A Novel Approach for Optimum Coordination of Directional Overcurrent Relays Including Far-End Faults in Interconnected Power Systems

Abstract

Abstract- I. INTRODUCTIONThe primary protection in distribution and sub-transmission networks and backup protection in transmission networks are usually provided using DOCRs [1]. For reducing the chances of excessive power outages, only faulted section of the power system is isolated using primary relays as quickly as possible. As long as the primary relay fails to operate, the backup relays have to instigate to clear the fault after the prescribed time interval. This practice is called relay coordination. The proper coordination of primary and backup relays is essential to ensure the reliability of protection scheme, which is achieved by locating the optimal values of PS and TMS. In modern multi-loop and multi-source interconnected power systems, finding the optimal PS and TMS using analytical methods becomes very hard. Alternatively, it can be easily solved by optimization techniques [2].In the last few years, several optimization techniques are employed to solve the relay coordination problem. Among the conventional methods, Linear Programming (LP) technique gained good recognition to solve this problem, including simplex, two-phase simplex and dual simplex methods [3][4][5]. The LP methods involve assumptions in PS, allowing for operating time of each relay as a linear function of TMS. In [6,7], Sequential Quadratic Programming (SQP) has been used in order to optimize both TMS and PS. Afterwards, Artificial Intelligence (AI) techniques are studied more to solve the coordination problem such as GA The HSA is one of the metaheuristic optimization methods which is developed by Z.W. Geem [22]. It has the characteristics of fast convergence speed, easy in concept and simple in implementation with only a few parameters and mathematical requirements [22,23]. Same as other metaheuristic methods, the performance of the HSA experiences a serious problem of sensitive parameters setting. Hence, fine tuning of the parameters are required, which can help the HSA to maintain a balance between diversification and intensification and to explore the population in the evolution process [24]. To improve the performance of HSA, IHSA is presented in

An info‐gap risk‐averse optimization model for coordination of overcurrent protective relays considering power system uncertainty

“…Heretofore, different mathematical techniques and evolutionary algorithms are proposed in the literature to solve the coordination problem of the overcurrent relays. Among them are referred the genetic algorithm, 3,4 hybrid genetic algorithm, 5 particle swarm optimization, 6 modified particle swarm optimization, 7,8 differential evolution algorithm, 9 artificial bees colony algorithm, 10 water cycle algorithm, 11 seeker algorithm, 2 modified seeker algorithm, 12 improved firefly algorithm, 13 improved harmony search algorithm, 14 electromagnetic field optimization, modified electromagnetic field optimization, 15 hybrid biogeography‐based optimization with linear programming (LP), 16 modified adaptive teaching learning‐based optimization algorithm, 17 modified particle swarm optimization‐interval LP approach, 18 non‐dominated sorting genetic algorithm‐II, 19 and combination of cuckoo search and LP algorithms 20 . However, evolutionary algorithms suffer from the drawback in procuring an optimistic convergence of solutions due to highly variables and constraints 17 .…”