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.

  1. [1] G. J. Yu, Y. S. Jung, J. Y. Choi, and G. S. Kim, “A novel two-mode MPPT control algorithm based on comparative study of existing algorithms,” Solar Energy, Vol.76, No.4, pp. 455-463, 2004.
  2. [2] T. Tafticht, K. Agbossou, M. L. Doumbia, and A. Cheriti, “An improved maximum power point tracking method for photovoltaic systems,” Renewable Energy, Vol.33, No.7, pp. 1508-1516, 2008.
  3. [3] R. Akkaya, A. A. Kulaksiz, and O. Aydogdu, “DSP implementation of a PV system with GA-MLP-NN based MPPT controller supplying BLDC motor drive,” Energy Conversion and Management, Vol.48, No.1, pp. 210-218, 2007.
  4. [4] T. Hiyama and K. Kitabayashi, “Neural Network Based Estimation of Maximum Power Generation from PV Module Using Environmental Information,” IEEE Trans. on Energy Conversion, Vol.12, No.3, pp. 241-247, 1997.
  5. [5] Y. Kohata, K. Yamauchi, and M. Kurihara, “Quick Maximum Power Point Tracking of Photovoltaic Using Online Learning Neural Network,” Neural Information Processing, 16th Int. Conf., ICONIP 2009 Proc., Part I, LNCS 5863, pp. 606-613, 2009.
  6. [6] D. F. Specht, “A general regression neural network,” IEEE trans. Neural Networks, Vol.2, No.6, pp. 568-576, 1991.
  7. [7] D. Tomandl and A. Schober, “A Modified General Regression Neural Network (MGRNN) with a new efficient training algorithm as a robust “black-box”-tool for data analysis,” Neural Networks, Vol.14, pp. 1023-1034, 2001.
  8. [8] M. Su, J. Lee, and K. Hsieh, “A new ARTMAP-based neural network for incremental learning,” Neurocomputing, Vol.69, pp. 2284-2300, 2006.

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Last updated on Sep. 21, 2017