Journal of Zhejiang University SCIENCE A
ISSN 1673-565X(Print), 1862-1775(Online), Monthly

2009   Vol. 10   No. 2   p. 263~270

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

Chun-hua LI^†1, Xin-jian ZHU¹, Guang-yi CAO¹, Wan-qi HU², Sheng SUI¹, Ming-ruo HU¹

(¹Fuel Cell Research Institute, Shanghai Jiao Tong University, Shanghai 200240, China)
(²Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100080, China)
^†E-mail: viven_lch@163.com
Received June 23, 2008; revision accepted Feb. 21, 2008; Crosschecked Dec. 26, 2008

Abstract: 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.

Key words: Photovoltaic array, Maximum power point tracking (MPPT), Fuzzy neural network controller (FNNC), Radial basis function neural network (RBFNN)
doi:10.1631/jzus.A0820128             CLC number: TK01; TP2

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