Comparative Investigation of the Effect of Different IEC Characteristics on PSO and GA Convergence in Optimal Coordination of Directional Overcurrent Relays

Reliable coordination of Directional Overcurrent Relays (DOCRS) is crucial for ensuring selectivity, speed, and stability in power systems. However, the coordination problem is nonlinear, highly constrained, and sensitive to the inverse-time characteristics defined by the IEC standard. This paper presents a comparative investigation of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) methods for the optimal coordination of DOCRS using three IEC inverse-time characteristics: Standard Inverse (SI), Very Inverse (VI), and Extremely Inverse (EI). Both algorithms are implemented with identical objective functions and constraint-handling mechanisms provide a fair comparison. The IEEE ۶-bus system is used as the test network, and convergence behaviors, final objective function values, and sensitivity to algorithmic parameters are analyzed. Simulation results show that GA consistently achieves lower objective function values compared to PSO across all IEC characteristics. The results further indicate that while the choice of optimization parameters has a minor influence on the final solution, the selected IEC curve significantly affects algorithmic performance and convergence trends. These findings highlight the importance of matching the optimization technique and its parameters to the protection curve characteristics in relay coordination applications.

کلیدواژه ها:

Directional Overcurrent Relay (DOCR) ، Particle Swarm Optimization (PSO) ، Genetic Algorithm (GA) ، IEC Inverse-Time Characteristics ، Relay Coordination ، Optimization Convergence ، Sensitivity Analysis ، IEEE ۶-Bus System