| 引用本文: | 王志豪,李自成,王后能,等.基于RBF神经网络的光伏系统MPPT研究[J].电力系统保护与控制,2020,48(6):85-91.[点击复制] |
| WANG Zhihao,LI Zicheng,WANG Houneng,et al.MPPT study of solar PV power system based on RBF neural network algorithm[J].Power System Protection and Control,2020,48(6):85-91[点击复制] |
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| 摘要: |
| 针对光伏发电最大功率点跟踪(MPPT)技术的研究和现状，提出了一种基于径向基(Radical Basis Function, RBF)神经网络的MPPT算法。建立太阳能电池板的数学模型，分析光伏发电的主要影响因素。选取电池板的电压、电流为RBF神经网络的输入层，输出层直接调整Boost电路的占空比，达到最大功率点跟踪的目的。与传统的扰动观察法(P&O)相比，所提出的方法无需设定步长，通过RBF神经网络，直接调节Boost电路的占空比进行最大功率点跟踪。仿真和实验结果表明，所提出的MPPT算法与传统的P&O算法相比有更好的快速性和光伏利用效率。 |
| 关键词: 光伏发电 最大功率点跟踪 RBF神经网络 Boost电路 |
| DOI：10.19783/j.cnki.pspc.190112 |
| 投稿时间：2019-05-24修订日期：2019-11-13 |
| 基金项目:国家自然科学基金资助(41727801);武汉工程大学研究生创新基金资助(CX2018116) |
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| MPPT study of solar PV power system based on RBF neural network algorithm |
| WANG Zhihao,LI Zicheng,WANG Houneng,LIU Qing |
| (School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China) |
| Abstract: |
| Aiming at the development and research status of the Maximum Power Point Tracking (MPPT) technology of solar photovoltaic power, a MPPT algorithm based on Radical Basis Function (RBF) neural network is proposed. Firstly, this paper establishes a parameter model of the solar cell and analyzes the main influencing factors of photovoltaic power generation, selects the voltage and current of the panel as the input layer of the RBF neural network, and directly adjusts the duty of the Boost converter to achieve the maximum power point tracking. Compared with the traditional Perturbation and Observation method (P＆O), this method avoids the difficulty of setting the step size. The duty cycle of the Boost converter is adjusted directly for maximum power point tracking through the RBF neural network. Simulation and experimental results show that the provided MPPT algorithm can track quickly and more efficient than the traditional P&O algorithm. This work is supported by National Natural Science Foundation of China (No. 41727801) and Graduates Innovation Fund of Wuhan University of Engineering (No. CX2018116). |
| Key words: photovoltaic system maximum power point tracking RBF neural network Boost converter |
