改进粒子群优化算法在光伏多峰MPPT中的应用

Application of Particle Swarm Optimization Algorithm to Photovoltaic Multi-Peak MPPT

针对在局部阴影条件下,既实现光伏阵列最大功率点跟踪,又实现全局寻优,把模拟退火算法的Metropolis准则引入到粒子群算法中,通过在一定程度上接受恶化解,增强了粒子的多样性,从而使粒子群算法具有更好的全局寻优能力。在控制电路中,采用电压外环控制,保证粒子稳定前提下,收敛在最大功率点处。最后,将所提粒子群算法、传统MPPT算法通过仿真进行对比,结果表明在局部阴影条件下基于模拟退火粒子群算法能够准确、快速地跟踪到最大功率点并稳定地工作在最大功率点处,从而提高了光伏阵列转换效率。

On the condition of partial shade,the PV curve of PV array will show multiple local peaks.Traditional MPPT algorithm can only track a single power peak output power and the algorithm could not be completed effectively in multi-peak MPPT.Particle swarm optimization has good multi-pole optimization capability,but it is easy to fall into local optimal.In order to deal with this problem,Metropolis rule was introduced in particle swarm algorithm.It can increase the diversity of the particle swarm by accepting the degradation solution to some extent.The particle swarm algorithm can get out of the local optimal solution and realize global optimization.In the control circuit,the voltage outer ring control is applied to ensure the stability of the particle and it can converge to the maximum power point.Ultimately,MATLAB simulation of the algorithm shows that the algorithm in partial shading can more accurately and quickly track the maximum power point and effectively improve the output efficiency of PV array.