基于改进GSA算法的光伏最大功率点跟踪技术研究  

Research on Photovoltaic Maximum Power Point Tracking Technology Based on Improved GSA Algorithm

作　　者：李婕 张盼盼 齐山成 白圆童 杨捷 LI Jie;ZHANG Panpan;QI Shancheng;BAI Yuantong;YANG Jie(School of Electrical Engineering and Automation,Henan Institute of Technology,Xinxiang 453003,China;Xinxiang Engineering Research Center for Intelligent Condition Monitoring of Machinery,Xinxiang 453003,China)

机构地区：[1]河南工学院电气工程与自动化学院,河南新乡453003 [2]新乡市旋转机械运行状态智能监测工程技术研究中心,河南新乡453003

出　　处：《河南工学院学报》2025年第1期12-17,共6页Journal of Henan Institute of Technology

基　　金：河南省科技攻关项目(242102240040)。

摘　　要：光伏电池的输出特性随着温度、光照等环境因素变化,导致光伏系统发电效率低,因此提高光伏组件的最大功率点跟踪性能是十分必要的。通过搭建光伏电池的模型,提出了一种改进GSA算法的光伏MPPT控制策略,利用T-S模糊神经网络算法动态修正GSA算法中的衰减因子α,改善了GSA算法中因引力常数计算困难而导致最大功率点跟踪速度慢、误差大等问题。对正常和局部阴影工况下光伏组件MPPT性能的验证结果表明,改进后的GSA算法在光伏MPPT控制中较传统GSA算法具有收敛速度快、搜索振荡小和稳态精度高的优点。The output characteristics of photovoltaic modules vary with temperature,environment,and other factors,resulting in low power generation efficiency of photovoltaic systems.Therefore,it is necessary to improve the maximum power point tracking performance of photovoltaic modules.A model of photovoltaic modules is built and an improved gravity search algorithm(GSA)for photovoltaic maximum power point tracking(MPPT)control strategy is proposed in this paper.The T-S fuzzy neural network algorithm is used to dynamically modify the attenuation factorαin the GSA algorithm,which improves the problems of slow power point tracking speed and large errors caused by difficult calculation of gravity constants in the GSA algorithm.The MPPT performance of photovoltaic modules under normal and local shadow conditions was verified,and the results showed that the improved GSA algorithm has the advantages of faster convergence speed,smaller search oscillation,and higher steady-state accuracy in photovoltaic MPPT control compared to traditional GSA algorithms.

关 键 词：光伏组件模型 改进引力搜索算法 MPPT T-S模糊神经网络 

分 类 号：TM615[电气工程—电力系统及自动化]