JACIII Vol.18 No.4 pp. 682-696
doi: 10.20965/jaciii.2014.p0682


Incremental Learning on a Budget and its Application to Quick Maximum Power Point Tracking of Photovoltaic Systems

Koichiro Yamauchi

Department of Computer Science, Chubu University, 1200 Matsumoto, Kasugai, Aichi 487-8501, Japan

October 15, 2013
March 28, 2014
July 20, 2014
limited feneral regression neural network (LGRNN), incremental learning on a budget, embedded systems, kernel machines, maximum power point tracker (MPPT), shadow-flicker
Recent improvements in embedded systems has enabled learning algorithms to provide realistic solutions for system identification problems. Existing learning algorithms, however, continue to have limitations in learning on embedded systems, where physical memory space is constrained. To overcome this problem, we propose a Limited General Regression Neural Network (LGRNN), which is a variation of general regression neural network proposed by Specht or of simplified fuzzy inference systems. The LGRNN continues incremental learning even if the number of instances exceeds the maximum number of kernels in the LGRNN. We demonstrate LGRNN advantages by comparing it to other kernel-based perceptron learning methods. We also propose a light-weighted LGRNN algorithm, -LGRNNLight- for reducing computational complexity. As an example of its application, we present a Maximum Power Point Tracking (MPPT) microconverter for photovoltaic power generation systems. MPPT is essential for improving the efficiency of renewable energy systems. Although various techniques exist that can realize MPPT, few techniques are able to realize quick control using conventional circuit design. The LGRNN enables the MPPT converter to be constructed at low cost using the conventional combination of a chopper circuit and microcomputer control. The LGRNN learns the Maximum Power Point (MPP) found by Perturb and Observe (P&O), and immediately sets the converter reference voltage after a sudden irradiation change. By using this strategy, the MPPT quickly responds without a predetermination of parameters. The experimental results suggest that, after learning, the proposed converter controls a chopper circuit within 14 ms after a sudden irradiation change. This rapid response property is suitable for efficient power generation, even under shadow flicker conditions that often occur in solar panels located near large wind turbines.
Cite this article as:
K. Yamauchi, “Incremental Learning on a Budget and its Application to Quick Maximum Power Point Tracking of Photovoltaic Systems,” J. Adv. Comput. Intell. Intell. Inform., Vol.18 No.4, pp. 682-696, 2014.
Data files:
  1. [1] J. Platt, “A resource allocating network for function interpolation,” Neural Computation, Vol.3, No.2, pp. 213-225, 1991.
  2. [2] D. F. Specht, “A general regression neural network,” IEEE TRANSACTIONS ON NEURAL NETWORKS, Vol.2, No.6, pp. 568-576, Nov. 1991.
  3. [3] K. Yamauchi, “Pruning with replacement and automatic distance metric detection in limited general regression neural networks,” Proc. of Int. Joint Conf. on Neural Networks, San Jose, California, USA, July 31 – August 5, 2011, IEEE, pp. 899-906, Jul. 2011.
  4. [4] K. Yamauchi, “Incremental learning on a budget and its application to quick maximu power point tracking of photovoltaic systems, The 6th Int. Conf. on Soft Computing and Intelligent Systems, pp. 71-78, Nov. 2012.
  5. [5] J. Ma, J. Theiler, and S. Perkins, “Accurate on-line support vector regression,” Neural Computation, Vol.15, pp. 2683-2703, 2003.
  6. [6] L. Yingwei, N. Sundararajan, and P. Saratchandran, “A sequential learning scheme for function approximation using minimal radial basis function neural networks,” Neural Computation, Vol.9, pp. 461-478, 1997.
