{'fraction_non_alphanumeric': 0.02640845070422535, 'fraction_numerical': 0.008215962441314555, 'mean_word_length': 5.067615658362989, '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 Fast and Reliable Quadratic Approach for Q-adjustments in Fast Decoupled Load Flow Model

The paper presents a new fast, reliable and relatively simple decoupled quadratic load flow (DQLF) algorithm for Q-adjustments in power flow solutions. Q-adjusted solutions are inevitable for reactive power planning and management studies. For solving power flow problems, the fast decoupled load flow (FDLF) is probably the most popular, because of its efficiency. But for Q-adjusted studies, the matrix updating problem associated with B" matrix remains unresolved. Refactorization of B" matrix demands more CPU time. The proposed approach eliminates formation and refactorization of B" matrix, for both well behaved and ill-conditioned systems of Q-unadjusted and adjusted cases. The new method minimizes the computational burden by solving for the busbar voltage magnitudes (V) using a non-linear quadratic equation and it reduces the execution time significantly. The solution of this nonlinear equation undoubtedly offers better solution than that of a linear version. Improved and reliable convergence on normal and ill-conditioned system is expected. Enforcement of Q-limits is also very simple and effective. The proposed algorithm also proved to handle large degrees of ill-conditioning in Q-adjusted studies compared to the standard FDLF model. The performance of the proposed model is investigated by a number of case studies on IEEE test systems (14, 30, 57 & 118 bus) and results are reported for IEEE 118 bus system. The results indicate the established better convergence and reliability of the proposed model. It is at least 50% faster than the traditional FDLF model for Q-adjusted case studies