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

页码：18 / 23

页数：6

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