{'fraction_non_alphanumeric': 0.04090909090909091, 'fraction_numerical': 0.03068181818181818, 'mean_word_length': 5.204225352112676, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 1, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Application of DA‐preconditioned FINN for electric power system fault detection

This 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. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 166(2): 39– 46, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20497