Full Text:   <6989>

CLC number: TK01; TP2

On-line Access: 

Received: 2008-02-21

Revision Accepted: 2008-06-23

Crosschecked: 2008-12-26

Cited: 10

Clicked: 7255

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

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.

@article{title="A maximum power point tracker for photovoltaic energy systems based on fuzzy neural networks",
author="Chun-hua LI, Xin-jian ZHU, Guang-yi CAO, Wan-qi HU, Sheng SUI, Ming-ruo HU",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%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


[1] Altas, I.H., Sharaf, A.M., 2008. A novel maximum power fuzzy logic controller for photovoltaic solar energy systems. Renewable Energy, 33(3):388-399.

[2] Brambilla, A., 1999. New Approach to Photovoltaic Arrays Maximum Power Point Tracking. 30th Annual IEEE Power Electronics Specialists Conf., South Carolina, USA, p.632-637.

[3] Das, D., Esmaili, R., Xu, L., Nichols, D., 2005. An Optimal Design of a Grid Connected Hybrid Wind/ Photovoltaic/Fuel Cell System for Distributed Energy Production. 32nd Annual Conf. of the IEEE Industrial Electronics Society, Paris, France, p.2499-2504.

[4] Hiyama, T., Kouzuma, S., Imakubo, T., Ortmeyer, T.H., 1995. Evaluation of neural network based real time maximum power tracking controller for PV system. IEEE Trans. Energy Conv., 10(3):543-548.

[5] Huang, S.J., Huang, K.S., Chiou, K.C., 2003. Development and application of a novel radial basis function sliding mode controller. Mechatronics, 13(4):313-329.

[6] Kazimierczuk, M.K., Starman, L.A., 1999. Dynamic performance of PWM DC-DC boost converter with input voltage feedforward control. IEEE Trans. Circuits Syst. I: Fundam. Theory Appl., 46(12):1473-1481.

[7] Lee, C.H., Teng, C.C., 2000. Identification and control of dynamic systems using recurrent fuzzy neural networks. IEEE Trans. Fuzzy Syst., 8(4):349-366.

[8] Lin, F.J., Lin, C.H., 2004. A permanent-magnet synchronous motor servo drive using self-constructing fuzzy neural network controller. IEEE Trans. Energy Conv., 19(1):66-72.

[9] Lin, F.J., Wai, R.J., 2002. Adaptive fuzzy-neural-network control for induction spindle motor drive. IEEE Tran. Energy Conv., 17(4):507-513.

[10] Mukerjee, A.K., Dasgupta, N., 2007. DC power supply used as photovoltaic simulator for testing MPPT algorithms. Renewable Energy, 32(4):587-592.

[11] Patcharaprakiti, N., Premrudeepreechacharn, S., Sriuthaisiriwong, Y., 2005. Maximum power point tracking using adaptive fuzzy logic control for grid-connected photovoltaic system. Renewable Energy, 30(11):1771-1788.

[12] Santos, J.L., Antunes, F., Chehab, A., Cruz, C., 2006. A maximum power point tracker for PV systems using a high performance boost converter. Solar Energy, 80(7):772-778.

[13] Shtessel, Y.B., Zinober, A.S.I., Shkolnikov, I.A., 2003. Sliding mode control of boost and buck-boost power converters using method of stable system centre. Automatica, 39(6):1061-1067.

[14] Swiegers, W., Enslin, J.H.R., 1998. An Integrated Maximum Power Point Tracker for Photovoltaic Panels. Proc. IEEE Int. Symp. on Industrial Electronics, Pretoria, South Africa, p.40-44.

[15] Torres, A.M., Antunes, F.L.M., 1998. An Artificial Neural Network-based Real Time Maximum Power Tracking Controller for Connecting a PV System to the Grid. 24th Annual Conf. of the IEEE Industrial Electronics Society, Aachen, Germany, p.554-558.

[16] Valenciaga, F., Puleston, P.F., Battaiotto, P.E., 2001. Power control of a photovoltaic array in a hybrid electric generation system using sliding mode techniques. IEE Proc.-Control Theory Appl., 148(6):448-455.

[17] Xiao, W., Dunford, W.G., Capel, A., 2004. A Novel Modeling Method for Photovoltaic Cells. IEEE Power Electronics Specialists Conf., Aachen, Germany, p.1950-1956.

[18] Xiao, W., Dunford, W.G., Palmer, P.R., Capel, A., 2007a. Regulation of photovoltaic voltage. IEEE Trans. Ind. Electron., 54(3):1365-1374.

[19] Xiao, W., Ozog, N., Dunford, W.G., 2007b. Topology study of photovoltaic interface for maximum power point tracking. IEEE Trans. Ind. Electron., 54(3):1696-1704.

[20] Zhong, Z.D., Huo, H.B., Zhu, X.J., 2008. Adaptive maximum power point tracking control of fuel cell power plants. J. Power Sources, 176:259-269.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE