Advertisement

Arabian Journal for Science and Engineering

, Volume 42, Issue 12, pp 4971–4982 | Cite as

An Efficient Maximum Power Point Tracking Controller for Photovoltaic Systems Using Takagi–Sugeno Fuzzy Models

  • D. Ounnas
  • M. Ramdani
  • S. Chenikher
  • T. Bouktir
Research Article - Electrical Engineering
  • 282 Downloads

Abstract

This paper proposes a new Takagi–Sugeno (T–S) fuzzy model-based maximum power tracking controller to draw the maximum power from a solar photovoltaic (PV) system. A DC–DC boost converter is used to control the output power from the PV panel. Based on the T–S fuzzy model, the fuzzy maximum power point tracking controller is designed by constructing fuzzy gain state feedback controller and an optimal reference model for the optimal PV output voltage, which corresponds actually to maximum power point (MPP). A comparative study with the two base-line controllers of perturb and observe, and the incremental conductance shows that the proposed controller offers fast dynamic response, much less oscillation around MPP, and superior performance.

Keywords

Photovoltaic (PV) system Maximum power point tracking (MPPT) T–S fuzzy model Linear matrix inequalities (LMIs) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cossutta, P.; Aguirre, M.P.; Cao, A.; Raffo, S.; Valla, M.I.: Single-stage fuel cell to grid interface with multilevel current-source inverters. IEEE Trans. Ind. Electron. 62(8), 5256–5264 (2015)CrossRefGoogle Scholar
  2. 2.
    Singh, S.; Subhash, C.K.: Optimal sizing of grid integrated hybrid PV-biomass energy system using artificial bee colony algorithm. IET Renew. Power Gen. 10(5), 642–650 (2016)CrossRefGoogle Scholar
  3. 3.
    Meghni, B.; Dib, D.; Azar, A.T.; Saadoun, A.: Effective supervisory controller to extend optimal energy management in hybrid wind turbine under energy and reliability constraints. Int. J. Dynam. Control 1–15 (2017). doi:  10.1007/s40435-016-0296-0
  4. 4.
    Rahim, A.H.M.A.; Khan, M.H.: A swarm-based adaptive neural network SMES control for a permanent magnet wind generator. Arab. J. Sci. Eng. 39(11), 7957–7965 (2014)CrossRefGoogle Scholar
  5. 5.
    Vincheh, M.R.; Kargar, A.; Markadeh, G.A.: A hybrid control method for maximum power point tracking (MPPT) in Photovoltaic systems. Arab. J. Sci. Eng. 39(6), 4715–4725 (2014)CrossRefGoogle Scholar
  6. 6.
    Azri, M.; Rahim, N.A.; Elias, M.F.M.: Transformerless DC/AC converter for grid-connected PV power generation system. Arab. J. Sci. Eng. 39(11), 7945–7956 (2014)CrossRefGoogle Scholar
  7. 7.
    Tipler, P.A.; Mosca, G.: Physics for Scientist and Engineers, 6th edn. W.H. Freeman, New York (2008)Google Scholar
  8. 8.
    Oladimeji, I.; Nor, Z. Y.; Nordin, S.: Matlab/Simulink model of solar PV array with perturb and observe MPPT for maximising PV array efficiency. In: Conference on Energy Conversion (CENCON). pp. 254–258 (2015)Google Scholar
  9. 9.
    Abdelsalam, A.K.; Massoud, A.M.; Ahmed, S.; Enjeti, P.N.: High performance adaptive perturb and observe MPPT technique for photovoltaic-based microgrids. IEEE Trans. Power Electron. 26(4), 1010–1021 (2011)CrossRefGoogle Scholar
  10. 10.
    Manoj, P.; Amruta, D.: Design and simulation of Perturb and Observe Maximum Power Point Tracking using MATLAB/Simulink. In: International Conference on Industrial Instrumentation and Control (ICIC). pp. 1345–1349 (2015)Google Scholar
  11. 11.
    Safari, A.; Mekhilef, S.: Simulation and hardware implementation of incremental conductance MPPT with direct control method using Cuk converter. IEEE Trans. Ind. Electron. 58(4), 1154–1161 (2011)CrossRefGoogle Scholar
  12. 12.
    Worku, M. Y.; Abido, M. A.: Real-time implementation of grid-connected PV system with decoupled P–Q controllers. In: Conference on Control and Automation (MED). pp. 841–846 (2014)Google Scholar
