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JRM Vol.27 No.5 pp. 489-495
doi: 10.20965/jrm.2015.p0489
(2015)

Paper:

Improvement of EMC in MPPT Control of Photovoltaic System Using Auto-Tuning Adaptive Velocity Estimator

Tsuyoshi Ohba, Risa Matsuda, Haruo Suemitsu, and Takami Matsuo

Department of Architecture and Mechatronics, Oita University
700 Dannoharu, Oita 870-1192, Japan

Received:
April 27, 2015
Accepted:
July 15, 2015
Published:
October 20, 2015
Keywords:
maximum power point tracking, incremental conductance method, differential filter, electro-magnetic compatibility (EMC)
Abstract
Proposed modified-IC method

Output by a photovoltaic array is nonlinear and changes with solar irradiation and cell temperature. Maximum Power Point Tracking (MPPT) is needed to maximize the energy produced. Most MPPT techniques include the time derivative of current and voltage. These electrical signals are disturbed by high-frequency noise such as from the power device switching. Lowpass filters are used to reduce circuit noise, but estimation error occurs when the maximum power point is calculated. We therefore apply an adaptive observer to estimate the time derivative of noisy signals. Specifically, we propose an auto-tuning velocity estimator with a forgetting factor based on the adaptive observer. We also improve the incremental conductance method by using an auto-tuning velocity estimator.

Cite this article as:
T. Ohba, R. Matsuda, H. Suemitsu, and T. Matsuo, “Improvement of EMC in MPPT Control of Photovoltaic System Using Auto-Tuning Adaptive Velocity Estimator,” J. Robot. Mechatron., Vol.27, No.5, pp. 489-495, 2015.
Data files:
References
  1. [1] D. P. Holm and M. E. Ropp, “Comparative Study of Maximum Power Point Tracking Algorithms,” Prog. Photovolt: Res. Appl., Vol.11, Issue 1, pp. 47-62, 2003.
  2. [2] M. A. S. Masoum, H. Dehbonet, and E. F. Fuchs, “Theoretical and experimental analyses of photovoltaic systems with voltage and current-based maximum power-point tracking,” IEEE Trans. on Energy Convers., Vol.17, No.4, pp. 514-522, 2002.
  3. [3] 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, Issue 4, pp. 455-463, 2003.
  4. [4] W. Xiao, W. G. Dunford, P. R. Palmer, and A. Capel, “Regulation of Photovoltaic Voltage,” IEEE Trans. on Industrial Electronics, Vol.54, Issue 3, pp. 1365-1374, 2007.
  5. [5] R. Faranda and S. Leva, “Energy comparison of MPPT techniques for PV Systems,” WSEAS Trans. on Power Systems, Vol.6, Issue 3, pp. 446-455, 2008.
  6. [6] M. Calavia, J. M. Periée, J. F. Sanz, and J. Salláan, “Comparison of MPPT strategies for solar modules,” Proc. of Int. Conf. on Renewable Energies and Power Quality, 2010.
  7. [7] A. Tille and M. Montanari, “A low-noise estimator of angular speed and acceleration from shaft-encoder measurements,” ATKAAF, Vol.42, Issue 3-4, pp. 169-176, 2001.
  8. [8] P. R. Belanger, P. Dobrovolny, A. Helmy, and X. Zhang, “Estimation of angular velocity and acceleration from shaft-encoder measurements,” Int. J. Robot Res., Vol.17, Issue 11, pp. 1225-1233, 1998.
  9. [9] A. Levant, “Robust exact differentiation via sliding mode technique,” Automatica, Vol.34, Issue 3, pp. 379-384, 1998.
  10. [10] T. Emaru, K. Imagawa, Y. Hoshino, and Y. Kobayashi, “Velocity and Acceleration Estimation by a Nonlinear Filter Based on Sliding Mode and Application to Control System,” J. of Robotics and Mechatronics, Vol.21, Issue 5, pp. 590-596, 2009.
  11. [11] T. Matsuo, S. Wada, and H. Suemitsu, “Model-Based and Non-Model-Based Velocity Estimators for Mobile Robots,” Int. J. Innovating Computing, Information and Control, Vol.4, Issue 12, pp. 3123-3133, 2008.
  12. [12] T. Matsuo , K. Adachi, and H. Suemitsu, “Frequency Estimation with an LMI-based Adaptive Update Law,” Int. J. Int. J. of Advanced Mechatronic Systems, Vol.1, Issue 2, pp. 100-107, 2008.
  13. [13] T. Nomura, Y. Kitsuka, and T. Matsuo, “Nonmodel-Based Estimation for Velocity and Acceleration by Adaptive Identification Method,” IEEJ Trans. on Electrical and Electronic Engineering, Vol.5, Issue 3, pp. 372-374, 2010.
  14. [14] Y. Kitsuka, T. Nimiya, H. Suemitsu, and T. Matsuo, “Non-Model-Based Velocity and Acceleration Estimators for a Suspension System with Parallel Connection of a Hydraulic Actuator,” Proc. of 2010 IEEE Multi-Conference on Systems and Control, pp. 549-554, 2010.
  15. [15] Y. Kawakami, Y. Eguchi, T. Nimiya, H. Suemitsu, and T. Matsuo, “Velocity and Acceleration Estimation by Iterative Learning Observer and Performance Validation with MEMS-Based Inertial Sensors,” Int. J. of Advanced Mechatronic Systems, Vol.5, Issue 2, pp. 113-121, 2013.
  16. [16] M. Ezzeldin, P. P. J. van den Bosch, and R. Waarsing, “Improved Convergence of MRAC Design for Printing System,” Proc. of American Control Conf., pp. 3232-3237, 2009.
  17. [17] Y. Eguchi, T. Ohba, H. Suemitsu, and T. Matsuo, “Auto-Tuning Velocity Estimator by Using Adaptive Observer,” ICIC Express Letters, Vol.8, Issue 2, pp. 427-433, 2014.

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Last updated on Nov. 15, 2018