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

Authors:
Kuei-Hsiang Chao;Ching-Ju Li;Meng-Huei Wang
Affiliations:
Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung, Taiwan, R.O.C.;Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung, Taiwan, R.O.C.;Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung, Taiwan, R.O.C.
Venue:
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Year:
2009

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Abstract

In this paper, a 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.