The application of improved particle swarm optimization algorithm involtage stability constrained optimal power flow

In order to solve the problem of minimizing cost of power generation calculation in voltage stability constrained optimal power flow optimal of power system, dynamic double-population particle swarm optimization algorithm is used on the basis of the traditional particle swarm optimization algorithm, In this algorithm the particles not only depends on successful experience to move but also get experience from failure cases. And the particles are constantly changing in the process of iteration, which overcomes the local convergence of traditional PSO. The dynamic double-population particle swarm optimization algorithm is applied to the voltage stability constrained optimal power flow calculation to minimizing the generation cost problem, which was tested in a standard IEEE30 system, in order to prove the effectiveness of dynamic double-population particle swarm optimization algorithm, it is compared with genetic algorithm (GA) and results show that, dynamic double-population particle swarm optimization algorithm is better than genetic algorithm in computing power cost minimization problem.