Akademik Veri Yönetim Sistemi
SCIENTIFIC REPORTS, cilt.16, sa.1, 2025 (SCI-Expanded, Scopus)
Directional Overcurrent Relay (DOCR) coordination has become an increasingly important problem in modern distribution networks, where growing system complexity and high fault-current variability demand fast and highly selective protection strategies. With the widespread integration of smart grids and renewable energy sources, determining relay settings accurately and in an optimized manner is critical to maintaining system reliability. This study aims to systematically evaluate the effectiveness of five advanced generation metaheuristic algorithms (Zebra Optimization Algorithm (ZOA), African Vultures Optimization Algorithm (AVOA), Transit Search Optimization (TSO), Nutcracker Optimization Algorithm (NOA), and Artificial Rabbits Optimization (ARO)) for minimizing the total primary relay operating time. In this context, the DOCR coordination problem is formulated as an optimization task in which the Time Multiplier Setting (TMS) and Pickup Setting (PS) are jointly tuned under inverse time-current characteristics. A standard and reproducible MATLAB/Simulink-based analysis framework is established, and the three-phase short-circuit fault scenario that produces the highest fault current is consistently applied to the IEEE 3-, 8-, and 15-bus test systems. To ensure the reliability of the findings, multiple repeated simulations are performed and the results are statistically validated. The numerical results show that ARO achieves the lowest total primary relay operating time in both the IEEE 3-bus (0.23 s) and 8-bus (2.86 s) systems compared with the other algorithms considered (ZOA, AVOA, TSO, and NOA), while NOA provides the best performance in the 15-bus system with a total operating time of 5.25 s, outperforming ARO, ZOA, AVOA, and TSO. When compared with classical and other heuristic methods reported in the literature for the same or similar test systems, the proposed advanced algorithms reduce the total primary relay operating time by approximately 60% to 97%, while still satisfying the selectivity requirements. Furthermore, the results indicate that algorithm performance is sensitive to network scale and topology: ARO is more effective in small and medium-sized systems, NOA performs better in larger systems, and TSO exhibits balanced behaviour across all test systems. Overall, the findings demonstrate that advanced metaheuristic algorithms provide effective, scalable, and reliable solutions for DOCR coordination, highlighting their high applicability potential in future protection schemes for smart-grid and renewable-integrated distribution networks.