MPPT Based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for a Photovoltaic System Under Unstable Environmental Conditions

Pascal Kuate Nkounhawa; Dieunedort Ndapeu; Bienvenu Kenmeugne

doi:doi:10.11648/j.ajee.20210903.12

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MPPT Based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for a Photovoltaic System Under Unstable Environmental Conditions

Pascal Kuate Nkounhawa, Dieunedort Ndapeu, Bienvenu Kenmeugne

Published in American Journal of Energy Engineering (Volume 9, Issue 3)

Received: 9 August 2021 Accepted: 25 August 2021 Published: 3 September 2021

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Abstract

Many algorithms have been used to track the MPP in a PV generator. Although these algorithms have proved their worth, the fact remains that they still have limits in terms of stability, response times and significant presence of oscillations, especially for sub-Saharan conditions where the climate variation is very sudden and has a considerable impact on the power delivered at the generator output. In this article, the objective is to develop a maximum power point tracking (MPPT) controller based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) to improve the performance of the Felicity Solar photovoltaic module FL-M-160W submitted to varying environmental conditions. The specifications of the FL-M-160W module are used to analyze and model the PV generator and boost converter located between the panel and the load in Matlab / Simulink. After the experimental tests, a database was set up to develop the neurofuzzy controller. The proposed ANFIS model was tested and validated under the Matlab / Simulink environment and then inserted into the PV system. The optimum voltage Vopt provided by this model is compared to the reference voltage Vpv provided by the PV generator and the error obtained is used to adjust the duty cycle of the DC-DC boost converter. After simulations, the results obtained reveal a good performance of the ANFIS controller compared to conventional P&O, InC and HC controllers in terms of stability, convergence speed, accuracy, robustness, and response time even under unstable environmental conditions with an efficiency of about 98%.

Published in	American Journal of Energy Engineering (Volume 9, Issue 3)
DOI	10.11648/j.ajee.20210903.12
Page(s)	68-84
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Photovoltaic System, Modeling, MPPT Controller, ANFIS, Converter, Unstable Environmental Conditions

References

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[3]	Hassan A. Yousef (1999), “Design and implementation of a fuzzy logic computer-controlled sun tracking system “, In the IEEE International Symposium on Industrial Electronics, IEEE, Bled, Slovenia, 3: 1030-1034.
[4]	M. Veerachary, T. Senjyu, K. Uezato (2012), “Neural Network based Maximum Power Point Tracking of Coupled-Inductor Interleaved-boost Converter Supplied PV System using Fuzzy Controller”, IEEE Transactions on Industrial Electronics, Vol. 50, pp. 749-758.
[5]	Z. Cheng, Z. Pang, Y. Liu and P. Xue (2010), "An adaptive solar Photovoltaic array reconfiguration methods based on Fuzzy control", in 2010 8th World Congress on Intelligent Control and Automation (WCICA), pp. 176-181.
[6]	W. Xiao, W. G. Dunford, and A. Capel (2004), “A novel modeling method for photovoltaic cells”, in Proc. IEEE 35th Annu. Power Electron. Spec. Conf. (PESC), vol. 3, pp. 1950–1956.
[7]	Syed Zulqadar Hassan, Hui Li, Tariq Kamal, Ugur Arifo glu, Sidra Mumtaz and Laiq Khan (2017), “Neuro-Fuzzy Wavelet Based Adaptive MPPT Algorithm for Photovoltaic Systems”, MDPI, Energies, 10, 394.
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[13]	Hussein KH, Muta I, Hoshino T, Osakada M. (1995), “Maximum photovoltaic power tracking: an algorithm for rapidly changing atmospheric conditions”, IEE Proc Gener, Trans Distrib; 142 (1): 59–64.
[14]	M. Razzazan, Z. Mirbagheri, and A. Ramezani (2017), “Maximum Power Point Tracking Using Constrained Model Predictive Control for Photovoltaic Systems”, Journal of Solar Energy Research (JSER), Spring 2 (2): p. 19-24.
[15]	A. Bin-Halabi, A. Abdennour and H. Mashaly (2014), ‘‘An accurate ANFIS-based MPPT for solar PV System”, Intl. J. Advanced Computer Research 4, 588–595.
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APA Style

Pascal Kuate Nkounhawa, Dieunedort Ndapeu, Bienvenu Kenmeugne. (2021). MPPT Based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for a Photovoltaic System Under Unstable Environmental Conditions. American Journal of Energy Engineering, 9(3), 68-84. https://doi.org/10.11648/j.ajee.20210903.12

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ACS Style

Pascal Kuate Nkounhawa; Dieunedort Ndapeu; Bienvenu Kenmeugne. MPPT Based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for a Photovoltaic System Under Unstable Environmental Conditions. Am. J. Energy Eng. 2021, 9(3), 68-84. doi: 10.11648/j.ajee.20210903.12

