Optimal over-current relay coordination in distribution network using grew wolf optimization

Abstract This paper introduces a novel approach to address the optimal coordination of directional overcurrent relays (DOCRs) in modern power distribution networks. By utilizing the different optimization methods such as genetic algorithm (GA), particle swarm optimization (PSO), pattern search (PS), grey wolf optimization (GWO), the study aims to tackle the inherent complexity and nonlinearity of the relay coordination problem effectively. GWO stands out due to its ability to handle highly nonlinear optimization problems by leveraging the social behavior and hunting mechanisms of grey wolves and its ability to quickly converge to near-optimal solutions make it a popular choice. This unique feature enables the algorithm to explore the solution space more efficiently by repositioning solutions around each other, thereby facilitating better exploitation of the solution space. The effectiveness of the proposed GWO algorithm is evaluated using fault data generated from various test systems ranging from small-scale 8-bus networks to large 15-bus systems. The results demonstrate several key advantages, reduced operating time, robust coordination, and reduced coordination interval. Compared to other optimization algorithms, the GWO algorithm achieves a reduced coordination interval between primary and backup relay pairs. This optimization contributes to faster and more precise fault detection and isolation within the network in comparison to other techniques. Overall, the findings highlight the superior performance and robustness of the GWO algorithm in addressing the optimal coordination challenges of DOCRs in modern power distribution networks thereby enhancing the efficiency and reliability of protection systems in complex electrical grids.