Genetic Algorithm (GA) [7]-[9], Particle Swarm Algorithm (PSO) [10]-[11] among others are used to solve the issue of optimal coordination of overcurrent relays. The pickup value of an overcurrent relay must be set
between the maximum load current and the minimum fault current experienced by the relay. In high voltage and extra-high voltage networks, these parameters are often not well defined, for a safe selection of a pickup setting. For such cases, the distance relay furnishes excellent protection under all circumstances.The performance of a distance relay near its zone
boundaries is not very predictable because of various types of errors. Consequently, it becomes necessary to use multiple zones of protection to cover the entire line dependably and securely. Zone 1 relay operates instantaneously (no intentional delay – i.e. in about one to two cycles) while a fault in Zone 2 causes the relay to operate with an added delay (generally of the order of 20 to 30 cycles). In this fashion, the entire line is protected even where the zone boundary is not very precisely determined. In addition to these two zones, often a third zone (with an additional time delay about one second) is provided at each end in order to provide remote backup for the protection of the adjacent circuits. It should be noted that often, due to system load, it is not possible to obtain a secure Zone 3 setting on high voltage networks. The differential evolution (DE) algorithm, proposed by
Storn and Price [13], is a simple population-based stochastic search technique. DE has been successfully applied in diverse fields such as power systems, mechanical engineering, communication and pattern recognition [14]. In DE, there exist many trials vector generation strategies out of which a few may be suitable for solving a particular problem. Moreover, three crucial control parameters involved in DE, i.e., population size, scaling factor, and crossover rate, may significantly influence the optimization performance of the DE. This article proposes an intelligent relay coordination
method based on combination of differential evolution and genetic algorithms (DE-GA). In reference [15], the optimal coordination of overcurrent and distance relays have been performed with respect to the critical points. In this paper, the distance relay is considered as the main relay and the overcurrent relay is as the backup relay. Results show the proposed method has significantly reduced the execution time of the algorithm while improving the accuracy of the output results in comparison with the other nature-inspired algorithms such as PSO and GA those previously have been applied to the problem and demonstrate the ability of DE-GA to solve non-linear optimization problems.