Application of nature inspired metaheuristic techniques to overcurrent relay coordination

Searching is a major activity in artificial Intelligence (AI). Evolutionary search and metaheuristic algorithms form major part of artificial intelligence that has been used to find optimal solutions to a variety of problems. They have been applied in various problems of electrical power systems like economic load dispatch and relay coordination. In this work their application to relay coordination has been presented. The optimal coordination of directional as well as non-directional overcurrent relays holds major concern for reliable operation of interconnected power system. This paper presents a comparative study of AI algorithms for the determination of optimal settings of Time multiplier settings (TMS) and Plug settings (PS) of directional overcurrent relays. The problem is formulated as a nonlinear programming problem (NLPP) and solved by using metaheuristic methods such as genetic algorithm (GA), Hybrid GA-NLPP, Firefly algorithm (FA) and Cuckoo search algorithms (CSA) with the help of MATLAB software. Results have been shown for two systems. The comparison of results shows the credibility of nature inspired metaheuristic algorithms.