In an electrical power network linear and non-linear models are used for directional overcurrent relay (DOCR) coordination issue by applying different heuristic techniques. Nature inspired algorithms (NIA) have found great interest in power system optimization issues. This article proposes the recently developed meta-heuristic technique known as Firefly Algorithm (FA) that mimics the flashing behavior of fireflies. The implementation of the proposed algorithm has been utilized to solve the coordination of Directional Over-current Relay (DOCR) problems. The main aim of this work is to find out the optimum values of the Time Dial Setting (TDS) to minimize the relay operating time. The modifications to original FA has been implemented in this paper to solve the DOCR coordination issues. Self-adaptive weight and experience-based learning strategy are added in the original FA, named as improved firefly algorithm (IFA). In IFA, a self-adaptive weight is presented to change the propensity of moving the best solution and ignoring the worst solution. In addition, an experience-based learning system is created and utilized arbitrarily to keep up the populace-assorted variety and improve the exploration capacity. The IFA has been tested on IEEE 6 and 30-bus systems and tested on IEEE 9-bus system for numerical DOCRs and the results had been verified by a comparative study with other optimization techniques. The obtained results show that the IFA provides efficient and promising results compared to other meta-heuristic techniques mentioned in the literature. The IFA has been successfully implemented on MATLAB software programming.

The directional overcurrent relays (DOCRs) coordination is a useful tool in guaranteeing the safe protection of the power system by the proper coordination of primary and backup protection systems. The optimization model of this problem is non-linear and highly constrained. The main objective of this paper is to develop a hybridized version of the Whale optimization algorithm referred to as HWOA for the optimal coordination of the DOCRs. The hybridization is done by deploying the simulated annealing (SA) in the WOA algorithm in order to improve the best solution found after each iteration and enhance the exploitation by searching the most promising regions located by the WOA algorithm, which leads toward a globally optimum solution. The proposed algorithm has been applied to five test systems, including the IEEE 3-bus, 8-bus, 9-bus, 15-bus, and 30-bus test systems. Furthermore, the results obtained using the proposed HWOA are compared with those obtained using the traditional WOA and a number of up-to-date algorithms. The obtained results show the effectiveness of the proposed HWOA in minimizing the relay operating time for the optimal coordination of the DOCRs.

In electrical engineering problems, bio- and nature-inspired optimization techniques are valuable ways to minimize or maximize an objective function. We use the root tree algorithm (RTO), inspired by the random movement of roots, to search for the global optimum, in order to best solve the problem of overcurrent relays (OCRs). It is a complex and highly linear constrained optimization problem. In this problem, we have one type of design variable, time multiplier settings (TMSs), for each relay in the circuit. The objective function is to minimize the total operating time of all the primary relays to avoid excessive interruptions. In this paper, three case studies have been considered. From the simulation results, it has been observed that the RTO with certain parameter settings operates better compared to the other up-to-date algorithms.