JACIII Vol.14 No.6 pp. 677-682
doi: 10.20965/jaciii.2010.p0677


High-Speed Maximum Power Point Tracker for Photovoltaic Systems Using Online Learning Neural Networks

Yasushi Kohata*, Koichiro Yamauchi**, and Masahito Kurihara*

*Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Hokkaido 060-0814, Japan

**Chubu University, Department of Information Science, 1200 Matsumoto-cho, Kasugai-shi, Aichi 487-8501, Japan

January 25, 2010
May 25, 2010
September 20, 2010
Photo Voltaic (PV), MPPT, General Regression Neural Network (GRNN), Perturbation and Observation (P&O), embedded system implementation
Photo Voltaic (PV) devices have a Maximum Power Point (MPP) at which they generate maximum power. Because the MPP depends on solar radiation and PV panel temperature, it is not constant over time. A Maximum Power Point Tracker (MPPT) is widely used to continuously obtain maximum power, but if the solar radiation changes rapidly, the efficiency of most classic MPPT (e.g., the Perturbation and Observation (P&O) method) reduces. MPPT controllers using neural network respond quickly to rapidly changing solar radiation but must usually undergo prelearning using PV-specific data, so we propose MPPT that handles both online learning of PV properties and feed-forward control of the DC-DC converter with a neural network. Both simulation results and actual device performance using our proposed MPPT showed great efficiency even under rapidly changing solar radiation. Our proposal is implemented using a small microcomputer using low computational power.
Cite this article as:
Y. Kohata, K. Yamauchi, and M. Kurihara, “High-Speed Maximum Power Point Tracker for Photovoltaic Systems Using Online Learning Neural Networks,” J. Adv. Comput. Intell. Intell. Inform., Vol.14 No.6, pp. 677-682, 2010.
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Last updated on Jul. 12, 2024