基于改进SCHO算法的局部遮荫光伏MPPT研究

Research on photovoltaic MPPT under partial shading base on improved SCHO algorithm

针对局部遮荫工况下光伏阵列最大功率点跟踪(MPPT)存在的易陷入局部功率峰值点、跟踪时间长、跟踪精度低等问题,提出一种基于改进双曲正余弦优化(SCHO)的控制算法.其采用立方混沌映射初始化,提高初始候选解集的遍历性,并利用贝塔分布的概率特性修正SCHO算法的切换标准,提高算法与候选解集寻优进程的适配性.同时,对SCHO算法中的全局最优解、个体当前解引入平衡权重更新策略,且采用透镜成像反向学习策略对寻优后期的候选解进行扰动,采用比例收缩法对寻优空间进行动态钳位,提高算法的全局勘探及局部开发能力.Matlab仿真结果表明,相比其他控制算法,本文提出的改进SCHO算法能缩短MPPT时间、提高MPPT精度,故具有更优的MPPT性能,可为进一步提升光伏发电效率提供参考.

A control algorithm based on improved sinh-cosh optimization(SCHO)is proposed to address the issues of maximum power point tracking(MPPT)of photovoltaic arrays under partial shading conditions,such as being prone to getting stuck in local power peak,long tracking time,and low tracking accuracy,which initializes the candidate solutions by Cubic Chaos map to improving the traversibility,modify the switching criteria of the SCHO algorithm by using the probability characteristics of the Beta distribution to improve the adaptability of the algorithm to the optimization process of candidate solutions.Meanwhile,a balanced-weight update strategy is introduced for the global optimal solution and individual current solution in the SCHO algorithm,and a lens imaging reverse learning strategy is adopted to perturb the candidate solutions in the later stage of optimization,and the proportional contraction method is used to clamp the optimization space dynamically,which improves the global exploration and local exploitation capabilities of the algorithm.The Matlab simulation results show that the improved SCHO algorithm proposed in this paper can shorten MPPT time and improve MPPT accuracy comparing with other control algorithms,so it has better MPPT performance,which provides reference for further improving the efficiency of photovoltaic power generation.