Adaptive particle swarm for reactive power optimization in power systems

The reactive power optimization in power systems is quite difficult but very important. Recent research results have shown that evolutionary computation has advantages in finding the global optimum and dealing with discrete variables. The particle swarm optimization (PSO) algorithm, a new evolutionary computation method, has been proved to be powerful but needs parameters predefined for a given problem. In this paper, an adaptive particle swarm optimization (APSO) algorithm is proposed and applied to reactive power optimization in power systems. The proposed APSO method can adjust parameters automatically in optimization process. The performance of the APSO is demonstrated by solving the reactive power optimization problems in the IEEE 30-bus power system and a practical 125-bus power system. The simulation results show that the APSO algorithm is more efficient in searching global optimization solution compared with PSO algorithm, conventional gradient method and the standard genetic algorithm.