基于改进鹦鹉算法的光伏多峰值MPPT控制
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1.三峡大学电气与新能源学院 宜昌 443002; 2.新能源微电网湖北省协同创新中心 宜昌 443002

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TN911.34

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国家自然科学基金面上项目(52377191)资助


PV multi-peak MPPT control based on improved parrot algorithm
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1.College of Electrical and New Energy, Three Gorges University,Yichang 443002, China; 2.Hubei Provincial Collaborative Innovation Center for New Energy Microgrid,Yichang 443002, China

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    摘要:

    针对光伏阵列在光照不均条件下呈现多峰突变,导致传统的最大功率点追踪失衡问题,提出了一种基于改进鹦鹉算法的MPPT控制。首先考量Halton序列进行种群初始化,使多样性变化显著;其次选取切线搜索算法中的切线飞行机制减少动态跳跃,克服早熟和局部极点问题;后期通过鹦鹉翻筋斗觅食策略进行二次更新,减小自适应范围,加速收敛。对比传统鹦鹉算法、金豺算法以及灰狼优化算法,测试结果显示,改进的鹦鹉算法的MPPT控制跟踪效率分别为98.23%、97.26%、96.91%和96.81%,跟踪时间分别为0.077 s、0.112 s、0.127 s、0.156 s,最后通过实验验证跟踪精度和速率均显著高于其余3种算法。

    Abstract:

    Aiming at the problem that PV arrays show multipeak mutation under uneven light conditions, which leads to the imbalance of the traditional maximum power point tracking, an MPPT control based on the Improved Parrot Algorithm is proposed. Firstly, the Halton sequence is considered for population initialization to make the diversity change significant; secondly, the tangent flight mechanism in the Tangent Search Algorithm is selected to reduce the dynamic jumps and overcome the problems of precocity and local polarity; and later, the secondary update is performed by the parrot somersaulting foraging strategy to reduce the adaptive range and accelerate the convergence. Comparing the traditional parrot algorithm, the golden jackal algorithm and the gray wolf optimization algorithm, the test results show that the improved parrot algorithm′s MPPT control tracking efficiency is 98.23%, 97.26%, 96.91% and 96.81%, and the tracking time is 0.077 s, 0.112 s, 0.127 s, and 0.156 s, respectively, and the tracking accuracy and rate are significantly higher than the remaining three algorithms.

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魏业文,邱天一.基于改进鹦鹉算法的光伏多峰值MPPT控制[J].电子测量技术,2024,47(12):83-90

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  • 在线发布日期: 2024-11-04
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