy inference neural network (FINN) for electric power system fault detection. It extracts features of input data with two-staged precondition of fast Fourier transform (FFT) and DA. FFT is useful for extracting the features of fault currents while DA plays a key role in classifying input data into clusters in a sense of global classification. FINN is a more accurate estimation model than the conventional artificial neural networks (ANNs). The proposed method is successfully applied to data obtained by the Tokyo Electric Power Company (TEPCO) power simulator.
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