基于改进布谷鸟搜索算法的光伏MPPT控制
Photovoltaic MPPT Control Based on Improved Cuckoo Search Algorithm
李艳波 1王笑寒 1陈俊硕 1高江琦1
作者信息
- 1. 长安大学电气工程系,陕西 西安 710064
- 折叠
摘要
复杂阴影情况下,光伏阵列的P-V特性曲线会出现多个峰值,传统的MPPT算法因不能准确识别局部峰值和全局峰值,而无法进行复杂阴影情况下的最大功率点跟踪.针对传统布谷鸟搜索(Cuckoo Search,CS)算法因鸟窝之间缺乏交流能力导致可能陷入局部最优的问题,提出了一种多策略改进布谷鸟搜索(EGICS)算法.将传统CS算法中发现概率值的选择自适应变化,提高算法的搜索能力;将步长因子自适应化,提高算法的收敛速度;引入高斯扰动和精英反向学习策略,增加种群多样性,避免算法陷入局部最优.对EGICS算法在单峰和多峰函数中进行性能测试,并将其应用于光伏系统MPPT控制中进行仿真验证.仿真结果表明,EGICS算法在收敛速度、跟踪精度以及动态稳定三个方面有更好的效果.
Abstract
In the case of complex shadows,the P-V characteristic curve of the photovoltaic array will have multi-ple peaks.The traditional MPPT algorithm cannot accurately identify the local peak and the global peak,so it is una-ble to track the maximum power point in the case of complex shadows.This paper proposes a multi-strategy improved cuckoo search(EGICS)algorithm to solve the problem that the traditional cuckoo search(CS)algorithm may fall in-to a local optimum due to the lack of communication between nests.The selection of the discovery probability value in the traditional CS algorithm is adaptively changed to improve the search ability of the algorithm.The step factor is a-daptive to improve the convergence speed of the algorithm.Gaussian perturbation and elite reverse learning strategy are introduced to increase the diversity of the population and avoid the algorithm falling into the local optimum.The performance of the EGICS algorithm is tested in single-peak and multi-peak functions,and it is applied to the MPPT control of PV systems for simulation verification.The simulation results show that the EGICS algorithm has better effects in three aspects:convergence speed,tracking accuracy and dynamic stability.
关键词
局部阴影/最大功率点跟踪/布谷鸟搜索算法/高斯扰动/精英反向学习Key words
Partial shadow/Maximum power point tracking/Cuckoo search algorithm/Gaussian perturbation/Elite reverse learning引用本文复制引用
基金项目
国家重点研发计划(2021YFB1600200)
国家自然科学基金面上项目(12172064)
陕西省重点研发计划(2021KW-13)
河南省交通厅科技项目(2021G10)
出版年
2024
NETL
NSTL
万方数据