{'fraction_non_alphanumeric': 0.008284023668639054, 'fraction_numerical': 0.0, 'mean_word_length': 5.313432835820896, '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}}
New Design of a Neural Network Algorithm for Detecting and Classifying Trans-mission Line Faults

This paper introduces a new artificial intelligent based approach for detecting and classifying the faults in power system networks. This approach utilizes unique type of neural network specially developed to deal with large amount of input data. A model of an actual power network is implemented in ATP program and used for simulating the fault scenarios on transmission lines. Protective algorithm is implemented in MATLAB and interacts with the network model simulations in ATP. Procedures of generating training and testing patterns are performed carefully to ensure covering of all possible events. Training and testing phases of the neural network algorithm are optimized to improve classification of a variety of previously unseen patterns.