改进光谱优化器在光伏MPPT中的应用

Application of Improved Light Spectrum Optimizer in Photovoltaic MPPT

光伏电池工作易受到环境的影响,当光伏阵列局部被云或者建筑物遮挡时,其输出的功率电压波形会呈现多峰值状态,传统的最大功率点跟踪(MPPT)方法追踪速度和精度会变差且易陷入局部最优值,为此,提出了改进的光谱优化器(LSO),并将之用于光伏的最大功率点跟踪上。首先,利用Logistic映射改进LSO初始化种群质量,提出了改进的LSO算法;然后将改进的LSO算法应用到光伏MPPT上;最后,在均匀光照、局部遮荫与动态环境中测试改进的LSO在MPPT上的应用效果,并与粒子群算法(PSO)、布谷鸟算法(CS)和LSO做对比。实验和仿真效果表明,引入Logistic映射的光谱优化器的追踪速度和精度较PSO、CS和LSO有所提升且在局部遮荫的情况下不会陷入局部最优。

The operation of photovoltaic cells is easily affected by the environment.When the photovoltaic array is partially obscured by clouds or buildings,its output power and voltage waveform will show a multi peak state.The traditional Maximum Power Point Tracking(MPPT)method will have poor tracking speed and accuracy,and it is easy to fall into local optima.Therefore,an improved Light Spectrum Optimizer(LSO)is proposed,and applied it for maximum power point tracking of photovoltaics.Firstly,logistic mapping was used to improve the initialization population quality of LSO,and an improved LSO algorithm was proposed.Then,the improved LSO algorithm was applied to photovoltaic MPPT.Finally,the application effect of improved LSO on MPPT was tested in uniform lighting,local shading,and dynamic environment,and compared with particle swarm optimization(PSO),cuckoo bird algorithm(CS),and LSO.According to experiments and simulations,it is found that the tracking speed and accuracy of the spectral optimizer with Logistic mapping are improved compared to PSO,CS,and LSO,and it will not fall into local optima under local shading.