Optimal coordination of directional over current relays using a modified real coded genetic algorithm: A comparative study Manoj Thakur ⇑ , Anand Kumar School of Basic Science, IIT Mandi, India article info Article history: Received 20 June 2015 Received in revised form 16 March 2016 Accepted 20 March 2016 Keywords: Overcurrent relay Power system protection Optimization Real coded genetic algorithm Constraint handling abstract In a large distribution network, the coordination of relays is a highly constrained optimization problem with the objective to minimize the overall operating time of each primary relay. For proper functioning of the power system with distributed power generating stations, appropriate coordination of protection devices is crucial. The present work incorporates bounded exponential crossover and power mutation (BEX–PM) into Real Coded Genetic Algorithm (RCGA) in order to find optimal settings for the Directional Overcurrent Relays (DOCRs). Optimal settings are obtained to minimize the overall action time of all the primary relays as well as to get rid of miscoordination among the backup/primary relay pairs. Another objective of the work is to maintain the difference of response time of backup relay and corresponding primary relay to possible minimum. Results obtained are compared with various approaches available in the literature. The results of this work evidences the compatibility of the pro- posed strategy in solving complex real-world optimization problems and applicability of the obtained results. Ó 2016 Published by Elsevier Ltd. 1. Introduction Several generating stations in modern power systems run paral- lely to feed a high voltage network. These networks connect millions of equipments. The consequences of development of fault in the power system may be devastating. In order to secure the network from such kind of abrupt circumstances, protective relays are infused into the system. These relays disconnect the malfunc- tioning part from the system by tripping the circuit. Directional Overcurrent Relays (DOCRs) are widely used protec- tion devices [1,2] that senses the current flowing in one direction only while current flowing in opposite direction is not noticed by them. For proper functioning of the system, a DOCR should operate for a fault appears in its zone only. The primary relay is supposed to operate on appearance of a fault. Primary relays are backed up by secondary relays, that operate on failure of primary relays. Proper coordination of DOCRs is important to improve perfor- mance of electric power devices and to prevent equipment dam- ages. Coordination of the DOCRs can be mathematically modeled as a constrained optimization problem [3] with parameters Time Dial Setting (TDS) and Plug Setting (PS). For each DOCR, tuning of TDS decides the operating time of the relay while, tuning of PS deci- des the pick up current. Numerous approaches have been proposed to solve optimal relay coordination problem. These approaches can be classified as:  Topological analysis approach.  Classical optimization based approach.  Artificial Intelligence (AI) based approach. Before the involvement of computers the coordination problem were solved manually with the help of huge mathematical compu- tation. These computations are time consuming, error prone and calculated settings are inappropriate practically. Topological anal- ysis approach finds relays that open the maximum number of cir- cuits using some heuristic strategy, considering PS as constant. The TDS values are computed from these relays for all the relays in the network sequentially. The process applied iteratively untill TDS values for all the relays are found. In classical optimization tech- niques the coordination problem is formulated as a linear opti- mization problem by considering PS constant. On the other hand, AI based approach employs metaheuristic algorithms which begins with a population of randomly generated solutions and every iter- ation improves the quality of the solution and finally whole popu- lation converges at an optimal solution. http://dx.doi.org/10.1016/j.ijepes.2016.03.036 0142-0615/Ó 2016 Published by Elsevier Ltd. ⇑ Corresponding author. E-mail addresses: manojpma@gmail.com (M. Thakur), anand.iitmandi@gmail. com (A. Kumar). Electrical Power and Energy Systems 82 (2016) 484–495 Contents lists available at ScienceDirect Electrical Power and Energy Systems journal homepage: www.elsevier.com/locate/ijepes