Optimized controller design for islanded microgrid using non-dominated sorting whale optimization algorithm (NSWOA)

Swarm-based nature-inspired optimization techniques are developing rapidly due to their universal acceptance and capability in solving various real-life challenges efficiently. Hybridization of swarm intelligence based optimization algorithms with multi-objective based solution techniques is creating a wide door in the field of optimization. The main focus of this work is, developing a hybrid Non-dominated Sorting Whale Optimization Algorithm (NSWOA), where swarm-based Whale Optimization Algorithm (WOA) is hybridized with multi-objective based, non-dominated sorting technique. This is done to develop an algorithm with efficient optima searching ability and faster computational speed. The application of NSWOA in optimizing the controller parameters of an islanded microgrid consisting of both static and dynamic load has been also described. SPSS software has been used to compare the performance of proposed NSWOA with non-dominated sorting genetic algorithm-II (NSGA-II) and Strength Pareto Evolutionary Algorithm (SPEA) technique in optimizing the controller parameters of islanded microgrid model with multi-objective problem. It is obtained that, NSWOA requires an average of 4 iterations to reach the best optimum solution, which is less than other existing algorithms. Moreover, the computational time required by NSWOA is 2.9201 s, which proves that, it converges at a much faster rate compared to existing NSGA-II and SPEA algorithms