回归算法与粒子群算法融合算法的光伏系统多峰值MPPT研究

Research on PV system multi⁃peak MPPT based on fusion algorithm of regression algorithm and particle swarm optimization algorithm

局部遮荫下光伏阵列输出功率呈现多峰值,传统最大功率点跟踪算法常失效。为了提高发电的效率和稳定性,提出一种回归算法与粒子群算法融合的最大功率跟踪方法,该方法利用线性回归的预测性、泛化能力强的特点,改进粒子群算法初始化中的粒子随机性、易陷入局部极值的问题,并应用于局部遮荫下光伏阵列(单、多晶硅)最大功率点跟踪。通过实验与理论研究,首先发现随着训练集的增加,该方法跟踪性能呈现先上升后平缓趋势下降,且训练集比例选取总数据的55%~75%之间,跟踪性能最佳;其次,发现单(多)晶硅电池最佳跟踪精度分别为99.13%(99.16%),对比遮荫对电池最大功率影响,说明该方法具有普适性;最后,与粒子群算法和遗传算法相比,跟踪精度都提高了,且方差为零,表明该算法的精度高和鲁棒性强。结果表明,该方法具有普适性、精度高和鲁棒性强等优点。

Under partial shading,the output power of PV array presents multiple peaks,so the traditional maximum power point tracking(MPPT)algorithm often fails.Therefore,an MPPT method fusing regression algorithm and particle swarm optimization(PSO)algorithm is proposed to improve the efficiency and stability of power generation.In the proposed method,the predictability and powerful generalization ability of linear regression is used to improve the particle randomness in the PSO initialization and eliminate the fact that the PSO algorithm is prone to falling into local extreme value.And then,it is applied to the MPPT of PV array(monocrystal silicon and polycrystalline silicon)under partial shading.By experimental and theoretical research,it is found firstly that,with the increase of training sets,the tracking performance of this method increases at first and then decreases gently,and the tracking performance is the best when the proportion of training sets is within 55%~75%of the total data;secondly,the optimal tracking accuracy of monocrystal silicon(polycrystalline silicon)PV cell is 99.13%(99.16%)respectively,which indicates that this method is suitable for MPPT of different types of solar PV cells as compared with the influence of the degree of shading on both cells′maximum power;finally,the tracking accuracy of the proposed method is improved in comparison with those of PSO algorithm and genetic algorithm,and its variance is zero,which shows that the method has high accuracy and strong robustness.To sum up,the proposed method has advantages of universality,high precision and strong robustness.