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Abstract

Mismatching effects due to partial shaded conditions are the major drawbacks existing in today’s photovoltaic (PV) systems. These mismatch effects are greatly reduced in distributed PV system architecture where each panel is effectively decoupled from its neighboring panel. To obtain the optimal operation of the PV panels, maximum power point tracking (MPPT) techniques are used. In partial shaded conditions, detecting the maximum operating point is difficult as the characteristic curves are complex with multiple peaks. In this paper, a neural network control technique is employed for MPPT. Detailed analyses were carried out on MPPT controllers in centralized and distributed architecture under partial shaded environments. The efficiency of the MPPT controllers and the effectiveness of the proposed control technique under partial shaded environments was examined using MATLAB software. The results were validated through experimentation.

 

Keywords

Partial shaded PV system Efficiency Artificial neural network Centralized controller Distributed controller.

Article Details

How to Cite
Ramaprabha, R., & Chitra, S. (2015). Comparative Analysis of Maximum Power Point Tracking Controllers under Partial Shaded Conditions in a Photovoltaic System. The Journal of Engineering Research [TJER], 12(1), 15–31. https://doi.org/10.24200/tjer.vol12iss1pp15-31
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References

  1. Aït Cheikh MS, Haddadi M, Zerguerras A (2007), Design of a neural network control scheme for the maximum power point tracking (MPPT). Revue des Energies Renouvelables 10(1):109– 118.
  2. Al-Amoudi A, Zhang L (2000), Application of radial basis function networks for solar-array modeling and maximum power-point prediction. IEEE Proceeding - Generation, Transmission and Distribution 147(5):310–316.
  3. De Medeiros Torres A, Antunes FLM, Dos Reis FS (1998), An artificial neural network-based real time maximum power tracking controller for connecting a PV System to the grid. Proceeding of IEEE the 24th Annual Conference on Industrial Electronics Society 1:554–558.
  4. Esram T, Chapman PL (2007), Comparison of photovoltaic array maximum power point tracking techniques. IEEE Transaction on Energy Conversion 22(2):439–449.
  5. Femia N, Petrone G, Spagnuolo G, Vitelli M (2005), Optimization of perturb and observe maximum power point tracking method. IEEE Trans.Power Electron 20(4):963–973.
  6. Herrmann W, Wiesner W, Waassen W (1997), Hot spots investigations on PV modules—new concepts for a test standard and consequences for module design with respect to by-pass diodes. Proceedings of the 26th IEEE Photovoltaic Specialists Conference 1129–1132.
  7. Hiyama T, Kitabayashi K (1997), Neural network based estimation of maximum power generation from PV module using environment information. IEEE Transaction on Energy Conversion 12(3):241–247.
  8. Jain S, Agarwal V (2004), A new algorithm for rapid tracking of approximate maximum power point in photovoltaic systems. IEEE Power Electron. Lett. 2(1):16–19.
  9. Jiang YC, Jaber A, Qahouq A, Orabi M (2012), AC PV solar system distributed architecture with maximum power point tracking. IEEE Telecommunication Energy Conference 1–5.
  10. Jordan D, Kurtz SR (2012), Photovoltaic degradation rates—An analytical review. NREL Report Number JA-5200-51664.
  11. Karatepe E, Boztepe M, Colak M (2007), Development of suitable model for characterizing photovoltaic arrays with shaded solar cells. Solar Energy 81(8):977–992.
  12. Kasper M, Bortis D, Kolar JW (2014), Classification and comparative evaluation of PV panel-integrated DC–DC converter concepts. IEEE Transactions on Power Electronics 29(5):2511–2526.
  13. Kazutaka I (2012), New I-V characteristics scantype MPPT control method for PV generation system. Journal of Technology Innovations in Renewable Energy 1:87–91.
  14. Koutroulis E, Kalaitzakis K, Voulgaris NC (2001), Development of a microcontroller-based, photovoltaic maximum power point tracking control system. IEEE Trans Power Electron 16(1):46–54.
  15. Masoum MAS, Dehbonei H, Fuchs EF (2002), Theoretical and experimental analyses of photovoltaic systems with voltage- and current-based maximum power-point tracking. IEEE Transactions on Energy Conversion 17(4):514–522.
  16. Patel H, Agarwal V (2008), Matlab-based modeling to study the effects of partial shading on PV array characteristics. IEEE Trans Energy Conversion 55(4):1689–1698.
  17. Ramaprabha R, Mathur BL (2008), Modeling and simulation of solar PV array under partial shaded conditions. Proc. of IEEE Int. Conf. on Sustainable Energy Technologies 12–16.
  18. Ramaprabha R, Mathur BL (2012), A comprehensive review and analysis of solar photovoltaic array configurations under partial shaded conditions. International Journal of Photo energy, Special Issue on Recent Developments in Solar Energy Harvesting and Photo Catalysis 1–16.
  19. Silvestre S, Boronat A, Chouder A (2009), Study of bypass diodes configuration on PV modules, Applied Energy 86(9):1632–1640.
  20. Villalva MG, Gazoli JR, Filho ER (2009), Comprehensive approach to modeling and simulation of photovoltaic arrays. IEEE Transactions on Power Electronics 24(5):1198– 1208.
  21. Walker GR, Pierce JC (2006), Photovoltaic DCDC module integrated converter for novel cascaded and bypass grid connection topologies design and optimization, IEEE Power Electronics Specialists Conference 1–7.
  22. Walker GR, Semia PC (2004), Cascaded DC-DC converter connection of photovoltaic modules. IEEE Transactions on Power Electronics 19(4):1130–1139.
  23. Walker GS (2001), Evaluating MPPT converter topologies using a MATLAB PV Model. Journal of Electrical & Electronics Engineering, Australia 21(1):49–56.