基于改进蜻蜓算法的光伏全局最大功率追踪

doi:10.11930/j.issn.1004-9649.202010137

中国电力 ›› 2022, Vol. 55 ›› Issue (2): 131-137.DOI: 10.11930/j.issn.1004-9649.202010137

基于改进蜻蜓算法的光伏全局最大功率追踪

薛飞^1,2, 马鑫^1,2, 田蓓^1,2, 吴慧¹

1. 国网宁夏电力有限公司电力科学研究院, 宁夏银川 750002;
2. 宁夏电力能源安全重点实验室, 宁夏银川 750002

收稿日期:2020-10-30 修回日期:2021-11-17 出版日期:2022-02-28 发布日期:2022-02-23
作者简介:薛飞（1994—），男，硕士，工程师，从事主动配电网规划与运行优化研究，E-mail：tjuxf1010@126.com;马鑫（1994—），男，通信作者，硕士，助理工程师，从事配电网运行优化及状态估计研究，E-mail：309480591@qq.com;田蓓（1977—），女，高级工程师（教授级），从事电力系统自动化研究，E-mail：babyqi0203@163.com
基金资助:
国家重点研发计划资助项目（面向高品质用能需求的清洁能源和储能混合系统关键技术研究，2017YFE0132100）。

Photovoltaic Global Maximum Power Tracking Based on Improved Dragonfly Algorithm

XUE Fei^1,2, MA Xin^1,2, TIAN Bei^1,2, WU Hui¹

1. Electric Power Research Institute, State Grid Ningxia Electric Power Co., Ltd., Yinchuan 750002, China;
2. Ningxia Key Laboratory of Electrical Energy Security, Yinchuan 750002, China

Received:2020-10-30 Revised:2021-11-17 Online:2022-02-28 Published:2022-02-23
Supported by:
This work is supported by National Key Research and Development Program of China (Research on Key Technologies of Clean Energy and Energy Storage Hybrid System for High Quality Energy Demand, No.2017 YFE0132100).

摘要/Abstract

摘要： 光伏阵列的P-U特性曲线在局部遮蔽条件下呈现多峰现象，针对传统最大功率点跟踪方法易陷入局部极值、群智能算法跟随速度慢的问题，提出一种基于蜻蜓算法和扰动观察法的改进最大功率点跟踪算法。该算法通过优化算法角色，引入Lévy飞行模式加快算法的收敛速度并提高全局搜索能力；结合扰动观察法，提出种群密度的概念，制定最优局部搜索策略提高种群搜索效率与搜索精度。最后，通过仿真对比扰动观察法、粒子群算法和原始蜻蜓算法的追踪结果，验证了所提算法的有效性。

关键词: 光伏发电系统, 最大功率点跟踪, 多峰特性, 蜻蜓算法, 扰动观察法

Abstract: A multi-peak phenomenon can be observed on the power-voltage (P-U) characteristic curve of a photovoltaic (PV) array under partially shaded conditions (PSCs). In this case, conventional maximum power point tracking (MPPT) algorithms tend to fall into local extremums, and swarm intelligence algorithms would spend much time in tracking. Thus, this paper proposes an improved MPPT algorithm based on the dragonfly algorithm (DA) and the perturbation and observation (P & O) algorithm. The convergence rate and global search ability of the algorithm are improved by optimizing particle roles and introducing the Lévy flight model. With the P & O algorithm, the concept of population density is put forward and an optimal local search strategy is formulated to modify the population search efficiency and precision. Finally, comparisons with the P & O algorithm, particle swarm optimization (PSO) algorithm, and the original DA through simulation verify the validity of the proposed algorithm.

Key words: photovoltaic system, maximum power point tracking, multi-peak characteristic, dragonfly algorithm, perturbation and observation algorithm

薛飞, 马鑫, 田蓓, 吴慧. 基于改进蜻蜓算法的光伏全局最大功率追踪[J]. 中国电力, 2022, 55(2): 131-137.

XUE Fei, MA Xin, TIAN Bei, WU Hui. Photovoltaic Global Maximum Power Tracking Based on Improved Dragonfly Algorithm[J]. Electric Power, 2022, 55(2): 131-137.

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https://www.electricpower.com.cn/CN/Y2022/V55/I2/131

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基于改进蜻蜓算法的光伏全局最大功率追踪

Photovoltaic Global Maximum Power Tracking Based on Improved Dragonfly Algorithm

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基于改进蜻蜓算法的光伏全局最大功率追踪

Photovoltaic Global Maximum Power Tracking Based on Improved Dragonfly Algorithm

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