基于自适应秃鹰搜索算法的光伏最大功率点跟踪

翟梓辰; 缪书唯

doi:10.19725/j.cnki.1007-2322.2023.0252

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基于自适应秃鹰搜索算法的光伏最大功率点跟踪

新能源电力系统 | 更新时间：2025-10-22

- 基于自适应秃鹰搜索算法的光伏最大功率点跟踪
- Maximum Power Point Tracking for Photovoltaic Systems Based on Adaptive Bald Eagle Search Algorithm
- 现代电力 2025年42卷第5期页码：919-927
- 作者机构：
  
  三峡大学电气与新能源学院,湖北省,宜昌市,443002
- 作者简介：
  
  [ "翟梓辰(1998)，男，硕士研究生，研究方向为光伏系统最大功率点跟踪技术和光伏并网控制，E-mail：15544427959@163.com" ]
  [ "缪书唯(1989)，男，博士，副教授，硕士生导师，通信作者，研究方向为风电场风况建模、风电场并网系统可靠性评估和优化，E-mail：jabker@163.com" ]
- 基金信息：
- DOI：10.19725/j.cnki.1007-2322.2023.0252
  中图分类号： 5
- 纸质出版：2025
- 稿件说明：
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翟梓辰, 缪书唯. 基于自适应秃鹰搜索算法的光伏最大功率点跟踪[J]. 现代电力, 2025,42(5):919-927.

ZHAI Zichen, MIAO Shuwei. Maximum Power Point Tracking for Photovoltaic Systems Based on Adaptive Bald Eagle Search Algorithm[J]. 2025, 42(5): 919-927.
翟梓辰, 缪书唯. 基于自适应秃鹰搜索算法的光伏最大功率点跟踪[J]. 现代电力, 2025,42(5):919-927. DOI： 10.19725/j.cnki.1007-2322.2023.0252.

ZHAI Zichen, MIAO Shuwei. Maximum Power Point Tracking for Photovoltaic Systems Based on Adaptive Bald Eagle Search Algorithm[J]. 2025, 42(5): 919-927. DOI： 10.19725/j.cnki.1007-2322.2023.0252.

摘要

局部遮阴条件下，光伏系统的P-V特性曲线呈多峰现象，导致传统最大功率点跟踪算法易陷入局部最大功率点，致使其跟踪效率降低。为此，提出自适应秃鹰搜索算法，该算法在传统秃鹰搜索算法的基础上引入高斯混合自适应游走策略、渐进式俯冲自适应切换策略以及秃鹰群规模调整机制，增强该算法的全局搜索与局部寻优能力，提高其收敛精度与速度。在Simulink仿真平台下，应用该算法对某光伏发电系统进行最大功率点跟踪，并与传统秃鹰搜索算法、粒子群优化算法和差分进化–灰狼优化算法进行对比。结果表明，在4组典型场景下文中所提算法较现有3类算法跟踪速度更快、精度更高，且跟踪过程中功率波动更小，可较好地提高光伏系统发电量。

Abstract

Under partial shading conditions

the P-V characteristic curve of the photovoltaic system shows multi-peak characteristics

which makes the traditional maximum power point tracking algorithm easy to fall into the local maximum power point

thus reducing its tracking efficiency. Therefore

this paper proposes an adaptive bald eagle search algorithm. Based on the traditional bald eagle search algorithm

the algorithm introduces Gaussian mixture adaptive walking strategy

progressive diving adaptive switching strategy and bald eagle swarm size adjustment mechanism to enhance the global search and local optimization ability of the algorithm and improve its convergence accuracy and speed. Under the Simulink simulation platform

the algorithm is applied to the maximum power tracking of a photovoltaic power generation system

and compared with the traditional bald eagle search algorithm

particle swarm optimization algorithm and grey wolf optimization algorithm. The results show that the proposed algorithm has faster tracking speed and higher accuracy than the existing three algorithms in four typical scenarios

and the power fluctuation in the tracking process is smaller

which can improve the power generation of photovoltaic system.

关键词

Keywords

references

SHANG Liqun, ZHU Weiwei. Photovoltaic system global maximum power point tracking method based on the global learning adaptive bacteria foraging algorithm[J]. Transactions of China Electrotechnical Society, 2019, 34(12): 2606−2614(in Chinese).

商立群, 朱伟伟. 基于全局学习自适应细菌觅食算法的光伏系统全局最大功率点跟踪方法[J]. 电工技术学报, 2019, 34(12): 2606−2614.

YANG Jialiang, WEI Xia, CHENG Zhijiang, et al. Photovoltaic maximum power point tracking with improved differential evolution[J]. Modern Electric Power, 2024, 41(3): 500−506(in Chinese).

杨家梁, 魏霞, 程志江, 等. 改进差分进化的光伏最大功率点跟踪[J]. 现代电力, 2024, 41(3): 500−506.

WANG Jiajian, PAZLAI · Mahemuti, KONG Bolong. Research on MPPT of photovoltaic system based on improved SALP swarm algorithm[J]. Acta Energiae Solaris Sinica, 2022, 43(04): 191−197(in Chinese).

王加健, 帕孜来·马合木提, 孔博龙. 基于改进樽海鞘群算法的光伏系统MPPT研究[J]. 太阳能学报, 2022, 43(04): 191−197.

SUNDARESWARAN K, VIGNESHKUMAR V, SANKAR P, et al. Development of an improved PO algorithm assisted through a colony of foraging ants for MPPT in PV system[J]. IEEE Transactions on Industrial Informatics, 2015, 12(1): 187−200.