  7. [7] JFG. de Freitas, M. Niranjan, and AH. Gee, “Hierarchical bayesiankalman models for regularisation and ard in sequential learning,” Technical Report TR 307, CUED/F-INFENG, 1998.
  8. [8] G.-B. Huang, P. Saratchandran, and N. Sundararajan, “A generalized growing and pruning rbf (ggap-rbf) neural network for function approximation,” IEEE TRANSACTIONS ON NEURAL NETWORKS, Vol.16, No.1, pp. 57-57, Jan. 2005.
  9. [9] F. Orabona, J. Keshet, and B. Caputo, “The projectron: a bounded kernel-based perceptron,” In ICML2008, pp. 720-727, 2008.
  10. [10] W. He and S. Wu, “A kernel-based perceptron with dynamic memory,” Neural Networks, Vol.25, pp. 105-113, 2011.
  11. [11] K. Yamauchi, Y. Kondo, A. Maeda, K. Nakano, and A. Kato, “Incremental learning on a budget and its application to power electronics,” The 20th Int. Conf. on Neural Information Processing, page to appear. Springer-Verlag, Nov. 2013.
  12. [12] T. Noguchi, S. Togashi, and R. Nakamoto, “Shortcurrent pulse-based maximum-power-point tracking method for multiple photovoltaic-and-converter module system,” IEEE Transactions on Industrial Electronics, Vol.49, No.1, pp. 217-223, Feb. 2002.
  13. [13] T. Hiyama and K. Kitabayashi, “Neural network based estimation of maximum power generation from pv module using environmental information,” IEEE Transactions on Energy Conversion, Vol.12, No.3, pp. 241-247, Sep. 1997.
  14. [14] R. Akkaya, A. A. Kulaksiz, and Ö. Aydoĝdu, “Dsp implementation of a pv system with ga-mlp-nn based mppt controller supplying bldc motor drive,” Energy Conversion & Management, Vol.48, pp. 210-218, 2007.
  15. [15] Y. Kohata, K. Yamauchi, and M. Kurihara, “Highspeed maximum power point tracker for photovoltaic systems using online learning neural networks,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.14, No.6, pp. 677-682, 2010.
  16. [16] X. Xu, D. Hu, and X. Lu, “Kernel-based least squares policy iteration for reinforcement learning,” IEEE TRANSACTIONS ON NEURAL NETWORKS, Vol.18, No.4, pp. 973-992, Jul. 2007.
  17. [17] J. L. Bentley, “Multidimensional binary search trees used for associative searching,” Communications of the ACM, Vol.18, No.9, pp. 509-517, 1975.
  18. [18] K. Yamauchi, “An importance weighted projection method for incremental learning under unstationary environments,” IJCNN2013: The Int. Joint Conf. on Neural Networks 2013, pp. 506-514, IEEE, Aug. 2013.
  19. [19] O. Dekel, S. Shalev-Shwartz, and Y. Singer, “The forgetron: A kernel-based perceptron on a budget,” SIAM Journal on Computing (SICOMP), Vol.37, No.5, pp. 1342-1372, Jan. 2008.
  20. [20] A. R. Webb, “Functional approximation by feed-forward networks: a least-squares approach to generalization,” IEEE TRANSACTIONS ON NEURAL NETWORKS, Vol.5, No.3, pp. 363-371, May 1994.
  21. [21] S. Schaal and C. G. Atkeson, “Constructive incremental learning from only local information. Neural Computation,” Vol.10, No.8, pp. 2047-2084, Nov. 1998.
  22. [22] A. Ghaffari, S. Seshagiri, and M. Krstić, “Power optimization for photovoltaic micro-converters using multivariable gradient-based extremum-seeking,” 2012 American Control Conf., pp. 3383-3388, The Institute of Electrical and Electronics Engineers, Inc. New York, New York, Jun. 2012.
  23. [23] T. Esram and P. L. Chapman, “Comparison of photovoltaic array maximum power point tracking techniques,” IEEE Transactions on Energy Conversion, Vol.22, No.2, pp. 439-449, Jun. 2007.
  24. [24] T. Esram, J. W. Kimball, P. T. Krein, P. L. Chapman, and P. Midya, “Dynamic maximum power point tracking of photovoltaic arrays using ripple correlation control,” IEEE Transaction on power electronics, Vol.21, No.5, pp. 1282-1291, Sep. 2006.
  25. [25] Y. T. Tan, D. S. Kirschen, and N. Jenkins, “A model of pv generation suitable for stability analysis,” IEEE Transactions on Energy Conversion, Vol.19, No.4, pp. 748-755, Dec. 2004.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on May. 10, 2024