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Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS): application for a photovoltaic system under unstable environmental conditions

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Abstract

In this paper, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are used as maximum power point tracking controllers to improve the performance of a stand-alone photovoltaic system. Based on the FL-M-160W PV module specifications, the PV panel and the boost converter were modeled in MATLAB/Simulink environment. Using a set of data collected during the experimental phase, the developed ANN and ANFIS-MPPT controllers have being learn, test and validate offline then inserted into the PV system. These controllers deliver at output an optimal voltage which will be compared to the reference voltage supplied by the photovoltaic generator and the error obtained will be used to adjust the duty cycle of the converter boost located between the PV panel and the load. It is shown after simulations that ANN and ANFIS-MPPT controllers are more robust and can follow the maximum power point with very low recovery time and low oscillations around the operating point in both in stable and changing atmospheric conditions.

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References

  1. Lashab, A., Bouzid, A., Snani, H.: Comparative study of three MPPT algorithms for a photovoltaic system control. In: 2015 World Congress on Information Technology and Computer Applications (WCITCA) (2015)

  2. Jiyong, L., Honghua, W.: Maximum power point tracking of photovoltaic generation based on the fuzzy control method. IEEE Supergen 9, 1–6 (2009)

    Google Scholar 

  3. Singh, G.: Solar power generation by PV (photovoltaic) technology: a review. Energy 53, 1–13 (2013)

    Article  Google Scholar 

  4. Koutroulis, E., Kalaitzakis, K., Voulgaris, N.C.: Development of a microcontroller-based photovoltaic maximum power point tracking control system. IEEE Trans. Power Electron. 16(1), 46–54 (2001)

    Article  Google Scholar 

  5. Femia, N., Petrone, G., Spagnuolo, G., Vitelli, M.: Optimization of perturb and observe maximum power point tracking method. IEEE Trans. Power Electron. 20(4), 963–973 (2005)

    Article  Google Scholar 

  6. Ait-Cheikh, M.S., Larbes, C., Tchoketch, G.F., Zerguerras, A.: Maximum power point tracking using fuzzy logic controller scheme. J. Renew. Energy 10, 387–395 (2007)

    Google Scholar 

  7. Nzoundja, F.C.B., Kamta, M., Wira, P.: A comprehensive assessment of MPPT algorithms to optimal power extraction of a PV panel. J. Sol. Energy Res. 4(3), 172–179 (2019)

    Google Scholar 

  8. Liu, F., Kang, Y., Zhang, Y., Duan, S.: Comparison of P&O and hill climbing MPPT methods for grid-connected PV converter. In: Proceedings of the 3rd IEEE Conference on Industrial Electronics and Applications, 2008 (ICIEA 2008), pp. 804–807 (2008)

  9. Salman, S., Ai, X., Wu, Z.: Design of a P-&-O algorithm based MPPT charge controller for a standalone 200W PV system. Prot. Control Mod. Power Syst. 3(25), 1–8 (2018)

    Google Scholar 

  10. 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. 56(11), 4473–4482 (2009)

    Article  Google Scholar 

  11. Belkaid, A., Colak, I., Isik, O.: Photovoltaic maximum power point tracking under fast varying of solar radiation. Appl. Energy 179, 523–530 (2016)

    Article  Google Scholar 

  12. Ankaiah, B., Nageswararao, J.: MPPT algorithm for solar photovotaic cell by incremental conductance method. Int. J. Innov. Eng. Technol. 2(1), 17–23 (2013)

    Google Scholar 

  13. Bebis, G., Georgiopoulos, M.: Feed-forward neural networks: why network size is so important. IEEE Potentials 13, 27–31 (1994)

    Article  Google Scholar 

  14. Boitier, V., Maussion, P., Cabal, C.: Recherche du maximum de puissance sur les générateurs photovoltaïques. Revue 3EI 54, 90–96 (2008)

    Google Scholar 

  15. Farhat, S., Alaoui, R., Kahaji, A., Bouhouch, L.: Estimating the photovoltaic MPPT by artificial neural network. In: International Renewable and Sustainable Energy Conference (IRSEC) (2013)

  16. James, E.A., Jasmin, J.: Implementation of fuzzy logic based maximum power point tracking in photovoltaic system. In: Proceedings of the International Conference on Control, Communication and Power Engineering, CCPE, pp. 546–556. Elsevier (2014)

  17. Harrag, A., Messalti, S.: Variable step size modified P&O MPPT algorithm using GA-based brid offline/online PID controller. Renew Sustain Energy Rev. 49, 1247–1260 (2015)

