GA-RBF neural network based maximum power point tracking for grid-connected photovoltaic systems

被引：0
|
作者：
Zhang, L [1 ]
Bai, YF [1 ]
Al-Amoudi, A [1 ]
机构：
[1] Univ Leeds, Sch Elect & Elect Engn, Leeds LS2 9JT, W Yorkshire, England
来源：
INTERNATIONAL CONFERENCE ON POWER ELECTRONICS, MACHINES AND DRIVES | 2002年 / 487期
关键词：
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a novel GA-RBFNN (Genetic Algorithm trained Radial Basis Function Neural Network)-based model to carry out the Maximum Power Point Tracking (MPPT) for grid-connected photovoltaic (PV) power generation control systems. The hidden layer of the neural network is self-organised by the GA-based RBF growing algorithm. The trained GA-RBFNN-based MPP model is then employed to predict the maximum power points of a PV array using measured environmental data. The simulation results are compared with the conventional P&O method, and the current/voltage waveforms of the PV panel are presented and discussed.
引用
收藏
页码：18 / 23
页数：6
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