{'fraction_non_alphanumeric': 0.018578352180936994, 'fraction_numerical': 0.0032310177705977385, 'mean_word_length': 5.321428571428571, '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}}
A Machine Learning Approach to the Identification of Voltage Control Area Using Synchrophasor Measurements

Voltage instability has caused a great deal of concern in the existing power systems. Despite best efforts, it is still not uncommon a phenomenon and has caused some of the major blackouts and catastrophic failures in the recent past, resulting in huge social and economic losses. The advent of synchrophasor technology has made possible wide-area measurements in real-time, and has found huge applications in power systems. The identification of subregions in power systems that experience a unique voltage instability problem is one of the most important steps of voltage stability analysis. This paper presents a method to identify voltage control area (VCA), based on coherent groups of buses, using system states obtained from synchronized phasor measurements. The coherency is identified by applying hierarchical clustering, a machine learning technique. The coherent buses are identified by applying the machine learning technique on the angles obtained from bus voltage phasors. The results so obtained using the data from Phasor Measurement Units (PMUs) on 10-machine, 39-bus New England power system model are presented.