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Study on Control Strategy for Photovoltaic Energy Systems Based on Recurrent Fuzzy Neural Networks

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3 Author(s)
Li Chun-hua ; Fuel Cell Res. Inst., Shanghai Jiao Tong Univ., Shanghai, China ; Jing Xu ; Zhu Xin-jian

To increase the output efficiency of a photovoltaic (PV) energy system, the real-time maximum power point (MPP) of the PV array must be tracked closely. Herein a recurrent fuzzy neural network controller (RFNNC) was proposed to track the MPP. A radial basis function neural network (RBFNN) was developed to provide the reference information to the RFNNC. With a derived learning algorithm, the parameters of the RFNNC were updated adaptively. The mean square error of the estimated tracking error is 0.4×10-2 which guarantees a good predicting performance of the RBFNN. The RFNNC only needs about 4 ms to reach steady state with small fluctuation. Compared with the fuzzy logic control algorithm, simulation results show that the proposed control algorithm yields much better tracking performance.

Published in:

Natural Computation, 2009. ICNC '09. Fifth International Conference on  (Volume:2 )

Date of Conference:

14-16 Aug. 2009