带FNN太阳能电池最大功率跟踪控制器的DC/DC升压变换器设计

Hung-Ching Lu, Te-Lung Shih
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引用次数: 18

摘要

本文演示了采用DC/DC升压变换器和模糊神经网络(FNN)系统的最大功率点跟踪(MPPT)控制器。该方法将DC/DC升压变换器的拓扑结构简化为状态方程,便于Matlab仿真。此外,FNN系统采用模糊与神经网络(NN)相结合的方法,具有不确定性信息处理和神经网络学习的优点。在分配合适的结构后,调整隶属函数并分配算法权重,在参数学习过程中有效地跟踪最大功率点。仿真结果验证了该系统能够高效、快速地跟踪MPP并将太阳能电池的电能转换到电池组中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design of DC/DC Boost converter with FNN solar cell Maximum Power Point Tracking controller
This paper demonstrates the Maximum Power Point Tracking (MPPT) controller that uses a DC/DC Boost converter with a Fuzzy Neural Network (FNN) system. It simplifies the topology of the DC/DC boost converter model to state equations, which is easy to simulate with Matlab. Additionally, the FNN system uses an integrated Fuzzy and Neural Network (NN), which advantages include uncertainty information processing and neural network learning. After assigning a suitable structure, we adjust the membership function and assign the algorithm weighting to track the maximum power point effectively in the parameters leaning process. The simulation result has verified the system to be efficient and rapid in tracking the MPP and converting the power from solar cells into the battery bank.
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