{'fraction_non_alphanumeric': 0.012244897959183673, 'fraction_numerical': 0.004081632653061225, 'mean_word_length': 5.169811320754717, '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}}
RBF Neural Network approach for security assessment and enhancement

Security assessment is the major concern in real-time operation of electric power systems. Traditionally, security evaluation method involves running full load flow and rotor dynamics analysis for each contingency, results as an infeasible method for real time application. This paper presents an approach for security assessment and enhancement using Radial Basis Function Neural Network (RBFNN). The security of the system is assessed in terms of security indices based on the intensity of both steady state and transient disturbances. The necessary corrective control action to be taken in the event of disturbance is also proposed and the effect of this action has also been observed in order to enhance the security. Usage of RBFNN improves the response time compared to other neural networks. The effectiveness of the proposed method is illustrated using IEEE 14 bus and IEEE 39 bus standard test systems.