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ISSN 0254-0096 CN 11-2082/K

太阳能学报 ›› 2022, Vol. 43 ›› Issue (4): 198-203.DOI: 10.19912/j.0254-0096.tynxb.2020-0803

• 电化学储能安全性与退役动力电池梯次利用关键技术专题 • 上一篇    下一篇

基于神经网络的改进扰动观察法MPPT控制

易磊, 谢雨龙, 曾凡炎, 李嘉琪, 黄海, 陈俊   

  1. 华中科技大学电气与电子工程学院,武汉 430074
  • 收稿日期:2020-08-12 出版日期:2022-04-28 发布日期:2022-10-28
  • 通讯作者: 易磊(1991—),男,硕士、工程师,主要从事电力电子装置以及永磁同步电机驱动控制方面的研究。2019210059@hust.edu.cn
  • 基金资助:
    教育部 2017 年首批新工科研究与实践项目(教高厅函(2018)17号)

IMPROVED PERTURBATION OBSERVATION METHOD MPPT CONTROL BASED ON NEURAL NETWORK

Yi Lei, Xie Yulong, Zeng Fanyan, Li Jiaqi, Huang Hai, Chen Jun   

  1. School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
  • Received:2020-08-12 Online:2022-04-28 Published:2022-10-28

摘要: 为进一步提高光伏发电系统的输出效率和动态调节性能,设计基于神经网络的改进扰动观察法最大功率点跟踪(MPPT)控制。该方法在扰动观察法基础上引入神经网络算法,使得在环境剧烈变化时,系统动态调节速度加快。通过系统仿真和硬件实测,得出该改进扰动观察法相比于传统扰动观察法和神经网络法,在一定程度上能加快动态调节速度和减小稳态误差的结论。

关键词: 神经网络, 光伏组件, MPPT, 扰动观察法

Abstract: In order to further improve the output efficiency and dynamic control performance of the photovoltaic power generation system, an improved perturbation observation method MPPT control is designed in this paper based on neural network. A neural network algorithm is introduced based on the perturbation and observation method, so the dynamic adjustment speed is accelerated when the environment changed drastically. Through system simulation and hardware measurement, it’s concluded that, compared with the traditional perturbation observation and neural network method, the dynamic adjustment speed can be accelerated and steady error can be reduced to some extent with the improved perturbation observation method.

Key words: neural network, photovoltaic module, MPPT, perturbation observation method

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