Home Technology Dynamic Model and Control of a Photovoltaic Generation System using Energetic Macroscopic Representation
Article
Licensed
Unlicensed Requires Authentication

Dynamic Model and Control of a Photovoltaic Generation System using Energetic Macroscopic Representation

  • Javier Solano EMAIL logo , José Duarte , Erwin Vargas , Jhon Cabrera , Andrés Jácome , Mónica Botero and Juan Rey
Published/Copyright: September 20, 2016

Abstract

This paper addresses the Energetic Macroscopic Representation EMR, the modelling and the control of photovoltaic panel PVP generation systems for simulation purposes. The model of the PVP considers the variations on irradiance and temperature. A maximum power point tracking MPPT algorithm is considered to control the power converter. A novel EMR is proposed to consider the dynamic model of the PVP with variations in the irradiance and the temperature. The EMR is evaluated through simulations of a PVP generation system.

Appendix: EMR pictograms

Energy source (ex. battery)
Tunable energy source (ex. PVP)
Mono-physicalconverter (ex. gearbox)
Action – Reaction variables
Energy distribution (same domain)
Energyaccumulation (ex. inertia)
Multi-physicalconverter (ex. pump)
Sensor
Closed loop control
Open loop control
Coupling inversion with distribution criteria
Energy management

References

1. Solano J, Duarte JL, Vargas EA, Cabrera JA, Jácome AM, Botero M, et al. Dynamic model and control of a photovoltaic generation system using energetic macroscopic representation. In 4th International Conference on Renewable Energy: Generation and Applications, Book of Abstract. ICREGA 2016.10.1515/ijeeps-2016-0078Search in Google Scholar

2. Anurag A, Bal S, Sourav S. A comparative study of mathematical modeling of photovoltaic array. Int J Emerging Electr Power Syst 2014;15(4):313–26. doi:10.1515/ijeeps-2013-0115.Search in Google Scholar

3. Chitti Babu B, Gurjar S, Meher A. Analysis of photovoltaic (PV) module during partial shading based on simplified two-diode model. Int J Emerging Electr Power Syst 2015;16(1):15–21. doi:10.1515/ijeeps-2014-0164.Search in Google Scholar

4. Khezzar R, Zereg M, Khezzar A. Modeling improvement of the four parameter model for photovoltaic modules. Solar Energy 2014;110:452–62. doi:10.1016/j.solener.2014.09.039.Search in Google Scholar

5. Villalva MG, Gazoli JR, Filho ER. Comprehensive approach to modeling and simulation of photovoltaic arrays. IEEE Trans Power Electron 2009;24(5):1198–208. doi:10.1109/TPEL.2009.2013862.Search in Google Scholar

6. Ishaque K, Salam Z, Taheri H. Accurate MATLAB simulink PV system simulator based on a two-diode model. J Power Electron 2011;11(2):179–87. doi:10.6113/JPE.2011.11.2.179.Search in Google Scholar

7. Wu Y-K, Chen C-S, Huang Yi-S, Lee C-Y. Advanced analysis of clustered photovoltaic system’s performance based on the battery-integrated voltage control algorithm. Int J Emerging Electr Power Syst 2009;10(4). doi:10.2202/1553-779X.2201.Search in Google Scholar

8. Bounechba H, Bouzid A, Snani H, Lashab A. Real time simulation of MPPT algorithms for PV energy system. Int J Electr Power Energy Syst Dec 2016;83:67–78. doi:10.1016/j.ijepes.2016.03.041.Search in Google Scholar

9. Bhende CN, Malla SG. Novel control of photovoltaic based water pumping system without energy storage. Int J Emerging Electr Power Syst 2012;13(5). doi:10.1515/1553-779X.2931.Search in Google Scholar

10. Dash PP, Yazdani A. A mathematical model and performance evaluation for a single-stage grid-connected photovoltaic (PV) system. Int J Emerging Electr Power Syst 2008;9(6). doi:10.2202/1553-779X.2033.Search in Google Scholar

11. Kolluru VR, Mahapatra K, Subudhi B. Real-time digital simulation and analysis of sliding mode and P&O mppt algorithms for a PV system. Int J Emerging Electr Power Syst 2015;16(4):313–22. doi:10.1515/ijeeps-2015-0010.Search in Google Scholar

