| 摘要: |
| 目的 光伏电池在实际应用中接收的光照可能会被遮挡,光伏阵列会在局部遮阴条件下运行,造成光伏系统
输出功率出现多峰值的情况。 针对传统最大功率点追踪(Maximum Power Point Tracking,MPPT)算法全局追踪能
力弱,以及难以兼顾追踪速度和精度的问题,提出了猎人猎物优化算法(Hunter-Prey Optimization,HPO)与变步长
扰动观察法(Improved Perturbation and Observation,IP&O)的结合算法。 方法 首先利用 HPO 算法,初始化种群之后
得到光伏系统的初始最大功率,并将其定义为整个算法的全局最优值;之后每次更新输出功率都要与最优值比较,
保留功率较大的作为全局最优值;当满足算法切换条件时,认为 HPO 算法已经追踪到最大功率点附近,切换到
IP&O 算法在最大功率点附近扰动,直至输出最优值;当光伏系统接收到光照发生变化时,可以通过重启条件快速
重启 HPO-IP&O 算法。 结果 为验证所提算法的可靠性,在 MATLAB / Simlink 中建立光伏系统仿真模型;在不同的
光照条件下,分别将粒子群算法(Particle Swarm Optimization,PSO)、鲸鱼算法(Whale Optimization Algorithm,WOA)
和所提算法对比;仿真结果表明: HPO-IP&O 算法不会陷入局部极值,同时其追踪精度也优于 WOA 算法,但是与
PSO 算法相差不大;在追踪速度上,HPO-IP&O 算法均快于 WOA 和 PSO 算法,且功率越大,HPO-IP&O 所用时间
越短。 结论 HPO-IP&O 算法解决了传统 MPPT 算法易陷入局部最优值、无法兼顾追踪速度和精度的问题,通过仿
真实验验证了所提算法在不同光照条件下的可行性和可靠性。 |
| 关键词: 最大功率点追踪 猎人猎物优化算法 变步长扰动观察法 光照条件 |
| DOI： |
| 分类号: |
| 基金项目: |
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| Research on Photovoltaic MPPT Control Based on HPO-IP&O Algorithm |
|
OUYANG Mingsan ZHOU Jie
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School of Electrical and Information Engineering Anhui University of Science and Technology Anhui Huainan 232001
China
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| Abstract: |
| Objective In practical applications the sunlight received by photovoltaic cells may be obstructed causing
photovoltaic arrays to operate under partial shading conditions resulting in the occurrence of multiple peaks in the output
power of the photovoltaic system. In response to the weak global tracking ability of traditional maximum power point
tracking MPPT algorithms and the difficulty in balancing tracking speed and accuracy a combined algorithm of hunter prey optimization HPO and improved perturbation and observation IP&O was proposed. Methods Firstly the HPO
algorithm was used to initialize the population and obtain the initial maximum power of the photovoltaic system which was
defined as the global optimum of the entire algorithm. Then each update of the output power was compared with the
optimal value and the larger power was retained as the global optimum. When the algorithm switching conditions were
met the HPO algorithm was considered to have tracked near the maximum power point and the IP&O algorithm was
perturbed near the maximum power point until the optimal value was output. When the illumination received by the
photovoltaic system changed the HPO-IP&O algorithm could be quickly restarted by restarting conditions. Results To
verify the reliability of the proposed algorithm a photovoltaic system simulation model was established in MATLAB /
Simulink. The particle swarm optimization PSO whale optimization algorithm WOA and the proposed algorithm were
compared under different illumination conditions. Simulation results showed that the HPO-IP&O algorithm did not fall into
local extremes and its tracking accuracy was better than that of the WOA algorithm but it was not significantly different
from the PSO algorithm. In terms of tracking speed the HPO-IP&O algorithm was faster than the WOA and PSO
algorithms and the larger the power the shorter the time used by HPO-IP&O. Conclusion The HPO-IP&O algorithm
solves the problems of traditional MPPT algorithms easily falling into local optimum and being unable to balance tracking
speed and accuracy. The feasibility and reliability of the proposed algorithm under different lighting conditions are verified
by simulation experiments. |
| Key words: maximum power point tracking hunter-prey optimization algorithm improved perturbation and observation
illumination condition |