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AMA Style

Pascal Kuate Nkounhawa, Dieunedort Ndapeu, Bienvenu Kenmeugne. MPPT Based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for a Photovoltaic System Under Unstable Environmental Conditions. Am J Energy Eng. 2021;9(3):68-84. doi: 10.11648/j.ajee.20210903.12

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@article{10.11648/j.ajee.20210903.12,
  author = {Pascal Kuate Nkounhawa and Dieunedort Ndapeu and Bienvenu Kenmeugne},
  title = {MPPT Based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for a Photovoltaic System Under Unstable Environmental Conditions},
  journal = {American Journal of Energy Engineering},
  volume = {9},
  number = {3},
  pages = {68-84},
  doi = {10.11648/j.ajee.20210903.12},
  url = {https://doi.org/10.11648/j.ajee.20210903.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajee.20210903.12},
  abstract = {Many algorithms have been used to track the MPP in a PV generator. Although these algorithms have proved their worth, the fact remains that they still have limits in terms of stability, response times and significant presence of oscillations, especially for sub-Saharan conditions where the climate variation is very sudden and has a considerable impact on the power delivered at the generator output. In this article, the objective is to develop a maximum power point tracking (MPPT) controller based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) to improve the performance of the Felicity Solar photovoltaic module FL-M-160W submitted to varying environmental conditions. The specifications of the FL-M-160W module are used to analyze and model the PV generator and boost converter located between the panel and the load in Matlab / Simulink. After the experimental tests, a database was set up to develop the neurofuzzy controller. The proposed ANFIS model was tested and validated under the Matlab / Simulink environment and then inserted into the PV system. The optimum voltage Vopt provided by this model is compared to the reference voltage Vpv provided by the PV generator and the error obtained is used to adjust the duty cycle of the DC-DC boost converter. After simulations, the results obtained reveal a good performance of the ANFIS controller compared to conventional P&O, InC and HC controllers in terms of stability, convergence speed, accuracy, robustness, and response time even under unstable environmental conditions with an efficiency of about 98%.},
 year = {2021}
}

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TY  - JOUR
T1  - MPPT Based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for a Photovoltaic System Under Unstable Environmental Conditions
AU  - Pascal Kuate Nkounhawa
AU  - Dieunedort Ndapeu
AU  - Bienvenu Kenmeugne
Y1  - 2021/09/03
PY  - 2021
N1  - https://doi.org/10.11648/j.ajee.20210903.12
DO  - 10.11648/j.ajee.20210903.12
T2  - American Journal of Energy Engineering
JF  - American Journal of Energy Engineering
JO  - American Journal of Energy Engineering
SP  - 68
EP  - 84
PB  - Science Publishing Group
SN  - 2329-163X
UR  - https://doi.org/10.11648/j.ajee.20210903.12
AB  - Many algorithms have been used to track the MPP in a PV generator. Although these algorithms have proved their worth, the fact remains that they still have limits in terms of stability, response times and significant presence of oscillations, especially for sub-Saharan conditions where the climate variation is very sudden and has a considerable impact on the power delivered at the generator output. In this article, the objective is to develop a maximum power point tracking (MPPT) controller based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) to improve the performance of the Felicity Solar photovoltaic module FL-M-160W submitted to varying environmental conditions. The specifications of the FL-M-160W module are used to analyze and model the PV generator and boost converter located between the panel and the load in Matlab / Simulink. After the experimental tests, a database was set up to develop the neurofuzzy controller. The proposed ANFIS model was tested and validated under the Matlab / Simulink environment and then inserted into the PV system. The optimum voltage Vopt provided by this model is compared to the reference voltage Vpv provided by the PV generator and the error obtained is used to adjust the duty cycle of the DC-DC boost converter. After simulations, the results obtained reveal a good performance of the ANFIS controller compared to conventional P&O, InC and HC controllers in terms of stability, convergence speed, accuracy, robustness, and response time even under unstable environmental conditions with an efficiency of about 98%.
VL  - 9
IS  - 3
ER  -

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Author Information

Pascal Kuate Nkounhawa

Laboratory of Engineering for Industrial Systems and Environment, Faculty of Sciences / Department of Physics / Mechanics and Energetics, University of Dschang, Dschang, Cameroon
Dieunedort Ndapeu

Laboratory of Engineering for Industrial Systems and Environment, Faculty of Sciences / Department of Physics / Mechanics and Energetics, University of Dschang, Dschang, Cameroon
Bienvenu Kenmeugne