  13. 13.
    Kjaer, S.B.: Evaluation of the hill climbing and the incremental conductance maximum power point trackers for photovoltaic power systems. IEEE Trans. Energy Conver. 27(4), 922–929 (2012)CrossRefGoogle Scholar
  14. 14.
    Mohammad, I.B.; Pouya, T.; Yousef, M.N.; Ali, K.K.; Paimaneh, S.: Modeling and simulation of hill climbing MPPT algorithm for photovoltaic application. In: Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), Conference on. pp. 1041–1044 (2016)Google Scholar
  15. 15.
    Adel, A.E.; Hamdi, A.; Montaser, A.S.: Implementation of a modified perturb and observe maximum power point tracking algorithm for photovoltaic system using an embedded microcontroller. IET Renew. Power Gen. 10(4), 551–560 (2016)CrossRefGoogle Scholar
  16. 16.
    Sera, D.; Mathe, L.; Kerekes, T.; Spataru, S.V.; Teodorescu, R.: On the perturb-and-observe and incremental conductance MPPT methods for PV systems. IEEE J. Photovolt. 3(3), 1070–1078 (2013)CrossRefGoogle Scholar
  17. 17.
    Masoum, M.A.S.; Dehbonei, H.; Fuchs, E.F.: Theoretical and experimental analysis of photovoltaic systems with voltage and current-based maximum power-point tracking. IEEE Trans. Energy Conver. 17(4), 514–522 (2002)CrossRefGoogle Scholar
  18. 18.
    Mellit, A.; Kalogirou, Sa: Artificial intelligence techniques for photovoltaic applications: a review. Prog. Energy Combust. Sci. 34(5), 574632 (2008)CrossRefGoogle Scholar
  19. 19.
    Belkaid, A.; Colak, I; Kayisli, K.: Implementation of a modified P&O-MPPT algorithm adapted for varying solar radiation conditions. Electr. Eng. pp. 1–8 (2016)Google Scholar
  20. 20.
    Ishaque, K.; Salam, Z.; Amjad, M.; Mekhilef, S.: An improved particle swarm optimization (PSO) based MPPT for PV with reduced steady-state oscillation. IEEE Trans. Power Electron. 27(8), 3627–3638 (2012)CrossRefGoogle Scholar
  21. 21.
    Zainuri, M.A.A.M.; Radzi, M.A.M.; Che Soh, A.; Abd Rahim, N.: Development of adaptive perturb and observe-fuzzy control maximum power point tracking for photovoltaic boost DC–DC converter. IET Renew. Power Gen. 8(2), 183–194 (2014)Google Scholar
  22. 22.
    Ahmed, A.; Ali, N.H.; Tshilidzi, M.: Perturb and observe based on fuzzy logic controller maximum power point tracking (MPPT). In: 2014 International Conference on Renewable Energy Research and Application (ICRERA). pp. 406–411 (2014)Google Scholar
  23. 23.
    Radak, B.; Chitralekha, M.; Anup, K.G.: MPPT of solar photovoltaic cell using perturb & observe and fuzzy logic controller algorithm for buck-boost DC–DC converter. In: 2015 International Conference on Energy, Power and Environment, pp. 1–5 (2015)Google Scholar
  24. 24.
    Liu, C.-L.; Chen, J.-H.; Liu, Y.-H.; Yang, Z.-Z.: An asymmetrical fuzzy-Logic-control-based MPPT algorithm for photovoltaic systems. Energies 7(4), 2178–2193 (2014)CrossRefGoogle Scholar
  25. 25.
    Vincheh, M.R.; Kargar, A.; Markadeh, G.A.: A hybrid control method for maximum power point tracking (MPPT) in photovoltaic systems. Arab. J. Sci. Eng. 39(6), 4715–4725 (2014)CrossRefGoogle Scholar
  26. 26.
    Abu-Rub, H.; Iqbal, A.; Moin Ahmed, S.; Peng, F.Z.; Li, Y.; Baoming, G.: Quasi-Z-source inverter-based photovoltaic generation system with maximum power tracking control using ANFIS. IEEE Trans. Sustain. Energy 4(1), 11–20 (2013)CrossRefGoogle Scholar
  27. 27.
    Afghoul, H.; Krim, F.; Chikouche, D.: Increase the photovoltaic conversion efficiency using neuro-fuzzy control applied to MPPT. In: Renewable and Sustainable Energy Conference (IRSEC). pp. 348–353 (2013)Google Scholar
  28. 28.
    Abido, M.A.; Khalid, M.S.; Worku, M.Y.: An efficient ANFIS-based PI controller for maximum power point tracking of PV systems. Arab. J. Sci. Eng. 40(9), 2641–2651 (2015)CrossRefGoogle Scholar
  29. 29.
