Optimal Temperature-dependent Power Flow using Quantum-behaved Particle Swarm Optimization

The proposal of this paper is the optimization of a power system through the application of Quantum-behaved Particle Swarm Optimization (QPSO) algorithm. The aim is to reduce the system losses and estimate the temperature of the branch conductors, since taking temperature into account will result in losses increasing when compared with the Newton-Raphson method. To accomplish this, the particles were the nodal voltages of the system, in which it differs from other applications of the PSO algorithms in power systems. The proposal was applied to the IEEE-30 and IEEE-118 bus systems. In these systems, distributed generation was added to verify the algorithm performance.