XU Libin, CHENG Ruofa, YANG Jiajing, et al. An improved INC algorithm under fast varying solar irradiation[J]. Advanced Technology of Electrical Engineering and Energy, 2020, 39(08): 56−65(in Chinese).

许立斌, 程若发, 杨嘉静, 等. 针对光照强度快速变化的改进INC算法[J]. 电工电能新技术, 2020, 39(08): 56−65.

LASHEEN M, ABDEL-SALAM M. Maximum power point tracking using Hill Climbing and ANFIS techniques for PV applications: A review and a novel hybrid approach[J]. Energy conversion and management, 2018, 171: 1002−1019.

JIANG Ping, LUAN Yanjun, ZHANG Wei, et al. Research of mult-peak MPPT under partial shaded conditions based on improved PSO algorithm[J]. Acta Energiae Solaris Sinica, 2021, 42(08): 140−145(in Chinese).

姜萍, 栾艳军, 张伟, 等. 局部遮阴下基于改进PSO的多峰值MPPT研究[J]. 太阳能学报, 2021, 42(08): 140−145.

ZHANG Zenghui, DENG Yuhao, LI Chunwei, et al. Photovoltaic MPPT method based on improved grey wolf optimization algorithm[J]. Electrical Measurement Instrumentation, 2022, 59(07): 100−105(in Chinese).

张增辉, 邓宇豪, 李春卫, 等. 基于改进灰狼优化算法的光伏MPPT方法[J]. 电测与仪表, 2022, 59(07): 100−105.

TEY K S, MEKHILEF S, SEYEDMAHMOUDIAN M, et al. Improved differential evolution-based MPPT algorithm using SEPIC for PV systems under partial shading conditions and load variation[J]. IEEE Transactions on Industrial Informatics, 2018, 14(10): 4322−4333.

FARES D, FATHI M, SHAMS I, et al. A novel global MPPT technique based on squirrel search algorithm for PV module under partial shading conditions[J]. Energy Conversion and Management, 2021, 230: 113773.

YANG Yongkang, MIAO Shuwei. MPPT simulation of photovoltaic system based on DE-GWO algorithm[J]. Electronic Measurement Technology, 2022, 45(07): 75−81(in Chinese).

杨永康, 缪书唯. 基于DE-GWO算法的光伏系统MPPT仿真研究[J]. 电子测量技术, 2022, 45(07): 75−81.

ZHAO Peiwen, ZHANG Damin, ZHANG Linna, et al. Bald eagle search algorithm with golden sine algorithm and crisscross strategy[J]. Journal of Computer Applications, 2023, 43(01): 192−201(in Chinese).

赵沛雯, 张达敏, 张琳娜, 等. 融合黄金正弦算法和纵横交叉策略的秃鹰搜索算法[J]. 计算机应用, 2023, 43(01): 192−201.

YANG Guohua, FENG Ji, LIU Xuan, et al. Fault location of a distribution network hierarchical model with a distribution generator based on IBES[J]. Power System Protection and Control, 2022, 50(18): 1−9(in Chinese).

杨国华, 冯骥, 柳萱, 等. 基于改进秃鹰搜索算法的含分布式电源配电网分区故障定位[J]. 电力系统保护与控制, 2022, 50(18): 1−9.

WU Haotian, SUN Rongfu, LIAO Siyang, et al. Short-term wind power probability forecasting method based on improved meteorological clustering and classification[J]. Automation of Electric Power Systems, 2022, 46(15): 56−65(in Chinese).

吴浩天, 孙荣富, 廖思阳, 等. 基于改进气象聚类分型的短期风电功率概率预测方法[J]. 电力系统自动化, 2022, 46(15): 56−65.

MO Shixun, YANG Hao, JIANG Kunping, et al. Parameter identification of transformer J-A model based on improved BES algorithm[J]. Advanced Technology of Electrical Engineering and Energy, 2022, 41(04): 67−74(in Chinese).

莫仕勋, 杨皓, 蒋坤坪, 等. 基于改进秃鹰搜索算法的变压器J-A模型参数辨识[J]. 电工电能新技术, 2022, 41(04): 67−74.

LI Ang, LIU Wenfeng, LI Yinke, et al. Research on MPPT control of photovoltaic system based on IPO-ICS algorithm[J]. Acta Energiae Solaris Sinica, 2023,44(5): 203-209(in Chinese).

李昂, 刘文锋, 李音柯, 等. 基于IPO-ICS算法的光伏系统MPPT控制研究[J]. 太阳能学报, 2023,44(5): 203-209.

ALSATTAR H A, ZAIDAN A A, ZAIDAN B B. Novel meta-heuristic bald eagle search optimisation algorithm[J]. Artificial Intelligence Review, 2020, 53: 2237−2264.

HUANG Chenchen, WEI Xia, HUANG Deqi, et al. Shuffled frog leaping grey wolf algorithm for solving high dimensional complex functions[J]. Control Theory Applications, 2020, 37(07): 1655−1666(in Chinese).

黄晨晨, 魏霞, 黄德启, 等. 求解高维复杂函数的混合蛙跳–灰狼优化算法[J]. 控制理论与应用, 2020, 37(07): 1655−1666.

HUANG Zhipeng, PAN Sanbo. MPPT control of model predictive control combined with golden section method[J]. Renewable Energy Resources, 2016, 34(12): 1766−1771(in Chinese).

黄志鹏, 潘三博. 模型预测控制结合黄金分割点法的MPPT控制[J]. 可再生能源, 2016, 34(12): 1766−1771.

ELTAMALY A M, FARH H M H. Dynamic global maximum power point tracking of the PV systems under variant partial shading using hybrid GWO-FLC[J]. Solar Energy, 2019, 177: 306−316.

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