基于云自适应梯度粒子群算法的无功优化

Reactive Power Optimization Based on Cloud Adaptive Gradient Particle Swarm Optimization

粒子群算法存在着早熟的现象,易陷入局部最小点,为了克服这个缺点,文章首先将云模型引入粒子群算法,将粒子分成2部分,靠近最优粒子和远离最优粒子的部分,其中靠近最优粒子种群的惯性权重由云模型的X-条件发生器自适应调整,提出了云自适应粒子群算法(cloud adaptiveparticle swarm optimization,CAPSO),然后引入梯度的思想,提出云自适应梯度粒子群算法(cloud adaptive gradientparticle swarm optimization,CAGPSO)。以网损最小为目标函数,对标准IEEE 14和IEEE 30节点系统进行仿真计算,结果表明改进后的CAGPSO算法能够获得更好的优化解。

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.