Reactive power optimization in power system based on improved niche genetic algorithm

Abstract

Reactive power optimization in power system can not only reduce power loss, but also improve voltage quality. It is a mixed optimizing question, which its operating variables include the continual and the separate, its solved process is quite complex. The genetic algorithm (GA) is used to solve this problem, but the GA easy to fall into local optimum problem with the increasing of system size, the key to improve the performance of the global convergence of GA is believed to apply the algorithms to deal with the diversity of population scientifically and better tactic of searching the global optimal solution. So the genetic simulated annealing algorithm and niche technique are integrated to search for the best solution of reactive power optimization problem. The simulation results of the standard IEEE-6 bus power system had indicated that the new improved niche genetic algorithm (INGA) can not only efficiently avoid possible non-convergence of GA and improve evolution speed and has better global optimization ability, but also improve the computing speed and stability of the algorithm. It was proved to be efficient and practical during the reactive power optimization.