    Article  Google Scholar 

  18. Tse, K.K., Ho, M.T., Chung, H.S., Hui, S.Y.R.: A comparative study of maximum-power-point trackers for photovoltaic panels using switching-frequency modulation scheme. IEEE Trans. Ind. Electron. 51(2), 410–418 (2004)

    Article  Google Scholar 

  19. Yong, Z., Hong, L., Liqun, L., Xiao, G.: The MPPT control method by using BP neural networks in PV generating system. In: Proceedings of the 2012 International Conference on Industrial Control and Electronics Engineering (2012)

  20. Xiao, G.W., Dunford, W.: A modified adaptive hill climbing MPPT method for photovoltaic power systems. In: Proceedings of the 35th Annual IEEE Power Electronics Specialists Conference, pp. 1957–1963 (2004)

  21. Messalti, S., Harrag, A., Loukriz, A.: A new variable step size neural networks MPPT controller: review. Renew Sustain Energy Rev. 68, 221–233 (2017)

    Article  Google Scholar 

  22. Hussein, K.H., Muta, I., Hoshino, T., Osakada, M.: Suivi de la puissance photovoltaïque maximale: un algorithme pour des conditions atmosphériques en évolution rapide. IEE Proc Gener Transm Distrib. 142, 59–64 (1995)

    Article  Google Scholar 

  23. Reisi, A.R., Moradi, M.H., Jamas, B.S.: Classification and comparison of maximum power point tracking techniques for photovoltaic system: a review. Renew. Sustain Energy Rev 19, 433–443 (2013)

    Article  Google Scholar 

  24. Mokhtar, M., Marie, M.: Engineering Applications of Matlab 5.3 and Simulink3, p. 538. Springer, London (2000)

    Book  Google Scholar 

  25. Krishna, S., Padiyar, K.R.: Transient stability assessment using artificial neural networks. IEEE Trans. Power Syst. 1, 627–632 (2000)

    Google Scholar 

  26. Rai, A.K., Kaushika, N.D., Singh, B., Agarwal, N.: Simulation model of ANN based maximum power point tracking controller for solar PV system. Sol. Energy Mater. Sol. Cells 95, 773–778 (2011)

    Article  Google Scholar 

  27. Al-Majidi, S.D., Abbod, M.F., Al-Raweshidy, H.S.: Design of an efficient maximum power point tracker based on ANFIS using an experimental photovoltaic system data. Electron. MDPI 8(858), 20 (2019)

    Google Scholar 

  28. Kulaksiz, A.A.: ANFIS-based estimation of PV module equivalent parameters: application to a stand-alone PV system with MPPT controller. Turk. J. Electr. Eng. Comput. Sci. 21, 2127–2140 (2013)

    Article  Google Scholar 

  29. Roger, J.-S.J.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)

    Article  Google Scholar 

  30. Zhang, L., Xiong, G., Zou, H., Weizhong, G.: Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference. Expert Syst. Appl. 37, 6077–6085 (2010)

    Article  Google Scholar 

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Authors and Affiliations

  1. Laboratory of Engineering for Industrial Systems and Environment, Faculty of Sciences/Department of Physics/Mechanics and Energetics, University of Dschang, BP 405, Dschang, Cameroon

    Pascal Kuate Nkounhawa & Dieunedort Ndapeu

  2. Laboratory of Engineering for Physical Systems Mechanics and Modeling, Faculty of Sciences/Department of Physics/Mechanics and Energetics, University of Dschang, BP 405, Dschang, Cameroon

    Pascal Kuate Nkounhawa & Dieunedort Ndapeu

  3. Laboratory of Mechanics and Civil Engineering, National Advanced School of Engineering of Yaoundé (ENSPY/UY1), University of Yaoundé 1, P.O. Box: 8390, Yaoundé, Cameroon

    Bienvenu Kenmeugne

Authors
  1. Pascal Kuate Nkounhawa
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  2. Dieunedort Ndapeu
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  3. Bienvenu Kenmeugne
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Correspondence to Pascal Kuate Nkounhawa.

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Kuate Nkounhawa, P., Ndapeu, D. & Kenmeugne, B. Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS): application for a photovoltaic system under unstable environmental conditions. Int J Energy Environ Eng 13, 821–829 (2022). https://doi.org/10.1007/s40095-022-00472-x

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  • DOI: https://doi.org/10.1007/s40095-022-00472-x

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