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Received: 2008-02-21

Revision Accepted: 2008-06-23

Crosschecked: 2008-12-26

Cited: 10

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Journal of Zhejiang University SCIENCE A 2009 Vol.10 No.2 P.263~270


A maximum power point tracker for photovoltaic energy systems based on fuzzy neural networks

Author(s):  Chun-hua LI, Xin-jian ZHU, Guang-yi CAO, Wan-qi HU, Sheng SUI, Ming-ruo HU

Affiliation(s):  Fuel Cell Research Institute, Shanghai Jiao Tong University, Shanghai 200240, China; more

Corresponding email(s):   viven_lch@163.com

Key Words:  Photovoltaic array, Maximum power point tracking (MPPT), Fuzzy neural network controller (FNNC), Radial basis function neural network (RBFNN)

Chun-hua LI, Xin-jian ZHU, Guang-yi CAO, Wan-qi HU, Sheng SUI, Ming-ruo HU. A maximum power point tracker for photovoltaic energy systems based on fuzzy neural networks[J]. Journal of Zhejiang University Science A, 2009, 10(2): 263~270.

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journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

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%T A maximum power point tracker for photovoltaic energy systems based on fuzzy neural networks
%A Chun-hua LI
%A Xin-jian ZHU
%A Guang-yi CAO
%A Wan-qi HU
%A Sheng SUI
%A Ming-ruo HU
%J Journal of Zhejiang University SCIENCE A
%V 10
%N 2
%P 263~270
%@ 1673-565X
%D 2009
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0820128

T1 - A maximum power point tracker for photovoltaic energy systems based on fuzzy neural networks
A1 - Chun-hua LI
A1 - Xin-jian ZHU
A1 - Guang-yi CAO
A1 - Wan-qi HU
A1 - Sheng SUI
A1 - Ming-ruo HU
J0 - Journal of Zhejiang University Science A
VL - 10
IS - 2
SP - 263
EP - 270
%@ 1673-565X
Y1 - 2009
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A0820128

To extract the maximum power from a photovoltaic (PV) energy system, the real-time maximum power point (MPP) of the PV array must be tracked closely. The non-linear and time-variant characteristics of the PV array and the non-linear and non-minimum phase characteristics of a boost converter make it difficult to track the MPP for traditional control strategies. We propose a fuzzy neural network controller (FNNC), which combines the reasoning capability of fuzzy logical systems and the learning capability of neural networks, to track the MPP. With a derived learning algorithm, the parameters of the FNNC are updated adaptively. A gradient estimator based on a radial basis function neural network is developed to provide the reference information to the FNNC. Simulation results show that the proposed control algorithm provides much better tracking performance compared with the fuzzy logic control algorithm.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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