Improved MICROPSO Algorithm and Its Application on Reactive Power Optimization

Abstract

In this paper, micro-particle swarm optimizer (MICROPSO) is improved and applied on the reactive power optimization problem. Self-adapted mutation operator is introduced in MICROPSO. For self-adapted mutation operator, mutation ratio is inverse-proportional to the fitness. So particles with worse fitness have higher mutation probability, and the algorithm can evolve. The self-adapted mutation operator keeps diversity of the particles. Simulation results on reactive power optimization of IEEE 30 system show that the solution of improved micro-particle swarm optimizer (IMICROPSO) with 4 particles is even better than standard PSO (SPSO) and MICROPSO with 30 particles. The advantage becomes more obvious with the population size enlarging.

VSG control strategy based on improved hybrid leapfrog algorithm

“…If the total number of iterations reaches the maximum number of iterations TMAX, the program is terminated and the optimal frog individual is output. Otherwise, repeat Step 3 to Step 5 until the maximum number of iterations [6] is met.…”