Optimal Coordination of Directional Overcurrent Relays Using Artificial Ecosystem-Based Optimization

Optimal directional overcurrent relays (DOCRs) coordination aims to find the optimal relay settings in order to protect the system, where, the primary relays are operated in the first to clear the faults, then the corresponding backup relays should be operated in case of failing the primary relays. DOCRs coordination problem is a non-convex and high dimensional optimization problem and it should be solved subject to operating constraints. The objective function for optimal coordination of DOCRs aims to minimize total operation time for all primary relays without violation in constraints to maintain reliability and security of the electric power system. This paper proposes the artificial ecosystem-based optimization (AEO) algorithm is for the solution of the DOCRs coordination problem. Simulation studies were carried out in IEEE 3-bus and IEEE 4-bus test systems to evaluate the performance of the proposed algorithm. The simulation results are compared with differential evolution algorithm (DE), opposition based chaotic differential evolution algorithm (OCDE1and OCDE2), and three real coded genetic algorithms (RCGAs) namely: Laplace crossover power mutation (LX-PM), Laplace crossover polynomial mutation (LX-POL), bounded exponential crossover power mutation (BEX-PM). The results clearly showed that the proposed algorithm is a powerful and effective method to solve the DOCRs coordination problem. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.2-s2.0-8510981008