Laboratory of Mechanics and Civil Engineering, National Advanced School of Engineering of Yaoundé (ENSPY / UY1), University of Yaoundé 1, Yaoundé, Cameroon

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APA Style

Pascal Kuate Nkounhawa, Dieunedort Ndapeu, Bienvenu Kenmeugne. (2021). MPPT Based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for a Photovoltaic System Under Unstable Environmental Conditions. American Journal of Energy Engineering, 9(3), 68-84. https://doi.org/10.11648/j.ajee.20210903.12

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ACS Style

Pascal Kuate Nkounhawa; Dieunedort Ndapeu; Bienvenu Kenmeugne. MPPT Based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for a Photovoltaic System Under Unstable Environmental Conditions. Am. J. Energy Eng. 2021, 9(3), 68-84. doi: 10.11648/j.ajee.20210903.12

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AMA Style

Pascal Kuate Nkounhawa, Dieunedort Ndapeu, Bienvenu Kenmeugne. MPPT Based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for a Photovoltaic System Under Unstable Environmental Conditions. Am J Energy Eng. 2021;9(3):68-84. doi: 10.11648/j.ajee.20210903.12

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@article{10.11648/j.ajee.20210903.12,
  author = {Pascal Kuate Nkounhawa and Dieunedort Ndapeu and Bienvenu Kenmeugne},
  title = {MPPT Based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for a Photovoltaic System Under Unstable Environmental Conditions},
  journal = {American Journal of Energy Engineering},
  volume = {9},
  number = {3},
  pages = {68-84},
  doi = {10.11648/j.ajee.20210903.12},
  url = {https://doi.org/10.11648/j.ajee.20210903.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajee.20210903.12},
  abstract = {Many algorithms have been used to track the MPP in a PV generator. Although these algorithms have proved their worth, the fact remains that they still have limits in terms of stability, response times and significant presence of oscillations, especially for sub-Saharan conditions where the climate variation is very sudden and has a considerable impact on the power delivered at the generator output. In this article, the objective is to develop a maximum power point tracking (MPPT) controller based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) to improve the performance of the Felicity Solar photovoltaic module FL-M-160W submitted to varying environmental conditions. The specifications of the FL-M-160W module are used to analyze and model the PV generator and boost converter located between the panel and the load in Matlab / Simulink. After the experimental tests, a database was set up to develop the neurofuzzy controller. The proposed ANFIS model was tested and validated under the Matlab / Simulink environment and then inserted into the PV system. The optimum voltage Vopt provided by this model is compared to the reference voltage Vpv provided by the PV generator and the error obtained is used to adjust the duty cycle of the DC-DC boost converter. After simulations, the results obtained reveal a good performance of the ANFIS controller compared to conventional P&O, InC and HC controllers in terms of stability, convergence speed, accuracy, robustness, and response time even under unstable environmental conditions with an efficiency of about 98%.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - MPPT Based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for a Photovoltaic System Under Unstable Environmental Conditions
AU  - Pascal Kuate Nkounhawa
AU  - Dieunedort Ndapeu
AU  - Bienvenu Kenmeugne
Y1  - 2021/09/03
PY  - 2021
N1  - https://doi.org/10.11648/j.ajee.20210903.12
DO  - 10.11648/j.ajee.20210903.12
T2  - American Journal of Energy Engineering
JF  - American Journal of Energy Engineering
JO  - American Journal of Energy Engineering
SP  - 68
EP  - 84
PB  - Science Publishing Group
SN  - 2329-163X
UR  - https://doi.org/10.11648/j.ajee.20210903.12
AB  - Many algorithms have been used to track the MPP in a PV generator. Although these algorithms have proved their worth, the fact remains that they still have limits in terms of stability, response times and significant presence of oscillations, especially for sub-Saharan conditions where the climate variation is very sudden and has a considerable impact on the power delivered at the generator output. In this article, the objective is to develop a maximum power point tracking (MPPT) controller based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) to improve the performance of the Felicity Solar photovoltaic module FL-M-160W submitted to varying environmental conditions. The specifications of the FL-M-160W module are used to analyze and model the PV generator and boost converter located between the panel and the load in Matlab / Simulink. After the experimental tests, a database was set up to develop the neurofuzzy controller. The proposed ANFIS model was tested and validated under the Matlab / Simulink environment and then inserted into the PV system. The optimum voltage Vopt provided by this model is compared to the reference voltage Vpv provided by the PV generator and the error obtained is used to adjust the duty cycle of the DC-DC boost converter. After simulations, the results obtained reveal a good performance of the ANFIS controller compared to conventional P&O, InC and HC controllers in terms of stability, convergence speed, accuracy, robustness, and response time even under unstable environmental conditions with an efficiency of about 98%.
VL  - 9
IS  - 3
ER  -

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