{'fraction_non_alphanumeric': 0.024390243902439025, 'fraction_numerical': 0.0, 'mean_word_length': 5.345454545454546, '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}}
Design of a neural network based power system stabilizer in reduced order power system

In this paper, a simple artificial neural network power system stabilizer (SANN-PSS) is implemented using a reduced order power system. Knowledge of the high bus voltage of a transformer is employed to alter the Heffron-Phillips' method. Furthermore, a reduction technique is applied to change the power system's sequence, and increase the power system's stability by adjusting the controller's parameters in real time in response to modifications in the operating conditions. The simulation results confirm and enhance the power system's stability with the suggested SANN-PSS at altered operating conditions.