改进粒子群算法在光伏多峰值MPPT中的应用

Application of Improved Particle Swarm Optimization in Photovoltaic Multi-peak MPPT

光伏阵列在局部遮阴的情况下会呈现多峰值的特性,传统的最大功率点跟踪(MPPT)算法会陷入局部最优点,从而导致算法实效。粒子群算法较其他智能算法具有参数少、控制简单的优点,但存在收敛速度慢以及容易早熟收敛的缺点。针对这些问题,提出了改进的粒子群算法,将自适应的惯性权重法与异步变化的学习因子相结合来改善存在的问题。通过MATLAB将传统粒子群算法与改进的粒子群算法仿真对比来验证改进后算法的优越性。

Photovoltaic arrays will exhibit multi-peak characteristics under partial shading.The traditional maximum power point tracking(MPPT) algorithm will fall into the local optimum,which will cause the algorithm become invalid.Compared with other intelligent algorithms,the particle swarm optimization(PSO) has the advantages of fewer parameters and simple control,but it has the disadvantages of slow convergence and easy convergence early.In response to these problems,the article proposes an improved PSO which combines the adaptive inertia weight method with asynchronously changing learning factors to improve existing problems.Finally,the traditional particle swarm algorithm and the improved particle swarm algorithm are compared with MATLAB simulation to verify the superiority of the improved particle swarm algorithm.