12. Muthusamy MV, Ramadoss RR. Hardware implementation and steady-state analysis of ZVS-PWM cuk converter based MPPT for solar PV module. Int J Emerging Electr Power Syst 2012;13(4). doi:10.1515/1553-779X.2920.Search in Google Scholar

13. Lyden S, Haque ME. Maximum power point tracking techniques for photovoltaic systems: a comprehensive review and comparative analysis. Renewable Sustainable Energy Rev Dec 2015;52:1504–18. doi:10.1016/j.rser.2015.07.172.Search in Google Scholar

14. Verma D, Nema S, Shandilya AM, Dash SK. Maximum power point tracking (MPPT) techniques: recapitulation in solar photovoltaic systems. Renewable Sustainable Energy Rev Feb 2016;54:1018–34. doi:10.1016/j.rser.2015.10.068.Search in Google Scholar

15. Li J, Wang H. A novel stand-alone PV generation system based on variable step size INC MPPT and SVPWM control. In 2009 IEEE 6th International Power Electronics and Motion Control Conference, 2155–60, 2009. IEEE. doi:10.1109/IPEMC.2009.5157758.Search in Google Scholar

16. Safari A, Mekhilef S. Simulation and hardware implementation of incremental conductance MPPT with direct control method using cuk converter. IEEE Trans Ind Electron 2011;58(4):1154–61. doi:10.1109/TIE.2010.2048834.Search in Google Scholar

17. Hua C-C, Fang Yi-H, Chen W-T. Hybrid maximum power point tracking method with variable step size for photovoltaic systems. IET Renewable Power Gener 2016;10(2):127–32. doi:10.1049/iet-rpg.2014.0403.Search in Google Scholar

18. Lopez-Lapeña O, Penella MT. Low-power FOCV MPPT controller with automatic adjustment of the sample&hold. Electron Lett 2012;48(20):1301. doi:10.1049/el.2012.1345.Search in Google Scholar

19. Messalti S, Harrag AG, Loukriz AE. A new neural networks MPPT controller for PV systems. In IREC2015 The Sixth International Renewable Energy Congress, 1–6, 2015. IEEE. doi:10.1109/IREC.2015.7110907.Search in Google Scholar

20. Abbes H, Loukil K, Abid H, Abid M, Toumi A. Implementation of a maximum power point tracking fuzzy controller on FPGA circuit for a photovoltaic system. In 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA), 6, pp. 386–91, 2015. IEEE. doi:10.1109/ISDA.2015.7489260.Search in Google Scholar

21. Hilloowala RM, Sharaf AM. A rule-based fuzzy logic controller for a PWM inverter in photo-voltaic energy conversion scheme. In Conference Record of the 1992 IEEE Industry Applications Society Annual Meeting, 762–69, 1992. IEEE. doi:10.1109/IAS.1992.244319.Search in Google Scholar

22. Sreekumar S, Benny A. Maximum power point tracking of photovoltaic system using fuzzy logic controller based boost converter. In 2013 International Conference on Current Trends in Engineering and Technology (ICCTET), 275–80, 2013. IEEE. doi:10.1109/ICCTET.2013.6675965.Search in Google Scholar

23. Wang LL. MPPT control for photovoltaic system based on T-S fuzzy reasoning. In 2015 5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), 1976–80, 2015. IEEE. doi:10.1109/DRPT.2015.7432562.Search in Google Scholar

24. Hiyama T, Kouzuma S, Imakubo T. Identification of optimal operating point of PV modules using neural network for real time maximum power tracking control. IEEE Trans Energy Conver 1995;10(2):360–7. doi:10.1109/60.391904.Search in Google Scholar

25. Saravanan S, Ramesh Babu N. Maximum power point tracking algorithms for photovoltaic system – a review. Renewable Sustainable Energy Rev May 2016;57:192–204. doi:10.1016/j.rser.2015.12.105.Search in Google Scholar

26. Veerachary M, Senjyu T, Uezato K. Neural-network-based maximum-power-point tracking of coupled-inductor interleaved-boost-converter-supplied PV system using fuzzy controller. IEEE Trans Ind Electron 2003;50(4):749–58. doi:10.1109/TIE.2003.814762.Search in Google Scholar

27. Kamarzaman NA, Tan CW. A comprehensive review of maximum power point tracking algorithms for photovoltaic systems.. Renewable Sustainable Energy Rev. Sep 2014;37:585–98. doi:10.1016/j.rser.2014.05.045.Search in Google Scholar

