Achieving optimum coordination of directional overcurrent relays is a very complicated task in the large interconnected system including huge numbers of relays. In the literature, various objective functions (OFs) are reported to obtain minimum operating time of relays and minimum prescribed discrimination time between the operations of primary and backup (P/B) relays. In this paper, the performance of five well‐established OFs is examined, and in addition, a new OF is proposed to improve the previously proposed OFs. The proposed OF (POF) minimises operating time of P/B relays together with discrimination time between their operations. Also, the coordination problem is formulated considering near and far‐end faults and solved by using a genetic algorithm. In order to evaluate the effectiveness, the POF is implemented over the IEEE 14‐bus and IEEE 30‐bus distribution networks, and obtained results are compared with those obtained by using previously proposed OFs presented in the literature.

The precise coordination of Directional Overcurrent Relays (DOCRs) is required to identify fault timely, effectively and isolate them from the network to avoid possible outages in a power system. The DOCRs coordination is an optimization problem including highly nonlinear constraints. In this paper, Cuckoo Search Algorithm (CSA) is implemented to solve coordination problem of DOCRs on two different case studies. The parameters of CSA are effectively tuned to obtain global best solution for the DOCRs coordination problem. The obtained results using the proposed method are compared with Genetic Algorithm (GA) and hybrid GA-Nonlinear programming (GA-NLP) methods. The result shows that the effective modification of CSA parameters can obtain feasible and superior solution for this complex problem.

This paper introduces a new protection system for solar photovoltaic generator (SPVG)-connected networks. The system is a combination of voltage-restrained overcurrent relays (VROCRs) and directional overcurrent relays (DOCRs). The DOCRs are implemented to sense high fault current on the grid side, and VROCRs are deployed to sense low fault current supplied by the SPVG. Furthermore, a novel challenge for the optimal coordination of DOCRs-DOCRs and DOCRs-VROCRs is formulated. Due to the inclusion of additional constraints of VROCR, the relay coordination problem becomes more complicated. To solve this complex problem, a hybrid Harmony Search Algorithm-Bollinger Bands (HSA-BB) method is proposed. Also, the lower and upper bands in BB are dynamically adjusted with the generation number to assist the HSA in the exploration and exploitation stages. The proposed method is implemented on three different SPVG-connected networks. To exhibit the effectiveness of the proposed method, the obtained results are compared with the genetic algorithm (GA), particle swarm optimization (PSO), cuckoo search algorithm (CSA), HSA and hybrid GA-nonlinear programming (GA-NLP) method. Also, the superiority of the proposed method is evaluated using descriptive and nonparametric statistical tests.

The optimal coordination of Directional Overcurrent Relays (DOCRs) is a nonlinear and non-convex optimization problem integrating large number of constraints. In this paper, the hybrid Cuckoo Search Algorithm (CSA) - Firefly Algorithm (FFA) approach is implemented to solve coordination problem of DOCRs. The Artificial Intelligence (AI) based method such as FFA searches large solution space with large deviation in results with different number of simulations. Also it is not assured that result given by FFA is global best. To solve this problem, the preliminary optimal value of Time Multiplier Setting (TMS) and pickup current (I_p) are determined using CSA. The values of these variables are used in FFA as upper bounds which reduce the solution space and give a global optimal solution with very minimum deviation. The obtained results using proposed method are compared with hybrid Genetic Algorithm-Nonlinear Programming (GA-NLP) as well as with conventional CSA and FFA methods. The outcome demonstrates that the proposed method can obtain realizable and global best solution with minimum deviation in results and improved computational efficiency for this complex problem.