Reactive power optimization based on cloud adaptive gradient particle swarm optimization

Power system reactive power optimisation is regarded as a typical high-dimesional, nonlinear and discontinuous problem. Swarm optimization (PSO) algorithm converges rapidly and is easy to implement, how ever it has the defect of prematurity during the optimisation process and it makes the PSO easy to fall into the local minimum. To cope with this defect, firstly the cloud model is led into PSO, and the particles are divided into two parts, i.e., the part adjacent to the optimal particle and the part distant from the optimal particle, in which the inertia weight of the population adjacent to the optimal particle is adaptatively adjusted by the X-condition generator of cloud model; then the idea of gradient is led in and an algorithm named as cloud adaptive gradient particle swarm optimization, CAGPSO) algorithm is proposed. Taking the minimum network loss as objective function, simulation for the proposed CAGPSO algorithm by standard IEEE 14-bus system and IEEE 30-bus system are performed, simulation results show that a better optimal solution can be attained by the proposed CAGPSO algorithm.