28. Elgendy MA, Zahawi B, Atkinson DJ. Assessment of perturb and observe MPPT algorithm implementation techniques for PV pumping applications. IEEE Trans Sustainable Energy 2012;3(1):21–33. doi:10.1109/TSTE.2011.2168245.Search in Google Scholar

29. Femia N, Petrone G, Spagnuolo G, Vitelli M. A technique for improving P&O MPPT performances of double-stage grid-connected photovoltaic systems. IEEE Trans Ind Electron 2009;56(11):4473–82. doi:10.1109/TIE.2009.2029589.Search in Google Scholar

30. Abdelsalam AK, Massoud AM, Ahmed S, Enjeti PN. High-performance adaptive perturb and observe MPPT technique for photovoltaic-based microgrids. IEEE Trans Power Electron 2011;26(4):1010–21. doi:10.1109/TPEL.2011.2106221.Search in Google Scholar

31. Petrone G, Spagnuolo G, Vitelli M. A multivariable perturb-and-observe maximum power point tracking technique applied to a single-stage photovoltaic inverter. IEEE Trans Ind Electron 2011;58(1):76–84. doi:10.1109/TIE.2010.2044734.Search in Google Scholar

32. Kollimalla SK, Mishra MK. Variable perturbation size adaptive P&O MPPT algorithm for sudden changes in irradiance. IEEE Trans Sustainable Energy 2014;5(3):718–28. doi:10.1109/TSTE.2014.2300162.Search in Google Scholar

33. Lian KL, Jhang JH, Tian IS. A maximum power point tracking method based on perturb-and-observe combined with particle swarm optimization. IEEE J Photovoltaics 2014;4(2):626–33. doi:10.1109/JPHOTOV.2013.2297513.Search in Google Scholar

34. Sera D, Kerekes T, Teodorescu R, Blaabjerg F. Improved MPPT algorithms for rapidly changing environmental conditions. In 2006 12th International Power Electronics and Motion Control Conference, 1614–19, 2006. IEEE. doi:10.1109/EPEPEMC.2006.4778635.Search in Google Scholar

35. Agbli KS, Péra MC, Hissel D, Rallières O, Turpin C, Doumbia I. Multiphysics simulation of a PEM electrolyser: energetic macroscopic representation approach. Int J Hydrogen Energy 2011;36(2):1382–98. doi:10.1016/j.ijhydene.2010.10.069.Search in Google Scholar

36. Bouscayrol A, Delarue P, Guillaud X, Lhomme W, Lemaire-Semail B. Simulation of a wind energy conversion system using energetic macroscopic representation. In 2012 15th International Power Electronics and Motion Control Conference (EPE/PEMC), 0-7803-925:DS3e.8-1–DS3e.8-6, 2012. IEEE. doi:10.1109/EPEPEMC.2012.6397362.Search in Google Scholar

37. Bienaime D, Devillers N, Pera MC, Gustin F, Berthon A, Grojo ML. Energetic macroscopic representation as an efficient tool for energy management in a hybrid electrical system embedded in a helicopter. In 2012 Electrical Systems for Aircraft, Railway and Ship Propulsion, 1–6, 2012. IEEE. doi:10.1109/ESARS.2012.6387385.Search in Google Scholar

38. Lhomme W, Delarue P, Giraud F, Lemaire-Semail B, Bouscayrol A. Simulation of a photovoltaic conversion system using energetic macroscopic representation. In 2012 15th International Power Electronics and Motion Control Conference (EPE/PEMC), DS3e.7–1–DS3e.7–6, 2012. IEEE. doi:10.1109/EPEPEMC.2012.6397361.Search in Google Scholar

39. Cabrera JA, Jácome AM, Rey JM, Solano J. Modeling and parameter identification of photovoltaic modules. In 2015 VIII Simposio Internacional Sobre Calidad de La Energía Eléctrica (SICEL), 2015.Search in Google Scholar

40. Khaehintung N, Wiangtong T, Sirisuk P. FPGA implementation of MPPT using variable step-size P&O algorithm for PV applications. In 2006 International Symposium on Communications and Information Technologies, 212–15, 2006. IEEE. doi:10.1109/ISCIT.2006.340033.Search in Google Scholar

Published Online: 2016-9-20
Published in Print: 2016-10-1

©2016 by De Gruyter

Downloaded on 27.2.2026 from https://www.degruyterbrill.com/document/doi/10.1515/ijeeps-2016-0078/html
Scroll to top button