    Dragan, M.; Srete, N.: ANFIS as a method for determinating MPPT in the photovoltaic system simulated in MATLAB/Simulink. In: Information and Communication Technology, Electronics and Microelectronics, pp. 1082–1086 (2015)Google Scholar
  30. 30.
    Elkhatib, K.; Abdel, A.: Design of maximum power fuzzy controller for PV systems based on the LMI-based stability. Intelligent Systems in Technical and Medical Diagnostics, Volume 230 of the series Advances in Intelligent Systems and Computing. pp. 77–88 (2012)Google Scholar
  31. 31.
    Abid, H.; Toumi, A.; Chaabane, M.: MPPT algorithm for photovoltaic panel based on augmented TakagiSugeno fuzzy model. Hindawi Publishing Corporation ISRN Renewable Energy, Article ID 253146 (2014)Google Scholar
  32. 32.
    Abid, H.; Toumi, A.; Chaabane, M.: TS fuzzy algorithm for photovoltaic panel. Int. J. Fuzzy Syst. 17(2), 215–223 (2015)CrossRefMathSciNetGoogle Scholar
  33. 33.
    Zhang, S.; Wang, T.; Li C., Zhang, J.; Wang, Y.: Maximum power point tracking control of solar power generation systems based on type-2 fuzzy logic. In: International World Congress on Intelligent Control and Automation, Guilin, pp. 12–15 (2016)Google Scholar
  34. 34.
    Zayani, H.; Allouche, M.; Kharrat M.; Chaabane M.: Maximum power point tracking control of solar power generation systems based on type-2 fuzzy logic. In: International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, Monastir, Tunisia, December, pp. 21–23 (2016)Google Scholar
  35. 35.
    Tanaka, K.; Wang, H.O.: Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach. Wiley, New York (2001)CrossRefGoogle Scholar
  36. 36.
    Gahinet, P.; Nemirovski, A.; Laub, A.J.; Chilali, M.: LMI Control Toolbox. MathWorks, Natick (1995)Google Scholar
  37. 37.
    Villalva, M.G.; Gazoli, J.R.; Filho, E.R.: Comprehensive approach to modelling and simulation of photovoltaic array. IEEE Trans. Power Electron. 25(5), 1198–1208 (2009)CrossRefGoogle Scholar
  38. 38.
    Ballouti, A.; Djahli, F.; Bendjadou, A.: MPPT system for photovoltaic module connected to battery adapted for unstable atmospheric conditions using VHDL-AMS. Arab. J. Sci. Eng. 36(3), 2021–2031 (2014)CrossRefGoogle Scholar
  39. 39.
    Ohtake, H.; Tanaka, K.; Wang, H.: Fuzzy modeling via sector nonlinearity concept. Integr. Comput. Aided Eng. 10(4), 333–341 (2003)Google Scholar
  40. 40.
    Lian, K.Y.; Liou, J.: Output tracking control for fuzzy systems via output feedback design. IEEE Trans. Fuzzy Sys. 14(5), 381–392 (2006)Google Scholar
  41. 41.
    Ounnas, D.; Ramdani, M.; Chenikher, S.; Bouktir, T.: Optimal reference model based fuzzy tracking control for wind energy conversion system. Int. J. Renew. Energy Res. 6(3), 1129–1136 (2016)Google Scholar
  42. 42.
    Bayod-Rjula, -A.; Cebollero-Abin, J.-A.: A novel MPPT method for PV systems with irradiance measurement. Sol. Energy 109, 95–104 (2014)CrossRefGoogle Scholar
  43. 43.
    Atiqah, H.b M.N.; Ahmad, M.b O.; Hedzlin, b Z.: Modeling and simulation of grid inverter in grid-connected photovoltaic system. Int. J. Renew. Energy Res. 4(4), 949–957 (2014)Google Scholar
  44. 44.
    Ounnas, D.; Ramdani, M.; Chenikher, S.; Bouktir, T.: A combined methodology of \(H_\infty \) fuzzy tracking control and virtual reference model for a PMSM. Adv. Electr. Electron. Eng. 13(3), 212–222 (2015)Google Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2017

Authors and Affiliations

  1. 1.Department of Electrotechnic, Faculty of TechnologyUniversity of Setif 1MaaboudaAlgeria
  2. 2.LASA Laboratory, Department of Electronics, Faculty of EngineeringUniversity Badji-Mokhtar of AnnabaAnnabaAlgeria
  3. 3.LABGET Laboratory, Department of Electrical Engineering, Faculty of Science and TechnologyUniversity of TebessaTebessaAlgeria

Personalised recommendations