THERMAL SCIENCE

International Scientific Journal

Thermal Science - Online First

online first only

Modeling of photovoltaic modules using a gray-box neural network approach

ABSTRACT
This paper proposes a gray-box approach to modeling and simulation of photovoltaic modules. The process of building a gray-box model is split into two components (known, and unknown or partially unknown). The former is based on physical principles while the latter relies on functional approximator and data-based modeling. In this paper, artificial neural networks were used to construct the functional approximator. Compared to the standard mathematical model of photovoltaic module which involves the three input variables - solar irradiance, ambient temperature and wind speed, a gray-box model allows the use of additional input environmental variables, such as wind direction, atmospheric pressure, and humidity. In order to improve the accuracy of the gray-box model, we have proposed two criteria for the classification of the daily input/output data whereby the former determines the season while the latter classifies days into sunny and cloudy. The accuracy of this model is verified on the real-life photovoltaic generator, by comparing with single-diode mathematical model and artificial neural networks model towards measured output power data.
KEYWORDS
PAPER SUBMITTED: 2016-03-22
PAPER REVISED: 2016-12-19
PAPER ACCEPTED: 2017-02-07
PUBLISHED ONLINE: 2017-03-03
DOI REFERENCE: https://doi.org/10.2298/TSCI160322023R
REFERENCES
  1. Chenni, R., et al., A Detailed Modeling Method for Photovoltaic Cells, Energy, 32 (2007), 9, pp. 1724-1730
  2. Karatepe, E., et al., Neural Network Based Solar Cell Model, Energy Conversion and Management, 47 (2006), 9-10, pp. 1159-2871
  3. Castañer, L., Silvestre, S., Modelling Photovoltaic Systems Using PSpice, John Wiley & Sons, 1st ed., Chichester, UK, 2002
  4. Nishioka, K., et al., Analysis of Multicrystalline Silicon Solar Cells by Modified 3-diode Equivalent Circuit Model Taking Leakage Current Through Periphery into Consideration, Solar Energy Materials and Solar Cells, 91 (2007), 13, pp. 1222-1227
  5. Celik, A. N., Acikgoz, N., Modelling and Experimental Verification of the Operating Current of Mono-Crystalline Photovoltaic Modules Using Four- and Five-parameter models, Applied Energy, 84 (2007), 1, pp. 1-15
  6. Dolara, A., et al., Comparison of Different Physical Models for PV Power Output Prediction, Solar Energy, 119 (2015), 1, pp. 83-99
  7. Mekhilefa, S., et al., Effect of Dust, Humidity and Air Velocity on Efficiency of Photovoltaic Cells, Renewable and Sustainable Energy Reviews, 16 (2012), 5, pp. 2920-2925
  8. Ali, H. M., et al., Effect of Dust Deposition on the Performance of Photovoltaic Modules in Taxila, Pakistan, Thermal Science, Online-First issue (2015), 1, pp. 46-46
  9. Omubo-Pepple, V. B., Israel-Cookey, C., Alaminokuma, G. I., Effects of Temperature, Solar Flux and Relative Humidity on the Efficient Conversion of Solar Energy to Electricity, European Journal of Scientific Research, 35 (2009), 2, pp. 173-180
  10. Mellit, A., et al., Artificial Neural Network-Based Model for Estimating the Produced Power of a Photovoltaic Module, Renewable Energy, 60 (2013), 1, pp. 71-78
  11. Almonacid, F., et al., Characterisation of PV CIS Module by Artificial Neural Networks, Renewable Energy, 35 (2010), 5, pp. 973-980
  12. Chen, C., et al., Online 24-h Solar Power Forecasting Based on Weather Type Classification Using Artificial Neural Network, Solar Energy, 85 (2011), 11, pp. 2856-2871
  13. Chen, S. X., et al., Solar Radiation Forecast Based on Fuzzy Logic and Neural Networks, Renewable Energy, 60 (2013), 1, pp. 195-201
  14. Ji, W., Chee, K. C., Prediction of Hourly Solar Radiation Using a Novel Hybrid Model of ARMA and TDNN, Solar Energy, 85 (2011), 5, pp. 808-817
  15. Huang, C., et al., Improvement in Artificial Neural Network-Based Estimation of Grid Connected Photovoltaic Power Output, Renewable Energy, 97 (2016), 1, pp. 838-848
  16. Aybar-Ruiz, A., et al., A Novel Grouping Genetic Algorithm-Extreme Learning Machine Approach for Global Solar Radiation Prediction from Numerical Weather Models Inputs, Solar Energy, 132 (2016), 1, pp. 129-142
  17. Al-Messabi, N., et al., Grey-box Identification for Photovoltaic Power Systems via Particle Swarm Algorithm, 2015 21st International Conference on Automation and Computing (ICAC), Glasgow, United Kingdom, 2015, Vol. 1, pp. 1-7
  18. Al-Messabi, N., et al., Heuristic Grey-Box Modelling for Photovoltaic Power Systems, Systems Science & Control Engineering, 4 (2016), 1, pp. 235-246
  19. Tan, K. C., Li, Y., Grey-Box Model Identification via Evolutionary Computing, Control Engineering Practice, 10 (2002), 7, pp. 673-684
  20. Villalva, M. G., et al., Comprehensive Approach to Modeling and Simulation of Photovoltaic Arrays, IEEE Transactions on Power Electronics, 24 (2009), 5, pp. 1198-1208
  21. Ranković, A., Sarić, A. T., Modeling of Photovoltaic and Wind Turbine Based Distributed Generation in State Estimation, Proceedings of 15th International Power Electronics and Motion Control Conference and Exposition - EPE-PEMC 2012, Novi Sad, Serbia, 2012, Vol. 1, pp. 1-6
  22. Qi, C., Ming, Z., Photovoltaic Module Simulink Model for a Stand-alone PV System, 2012 International Conference on Applied Physics and Industrial Engineering (Physics Procedia), Amsterdam, Netherlands, 2012, Vol. 24, pp. 94-100
  23. Di Piazza, M. C., et al., Identification of Photovoltaic Array Model Parameters by Robust Linear Regression Methods, International Conference on Renewable Energies and Power Quality (ICREPQ'09), Valencia, Spain, 2009, Vol. 1, pp. 1-7
  24. Reindl, D. T., et al., Diffuse Fraction Correlations, Solar Energy, 45 (1990), 1, pp. 1-7
  25. Myers, D., Solar Radiation: Practical Modeling for Renewable Energy Applications, CRC Press/Taylor & Francis Group, 1st ed., Boca Ration, USA, 2013
  26. Quasching, V., Understanding Renewable Energy Systems, Earthscan Publications Ltd., 2nd ed., London, UK, 2016
  27. Klucher, T. M., Evaluation of Models to Predict Insolation on Tilted Surfaces, Solar Energy, 23 (1979), 2, pp. 111-114
  28. Hagan, M. T., et al., Neural Network Design, Martin Hagan Pub., 2nd ed., 2014
  29. Badescu, V. (Ed.) Modeling Solar Radiation at the Earth's Surface, Springer, 1st ed., Germany, 2008
  30. Sharples, S., Charlesworth, P. S., Full-Scale Measurements of Wind-Induced Convective Heat Transfer From a Roof-Mounted Flat Plate Solar Collectors, Solar Energy, 62 (1998), 2, pp. 69-77