10.1007/s40095-022-00472-x

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

  • Pascal Kuate Nkounhawa*1,
  • Dieunedort Ndapeu1,
  • Bienvenu Kenmeugne2,
  1. Laboratory of Engineering for Industrial Systems and Environment, Faculty of Sciences/Department of Physics/Mechanics and Energetics, University of Dschang, Dschang, CM Laboratory of Engineering for Physical Systems Mechanics and Modeling, Faculty of Sciences/Department of Physics/Mechanics and Energetics, University of Dschang, Dschang, CM
  2. Laboratory of Mechanics and Civil Engineering, National Advanced School of Engineering of Yaoundé (ENSPY/UY1), University of Yaoundé 1, Yaoundé, CM

Published in Issue 2022-01-15

How to Cite

Kuate Nkounhawa, P., Ndapeu, D., & Kenmeugne, B. (2022). Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS): application for a photovoltaic system under unstable environmental conditions. International Journal of Energy and Environmental Engineering, 13(2 (June 2022). https://doi.org/10.1007/s40095-022-00472-x
Crossref
Scopus
Google Scholar
Europe PMC

Abstract

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.

Keywords

  • Photovoltaic system,
  • Modeling,
  • MPPT controller,
  • Artificial neural network (ANN),
  • Adaptive neuro-fuzzy inference system (ANFIS)

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 and Honghua (2009) Maximum power point tracking of photovoltaic generation based on the fuzzy control method (pp. 1-6)
  3. Singh (2013) Solar power generation by PV (photovoltaic) technology: a review (pp. 1-13) https://doi.org/10.1016/j.energy.2013.02.057
  4. Koutroulis et al. (2001) Development of a microcontroller-based photovoltaic maximum power point tracking control system 16(1) (pp. 46-54) https://doi.org/10.1109/63.903988
  5. Femia et al. (2005) Optimization of perturb and observe maximum power point tracking method 20(4) (pp. 963-973) https://doi.org/10.1109/TPEL.2005.850975
  6. Ait-Cheikh et al. (2007) Maximum power point tracking using fuzzy logic controller scheme (pp. 387-395)
  7. Nzoundja et al. (2019) A comprehensive assessment of MPPT algorithms to optimal power extraction of a PV panel 4(3) (pp. 172-179)
  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 et al. (2018) Design of a P-&-O algorithm based MPPT charge controller for a standalone 200W PV system 3(25) (pp. 1-8)
  10. Femia et al. (2009) A technique for improving P&O MPPT performances of double-stage grid-connected photovoltaic systems 56(11) (pp. 4473-4482) https://doi.org/10.1109/TIE.2009.2029589
  11. Belkaid et al. (2016) Photovoltaic maximum power point tracking under fast varying of solar radiation (pp. 523-530) https://doi.org/10.1016/j.apenergy.2016.07.034
  12. Ankaiah and Nageswararao (2013) MPPT algorithm for solar photovotaic cell by incremental conductance method 2(1) (pp. 17-23)
  13. Bebis and Georgiopoulos (1994) Feed-forward neural networks: why network size is so important (pp. 27-31) https://doi.org/10.1109/45.329294
  14. Boitier et al. (2008) Recherche du maximum de puissance sur les générateurs photovoltaïques (pp. 90-96)
  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 and Messalti (2015) Variable step size modified P&O MPPT algorithm using GA-based brid offline/online PID controller (pp. 1247-1260) https://doi.org/10.1016/j.rser.2015.05.003
  18. Tse et al. (2004) A comparative study of maximum-power-point trackers for photovoltaic panels using switching-frequency modulation scheme 51(2) (pp. 410-418) https://doi.org/10.1109/TIE.2004.825226
  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 et al. (2017) A new variable step size neural networks MPPT controller: review (pp. 221-233) https://doi.org/10.1016/j.rser.2016.09.131
  22. Hussein et al. (1995) Suivi de la puissance photovoltaïque maximale: un algorithme pour des conditions atmosphériques en évolution rapide (pp. 59-64) https://doi.org/10.1049/ip-gtd:19951577
  23. Reisi et al. (2013) Classification and comparison of maximum power point tracking techniques for photovoltaic system: a review (pp. 433-443) https://doi.org/10.1016/j.rser.2012.11.052
  24. Mokhtar and Marie (2000) Springer https://doi.org/10.1007/978-1-4471-0741-5
  25. Krishna and Padiyar (2000) Transient stability assessment using artificial neural networks (pp. 627-632)
  26. Rai et al. (2011) Simulation model of ANN based maximum power point tracking controller for solar PV system (pp. 773-778) https://doi.org/10.1016/j.solmat.2010.10.022
  27. Al-Majidi et al. (2019) Design of an efficient maximum power point tracker based on ANFIS using an experimental photovoltaic system data 8(858)
  28. Kulaksiz (2013) ANFIS-based estimation of PV module equivalent parameters: application to a stand-alone PV system with MPPT controller (pp. 2127-2140) https://doi.org/10.3906/elk-1201-41
  29. Roger (1993) ANFIS: adaptive-network-based fuzzy inference system 23(3) (pp. 665-685) https://doi.org/10.1109/21.256541
  30. Zhang et al. (2010) Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference (pp. 6077-6085) https://doi.org/10.1016/j.eswa.2010.02.118