{'fraction_non_alphanumeric': 0.010901883052527254, 'fraction_numerical': 0.0, 'mean_word_length': 5.688741721854305, '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}}
Genetic algorithms approach for the assessment of composite power system reliability considering multistate components

This paper introduces a genetic algorithms (GA) based approach for the assessment of composite power system reliability. This enhanced approach recognizes multistate components such as generation units with derated states. It also considers common mode failure for transmission lines. Binary encoded GA is used as a state sampling tool for the composite power system network states. Both annual and annualized adequacy indices are calculated. The superiority of the proposed approach over other conventional methods comes from the ability of GA to trace failure states in an intelligent, controlled and prespecified manner through the selection of a suitable fitness function. Case studies on a sample test system considering chronological load curves, derated states and common mode failures are presented. Results are analyzed to determine the effect of considering multistate components