Application of Artificial Neural Networks for Maximal Power Point Tracking

Amadou Fousseyni Toure; Fadaba Danioko; Badie Diourte

doi:doi:10.11648/j.ijrse.20211002.12

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Application of Artificial Neural Networks for Maximal Power Point Tracking

Amadou Fousseyni Toure, Fadaba Danioko, Badie Diourte

Published in International Journal of Sustainable and Green Energy (Volume 10, Issue 2)

Received: 6 April 2021 Accepted: 26 April 2021 Published: 8 May 2021

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Abstract

In this paper, a hybrid controller of maximum power point tracking of photovoltaic systems based on the artificial neuron network has been proposed and studied. The data needed for model generation are obtained from the series of measurements. The training of neural networks requires two modes: the off-line mode to get optimal structure, activation function and learning algorithm of neural networks and in an online way these optimal neural networks are used in the PV system. The hybrid model is made up of two neural networks; the first network has two inputs and two outputs; the solar irradiation and the ambient temperature are the inputs; the outputs are the references voltage and current at the maximal power point. The second network has two inputs and one output; the inputs use the outputs of the first network, and the output will be the periodic cycle which controls the DC/DC converter. The performance of the method is analyzed under the different conditions of climatic variation using the MATLAB/Simulink simulation tool. A we compared our proposed method to the perturbation and observation approach, in terms of tracking accuracy, steady state ripple and response time.

Published in	International Journal of Sustainable and Green Energy (Volume 10, Issue 2)
DOI	10.11648/j.ijrse.20211002.12
Page(s)	40-46
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

PV System PV, MPPT controller, Artificial Neural Networks, MATLAB/Simulink

References

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[6]	Farhoodnea, M., Mohamed, A., Shareef, H., & Zayandehroodi, H. (2013, June). Optimum D-STATCOM placement using firefly algorithm for power quality enhancement. In 2013 IEEE 7th international power engineering and optimization conference (PEOCO) (pp. 98-102). IEEE.
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[8]	Ramasamy, A., & Vanitha, N. S. (2014, January). Maximum power tracking for PV generating system using novel optimized fractional order open circuit voltage-FOINC method. In 2014 International Conference on Computer Communication and Informatics (pp. 1-6). IEEE.
[9]	Afghoul, H., Krim, F., Chikouche, D., & Beddar, A. (2013, May). Tracking the maximum power from a PV panels using of Neuro-fuzzy controller. In 2013 IEEE International Symposium on Industrial Electronics (pp. 1-6). IEEE.
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[14]	Malek, H., & Chen, Y. (2014, March). A single-stage three-phase grid-connected photovoltaic system with fractional order MPPT. In 2014 IEEE Applied Power Electronics Conference and Exposition-APEC 2014 (pp. 1793-1798). IEEE.
[15]	Toure, A. F., Addouche, S. A., Danioko, F., Diourte, B., & Mhamedi, A. E. (2019). Hybrid Systems Optimization: Application to Hybrid Systems Photovoltaic Connected to Grid. A Mali Case Study. Sustainability, 11 (8), 2356.
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APA Style

Amadou Fousseyni Toure, Fadaba Danioko, Badie Diourte. (2021). Application of Artificial Neural Networks for Maximal Power Point Tracking. International Journal of Sustainable and Green Energy, 10(2), 40-46. https://doi.org/10.11648/j.ijrse.20211002.12

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ACS Style

Amadou Fousseyni Toure; Fadaba Danioko; Badie Diourte. Application of Artificial Neural Networks for Maximal Power Point Tracking. Int. J. Sustain. Green Energy 2021, 10(2), 40-46. doi: 10.11648/j.ijrse.20211002.12

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AMA Style

Amadou Fousseyni Toure, Fadaba Danioko, Badie Diourte. Application of Artificial Neural Networks for Maximal Power Point Tracking. Int J Sustain Green Energy. 2021;10(2):40-46. doi: 10.11648/j.ijrse.20211002.12

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@article{10.11648/j.ijrse.20211002.12,
  author = {Amadou Fousseyni Toure and Fadaba Danioko and Badie Diourte},
  title = {Application of Artificial Neural Networks for Maximal Power Point Tracking},
  journal = {International Journal of Sustainable and Green Energy},
  volume = {10},
  number = {2},
  pages = {40-46},
  doi = {10.11648/j.ijrse.20211002.12},
  url = {https://doi.org/10.11648/j.ijrse.20211002.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijrse.20211002.12},
  abstract = {In this paper, a hybrid controller of maximum power point tracking of photovoltaic systems based on the artificial neuron network has been proposed and studied. The data needed for model generation are obtained from the series of measurements. The training of neural networks requires two modes: the off-line mode to get optimal structure, activation function and learning algorithm of neural networks and in an online way these optimal neural networks are used in the PV system. The hybrid model is made up of two neural networks; the first network has two inputs and two outputs; the solar irradiation and the ambient temperature are the inputs; the outputs are the references voltage and current at the maximal power point. The second network has two inputs and one output; the inputs use the outputs of the first network, and the output will be the periodic cycle which controls the DC/DC converter. The performance of the method is analyzed under the different conditions of climatic variation using the MATLAB/Simulink simulation tool. A we compared our proposed method to the perturbation and observation approach, in terms of tracking accuracy, steady state ripple and response time.},
 year = {2021}
}

