Maximum Power Point Tracking for Photovoltaic Systems Based on Adaptive Bald Eagle Search Algorithm

ZHAI Zichen; MIAO Shuwei

doi:10.19725/j.cnki.1007-2322.2023.0252

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Maximum Power Point Tracking for Photovoltaic Systems Based on Adaptive Bald Eagle Search Algorithm

更新时间：2025-10-22

- Maximum Power Point Tracking for Photovoltaic Systems Based on Adaptive Bald Eagle Search Algorithm
- Vol. 42, Issue 5, Pages: 919-927(2025)
- 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：
- 基金信息：
- DOI：10.19725/j.cnki.1007-2322.2023.0252
  CLC： 5
- Published：2025
- 稿件说明：
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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：

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

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