A Maximum Power Point Tracking Method Based on Extension Neural Network for PV Systems

被引：0
|
作者：
Chao, Kuei-Hsiang [1 ]
Li, Ching-Ju [1 ]
Wang, Meng-Huei [1 ]
机构：
[1] Natl Chin Yi Univ Technol, Dept Elect Engn, Taichung, Taiwan
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2009, PT 1, PROCEEDINGS | 2009年 / 5551卷
关键词：
Maximum power point tracking (MPPT); Perturbation and observation (P&O) method; Incremental conductance (INC) method; Photovoltaic (PV) system; Extension neural network (ENN); FUZZY CONTROLLER; MPPT METHOD;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, it maximum power point tracking (MPPT) technique based on extension neural network (ENN) was proposed to make full utilization of photovoltaic (PV) array Output power which depends on solar insolation and ambient temperature. The proposed ENN MPPT algorithm can automatically adjust the step size to track the PV array maximum power point (MPP). Compared with the conventional fixed step size perturbation and observation (P&O) and incremental conductance (INC) methods, the presented method is able to effectively improve the dynamic response and steady state performance of the PV systems Simultaneously. A theoretical analysis and the designed principle of the proposed method are described in detail. And some simulation results are made to demonstrate the effectiveness of the proposed MPPT method.
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页码：745 / 755
页数：11
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