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TY  - JOUR
T1  - Application of Artificial Neural Networks for Maximal Power Point Tracking
AU  - Amadou Fousseyni Toure
AU  - Fadaba Danioko
AU  - Badie Diourte
Y1  - 2021/05/08
PY  - 2021
N1  - https://doi.org/10.11648/j.ijrse.20211002.12
DO  - 10.11648/j.ijrse.20211002.12
T2  - International Journal of Sustainable and Green Energy
JF  - International Journal of Sustainable and Green Energy
JO  - International Journal of Sustainable and Green Energy
SP  - 40
EP  - 46
PB  - Science Publishing Group
SN  - 2575-1549
UR  - https://doi.org/10.11648/j.ijrse.20211002.12
AB  - In this paper, a hybrid controller of maximum power point tracking of photovoltaic systems based on the artificial neuron network has been proposed and studied. The data needed for model generation are obtained from the series of measurements. The training of neural networks requires two modes: the off-line mode to get optimal structure, activation function and learning algorithm of neural networks and in an online way these optimal neural networks are used in the PV system. The hybrid model is made up of two neural networks; the first network has two inputs and two outputs; the solar irradiation and the ambient temperature are the inputs; the outputs are the references voltage and current at the maximal power point. The second network has two inputs and one output; the inputs use the outputs of the first network, and the output will be the periodic cycle which controls the DC/DC converter. The performance of the method is analyzed under the different conditions of climatic variation using the MATLAB/Simulink simulation tool. A we compared our proposed method to the perturbation and observation approach, in terms of tracking accuracy, steady state ripple and response time.
VL  - 10
IS  - 2
ER  -

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Author Information

Amadou Fousseyni Toure

Institut of Sciences Appliques, University of Sciences Techniques and Technologies, Bamako, Mali
Fadaba Danioko

Faculty of Science and Techniques, University of Sciences Techniques and Technologies, Bamako, Mali
Badie Diourte

Faculty of Science and Techniques, University of Sciences Techniques and Technologies, Bamako, Mali

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Plain Text BibTeX RIS

APA Style

Amadou Fousseyni Toure, Fadaba Danioko, Badie Diourte. (2021). Application of Artificial Neural Networks for Maximal Power Point Tracking. International Journal of Sustainable and Green Energy, 10(2), 40-46. https://doi.org/10.11648/j.ijrse.20211002.12

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ACS Style

Amadou Fousseyni Toure; Fadaba Danioko; Badie Diourte. Application of Artificial Neural Networks for Maximal Power Point Tracking. Int. J. Sustain. Green Energy 2021, 10(2), 40-46. doi: 10.11648/j.ijrse.20211002.12

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AMA Style

Amadou Fousseyni Toure, Fadaba Danioko, Badie Diourte. Application of Artificial Neural Networks for Maximal Power Point Tracking. Int J Sustain Green Energy. 2021;10(2):40-46. doi: 10.11648/j.ijrse.20211002.12

Copy | Download

@article{10.11648/j.ijrse.20211002.12,
  author = {Amadou Fousseyni Toure and Fadaba Danioko and Badie Diourte},
  title = {Application of Artificial Neural Networks for Maximal Power Point Tracking},
  journal = {International Journal of Sustainable and Green Energy},
  volume = {10},
  number = {2},
  pages = {40-46},
  doi = {10.11648/j.ijrse.20211002.12},
  url = {https://doi.org/10.11648/j.ijrse.20211002.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijrse.20211002.12},
  abstract = {In this paper, a hybrid controller of maximum power point tracking of photovoltaic systems based on the artificial neuron network has been proposed and studied. The data needed for model generation are obtained from the series of measurements. The training of neural networks requires two modes: the off-line mode to get optimal structure, activation function and learning algorithm of neural networks and in an online way these optimal neural networks are used in the PV system. The hybrid model is made up of two neural networks; the first network has two inputs and two outputs; the solar irradiation and the ambient temperature are the inputs; the outputs are the references voltage and current at the maximal power point. The second network has two inputs and one output; the inputs use the outputs of the first network, and the output will be the periodic cycle which controls the DC/DC converter. The performance of the method is analyzed under the different conditions of climatic variation using the MATLAB/Simulink simulation tool. A we compared our proposed method to the perturbation and observation approach, in terms of tracking accuracy, steady state ripple and response time.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Application of Artificial Neural Networks for Maximal Power Point Tracking
AU  - Amadou Fousseyni Toure
AU  - Fadaba Danioko
AU  - Badie Diourte
Y1  - 2021/05/08
PY  - 2021
N1  - https://doi.org/10.11648/j.ijrse.20211002.12
DO  - 10.11648/j.ijrse.20211002.12
T2  - International Journal of Sustainable and Green Energy
JF  - International Journal of Sustainable and Green Energy
JO  - International Journal of Sustainable and Green Energy
SP  - 40
EP  - 46
PB  - Science Publishing Group
SN  - 2575-1549
UR  - https://doi.org/10.11648/j.ijrse.20211002.12
AB  - In this paper, a hybrid controller of maximum power point tracking of photovoltaic systems based on the artificial neuron network has been proposed and studied. The data needed for model generation are obtained from the series of measurements. The training of neural networks requires two modes: the off-line mode to get optimal structure, activation function and learning algorithm of neural networks and in an online way these optimal neural networks are used in the PV system. The hybrid model is made up of two neural networks; the first network has two inputs and two outputs; the solar irradiation and the ambient temperature are the inputs; the outputs are the references voltage and current at the maximal power point. The second network has two inputs and one output; the inputs use the outputs of the first network, and the output will be the periodic cycle which controls the DC/DC converter. The performance of the method is analyzed under the different conditions of climatic variation using the MATLAB/Simulink simulation tool. A we compared our proposed method to the perturbation and observation approach, in terms of tracking accuracy, steady state ripple and response time.
VL  - 10
IS  - 2
ER  -

Copy | Download