{'fraction_non_alphanumeric': 0.02169811320754717, 'fraction_numerical': 0.0, 'mean_word_length': 5.430303030303031, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, '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}}
ANN-based technique for fault location estimation using TLS-ESPRIT

In this paper, a methodology is proposed for identifying the fault location in transmission lines. The required features for the proposed algorithm is extracted from transient currents or voltages waveforms measured at the substation using the total least square-estimation of signal parameters via rotational invariance technique (TLS-ESPRIT). Since these transient waveforms are considered as a summation of damped sinusoids, TLS-ESPRIT is used to estimate different signal parameters mainly damping factors, frequencies and amplitudes of different modes contained in the signal. These parameters are functions of the fault location, system damping and fault time. Those features can then be employed for fault location identification. Artificial neural networks (ANNs) are used to estimate the fault location using this modal information. Training and testing data are generated using PSCAD/EMTDC simulations. It is crucial to indicate that no pre-fault data is required in this algorithm.