Optimal Polynomial Approximation of Photovoltaic Panel Characteristics Using a Stochastic Approach

Authors

  • Karel Zaplatílek University of Defence
  • Jan Leuchter University of Defence

DOI:

https://doi.org/10.3849/aimt.01005

Abstract

The essence of this article is finding the optimal degree of the p=f(v) real photovoltaic panel characteristics approximation polynomial. The characteristics are considered as one realization of a stochastic system and it is the result of long-term measuring. The outputs are the coefficients of an approximation polynomial of an optimal degree. For its calculation, we use the well-known Euclidean norm of residues. The advantage of this approach is that it takes all the influences on the panel’s attributes into consideration (solar irradiation, temperature, aging, random effects). It is necessary for the approximation to carry out a rotation of the measured characteristics and a backwards rotation of the approximation polynomial course. This method enables us to create a mathematical or numerical model of a real photovoltaic panel of any type. All the algorithms and experiments were done using MATLAB® system.

References

LEUCHTER, J., BAUER, P. and FINNEY, S.J. Modeling and experimental verification of EGS to achieve higher efficiency. In 35th Annual Conference of IEEE Industrial Electronics (IECON 2009. Porto (Portugal), 2009, p. 3983-3986.

LEUCHTER, J., RERUCHA, V. and ZOBAA, A.F. Mathematical modeling of photovoltaic systems. In 14th Power Electronics and Motion Control Conference (EPE-PEMC 2010). Ohrid (Macedonia), 2010, p. 422-427.

ZAPLATILEK, K. and LEUCHTER, J. Photovoltaic Panel Modeling in MATLAB® Environment. Radioengineering, 2011, vol. 20, no. 2, p. 445-450.

ZAPLATILEK, K. and LEUCHTER, J. Behavioral Model of Photovoltaic Panel in Simulink®. Advances in Electrical and Computer Engineering, 2011, vol. 11, no. 4, p. 83-88.

KODALI, H. K. and GANAPATHYSUBRAMANIAN, B. Computer simulation of heterogeneous polymer photovoltaic devices. Modelling and Simulation in Materials Science and Engineering, 2012, vol. 20, no. 3.

GONZALEZ, D., RAMOS, P., CARLOS, A., SAAVEDRA, M. and ANDRES, J. Modeling and control of grid connected photovoltaic systems. Revista Facultad de Ingenieria-Universidad de Antioquia (Colombia), 2012, no. 62, p. 145 - 156.

AMROUCHE, B., GUESSOUM, A. and ABDERREZAK, B. M. A simple behavioural model for solar module electric characteristics based on the first order system step response for MPPT study and comparison. Applied Energy, vol. 91, no. 1, p. 395-404.

QI, Ch. and MING, Z. Photovoltaic Module Simulink Model for a Stand-alone PV System. In Proceedings of the International Conference on Applied Physics and Industrial Engineering (ICAPIE). Wuhan (Peoples Republic of China), 2012, p. 94-100.

DIAZ, F., MONTERO, G. and ESCOBAR, J. M. An adaptive solar radiation numerical model. Journal of Computational and Applied Mathematics, 2012, vol. 236, no. 18, p. 4611-4622.

HEIBER, M. C. and DHINOJWALA, A. Dynamic Monte Carlo modeling of exciton dissociation in organic donor-acceptor solar cells. Journal of Chemical Physics, 2012, vol. 137, no. 1.

WANG, F., Mi, Z. and Su, S. Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters. Energies, 2012, vol. 5, no. 5, p. 1355-1370.

RODRIGO, P., RUS, C. and ALMONACID, F. A new method for estimating angular, spectral and low irradiance losses in photovoltaic systems using an artificial neural network model in combination with the Osterwald model. Solar Energy Materials and Solar Cells, vol. 96, no. 1, p. 186-194.

Di FAZIO, A. R. and RUSSO, M. Photovoltaic generator modelling to improve numerical robustness of EMT simulation. Electric Power Systems Research, vol. 83, no. 1, p. 136-143.

WISSEM, Z., GUEORGUI, K. and HEDI, K. Modeling and technical-economic optimization of an autonomous photovoltaic system. Energy, 2012, vol. 37, no. 1, p. 263-272.

LI, Z., He, S. and ZHANG, S. Approximation Methods for Polynomial Optimization, Springer, 2012.

MATHEWS, J.H. and FINK, K.K. Numerical Methods Using MATLAB®. Pearson Prentice-Hall, 2004.

Downloads

Published

15-10-2013

Issue

Section

Technical Information

How to Cite

Zaplatílek, K., & Leuchter, J. (2013). Optimal Polynomial Approximation of Photovoltaic Panel Characteristics Using a Stochastic Approach. Advances in Military Technology, 8(2). https://doi.org/10.3849/aimt.01005