Non-dominated Sorting Genetic Algorithm III for Multi-objective Optimal Reactive Power Dispatch Problem in Electrical Power System
The non-dominated sorting genetic algorithm (NSGA-III) was introduced to solve multi-objective optimal reactive power dispatch (MORPD) problems. MORPD as a non-linear, multi-objective optimization problem has the characteristics of non-convex, multi-constraint, and multi-variable (mix of discrete and continuous variables). The aim is to minimize the real power losses and voltage deviations. The feasibility of the proposed method was tested on the IEEE 57-bus power systems. The comparison of simulation results with the previous studies which applied the mixed variables of continuous and discrete showed that the proposed optimization method is more efficient and reliable in minimize the real power losses and computing period compared to multi-objective enhanced particle swarm optimization (MOEPSO), multi-objective particle swarm optimization (MOPSO) and multi-objective ant lion optimization (MOALO).
Paper
Full text
Non-dominated Sorting Genetic Algorithm III for Multi-objective Optimal Reactive Power Dispatch Problem in Electrical Power System
Semantic Scholar · Engineering · 2019
Abstract
The non-dominated sorting genetic algorithm (NSGA-III) was introduced to solve multi-objective optimal reactive power dispatch (MORPD) problems. MORPD as a non-linear, multi-objective optimization problem has the characteristics of non-convex, multi-constraint, and multi-variable (mix of discrete and continuous variables). The aim is to minimize the real power losses and voltage deviations. The feasibility of the proposed method was tested on the IEEE 57-bus power systems. The comparison of simulation results with the previous studies which applied the mixed variables of continuous and discrete showed that the proposed optimization method is more efficient and reliable in minimize the real power losses and computing period compared to multi-objective enhanced particle swarm optimization (MOEPSO), multi-objective particle swarm optimization (MOPSO) and multi-objective ant lion optimization (MOALO).