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Intelligent maximum power point tracking for PV system using Hopfield neural network optimized fuzzy logic controller

机译:基于Hopfield神经网络优化模糊控制器的光伏系统智能最大功率点跟踪。

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摘要

This paper presents a new method for maximum power point tracking of photovoltaic (PV) energy harvesting system by using the Hopfield neural network (HNN) optimized fuzzy logic controller (FLC). In the proposed method, HNN is utilized to automatically tune the FLC membership functions instead of adopting the trial-and-error approach. A complete simulation model of a PV system using the MAT-LAB/Simulink software is developed to validate the HNN optimized FLC. A hardware prototype of the PV maximum power point tracking controller was also implemented using the dSPACE DS1104 controller. Simulation and experimental results show the performance and effectiveness of the HNN optimized FLC. It is proven that the proposed HNN optimized FLC can provide accurate tracking of the PV maximum power point and improve the efficiency of PV systems.
机译:本文提出了一种利用Hopfield神经网络(HNN)优化的模糊逻辑控制器(FLC)跟踪光伏(PV)能量采集系统最大功率点的新方法。在提出的方法中,HNN用于自动调整FLC成员函数,而不是采用试错法。开发了使用MAT-LAB / Simulink软件的光伏系统的完整仿真模型,以验证HNN优化的FLC。还使用dSPACE DS1104控制器实现了PV最大功率点跟踪控制器的硬件原型。仿真和实验结果表明,HNN优化的FLC的性能和有效性。事实证明,提出的HNN优化FLC可以准确跟踪光伏最大功率点并提高光伏系统的效率。

著录项

  • 来源
    《Energy and Buildings》 |2012年第8期|p.29-38|共10页
  • 作者单位

    Department of Electrical, Electronic & Systems Engineering, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia;

    Department of Electrical, Electronic & Systems Engineering, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia;

    Department of Electrical, Electronic & Systems Engineering, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    photovoltaic; maximum power point tracking; fuzzy logic; hopfield neural network;

    机译:光伏最大功率点跟踪;模糊逻辑;Hopfield神经网络;

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