{'fraction_non_alphanumeric': 0.0210016155088853, 'fraction_numerical': 0.0016155088852988692, 'mean_word_length': 6.0397727272727275, '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}}
Coordination Index for Directional Overcurrent Relays using Multiobjective C-DEEPSO Approach

Directional Overcurrent Relays can be dynamically and quickly configured using optimization techniques making the electrical network more resilient. In this work, the directional overcurrent relay coordination is modeled as mixed integer non-linear problem that minimizes the sum of relays operating times and maximizes their coordination intervals. To solve this, the use of Multiobjective Canonical Differential Evolutionary Particle Swarm Optimization is proposed in this work. The effects of different probabilistic distributions (normal, uniform, triangular and qui-squared ones) to initialize the decision variables (time dials, pickup currents, curves and indexes) are also discussed in this work. With the proposed method a reduction in convergence speed of more than 50% can be obtained when the adequate probabilistic distribution is used. The decrease in time can also be enhanced if a different acceptable reduction of the Coordination Time Interval is considered resulting in different and robust solutions. Keywords— Power System Protection, Computational intelligence, Optimization, Modelling and Simulation, Blackout Prevention.