粒子群优化算法在光伏阵列多峰最大功率点跟踪中的应用

局部阴影情况下,光伏阵列功率-电压(P-U)特性曲线呈现多个极值点,传统的最大功率点跟踪(maximum power point tracking,MPPT)方法会失效。研究了粒子群优化算法(particle swarm optimization,PSO)在光伏阵列(photovoltaic array)多峰MPPT中的应用,该方法根据多峰P-U曲线的特性,提出将粒子初始位置分散定位在可能的峰值点电压处这一新思路,保证了粒子群算法不会陷入局部极值点且不会错过任何极值点。设置了粒子群算法的参数,同时提出有效的迭代终止策略,能够避免系统趋于稳定时的功率振荡。最后通过仿真验证了该算法在有、无阴影情况下均能够快速且准确地跟踪最大功率点,有效地提高了光伏阵列输出效率。

Application of PSO Algorithm in Global MPPT for PV Array

Conventional maximum power point tracking(MPPT) methods are ineffective under partially shaded conditions,because multiple local maximum can be exhibited on the power-voltage characteristic curve.A control algorithm based on particle swarm optimization(PSO) algorithm for solving multiple MPPT was studied.A novel way of disperse locating the initial position of agents to all the possible local maximum voltage points was proposed based on P-U characteristics,which could guarantee all the agents not converge at a local maximum and no maximum be missed.The parameters of PSO algorithm were set,and efficient iteration stop strategy was proposed which could reduce the power oscillation when trending to steady state.Simulation results indicate that the proposed global MPPT algorithm can fast and accurately track the global maximum under uniform solar irradiance and partially shaded conditions,and can improve the array output efficiency.