Performance of MPPT in photovoltaic systems using GA-ANN optimization scheme

Ahmed Ali; Bhekisipho Twala; Tshilidzi Marwala

doi:10.1007/978-981-10-7868-2_4

Performance of MPPT in photovoltaic systems using GA-ANN optimization scheme

Ahmed Ali
, Bhekisipho Twala
, Tshilidzi Marwala

University of Johannesburg

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

Abstract

Researchers all over the world are currently moving toward using solar energy resulting from large energy demand and sources of energy as well as the environmental problems, such as dynamic weather conditions. The control of maximum power point tracking (MPPT) meteorological conditions is an essential portion of improving solar power systems. In this paper, we introduce an elastic controller depend on artificial neural network for regulating the MPPT. This controller is employed to the buck–boost DC-to-DC converter using the MATLAB/Simulink software program. This paper proposes a design that maximizes the performance of GA-ANN scheme, and compared with ANN scheme, efficiency of PV module is shown as well as the saving power for both schemes. The results show that GA-ANN has performance about 45% over ANN scheme.

Original language	English
Title of host publication	Artificial Intelligence and Evolutionary Computations in Engineering Systems - Proceedings of ICAIECES 2017
Editors	Swagatam Das, Ramazan Bayindir, Subhransu Sekhar Dash, Paruchuri Chandra Naidu
Publisher	Springer Verlag
Pages	39-49
Number of pages	11
ISBN (Print)	9789811078675
DOIs	https://doi.org/10.1007/978-981-10-7868-2_4
Publication status	Published - 2018
Event	International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems, ICAIECES 2017 - Madanapalle, India Duration: 27 Apr 2017 → 29 Apr 2017

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	668
ISSN (Print)	2194-5357

Conference

Conference	International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems, ICAIECES 2017
Country/Territory	India
City	Madanapalle
Period	27/04/17 → 29/04/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Buck–Boost DC
Genetic algorithm
MPP tracker
Neural networks
Photovoltaic systems

ASJC Scopus subject areas

Control and Systems Engineering
General Computer Science

Access to Document

10.1007/978-981-10-7868-2_4

Cite this

Ali, A., Twala, B., & Marwala, T. (2018). Performance of MPPT in photovoltaic systems using GA-ANN optimization scheme. In S. Das, R. Bayindir, S. S. Dash, & P. C. Naidu (Eds.), Artificial Intelligence and Evolutionary Computations in Engineering Systems - Proceedings of ICAIECES 2017 (pp. 39-49). (Advances in Intelligent Systems and Computing; Vol. 668). Springer Verlag. https://doi.org/10.1007/978-981-10-7868-2_4

Ali, Ahmed ; Twala, Bhekisipho ; Marwala, Tshilidzi. / Performance of MPPT in photovoltaic systems using GA-ANN optimization scheme. Artificial Intelligence and Evolutionary Computations in Engineering Systems - Proceedings of ICAIECES 2017. editor / Swagatam Das ; Ramazan Bayindir ; Subhransu Sekhar Dash ; Paruchuri Chandra Naidu. Springer Verlag, 2018. pp. 39-49 (Advances in Intelligent Systems and Computing).

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title = "Performance of MPPT in photovoltaic systems using GA-ANN optimization scheme",

abstract = "Researchers all over the world are currently moving toward using solar energy resulting from large energy demand and sources of energy as well as the environmental problems, such as dynamic weather conditions. The control of maximum power point tracking (MPPT) meteorological conditions is an essential portion of improving solar power systems. In this paper, we introduce an elastic controller depend on artificial neural network for regulating the MPPT. This controller is employed to the buck–boost DC-to-DC converter using the MATLAB/Simulink software program. This paper proposes a design that maximizes the performance of GA-ANN scheme, and compared with ANN scheme, efficiency of PV module is shown as well as the saving power for both schemes. The results show that GA-ANN has performance about 45\% over ANN scheme.",

keywords = "Buck–Boost DC, Genetic algorithm, MPP tracker, Neural networks, Photovoltaic systems",

author = "Ahmed Ali and Bhekisipho Twala and Tshilidzi Marwala",

note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2018.; International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems, ICAIECES 2017 ; Conference date: 27-04-2017 Through 29-04-2017",

year = "2018",

doi = "10.1007/978-981-10-7868-2\_4",

language = "English",

isbn = "9789811078675",

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Ali, A, Twala, B & Marwala, T 2018, Performance of MPPT in photovoltaic systems using GA-ANN optimization scheme. in S Das, R Bayindir, SS Dash & PC Naidu (eds), Artificial Intelligence and Evolutionary Computations in Engineering Systems - Proceedings of ICAIECES 2017. Advances in Intelligent Systems and Computing, vol. 668, Springer Verlag, pp. 39-49, International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems, ICAIECES 2017, Madanapalle, India, 27/04/17. https://doi.org/10.1007/978-981-10-7868-2_4

Performance of MPPT in photovoltaic systems using GA-ANN optimization scheme. / Ali, Ahmed; Twala, Bhekisipho; Marwala, Tshilidzi.
Artificial Intelligence and Evolutionary Computations in Engineering Systems - Proceedings of ICAIECES 2017. ed. / Swagatam Das; Ramazan Bayindir; Subhransu Sekhar Dash; Paruchuri Chandra Naidu. Springer Verlag, 2018. p. 39-49 (Advances in Intelligent Systems and Computing; Vol. 668).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Performance of MPPT in photovoltaic systems using GA-ANN optimization scheme

AU - Ali, Ahmed

AU - Twala, Bhekisipho

AU - Marwala, Tshilidzi

N1 - Publisher Copyright: © Springer Nature Singapore Pte Ltd. 2018.

PY - 2018

Y1 - 2018

N2 - Researchers all over the world are currently moving toward using solar energy resulting from large energy demand and sources of energy as well as the environmental problems, such as dynamic weather conditions. The control of maximum power point tracking (MPPT) meteorological conditions is an essential portion of improving solar power systems. In this paper, we introduce an elastic controller depend on artificial neural network for regulating the MPPT. This controller is employed to the buck–boost DC-to-DC converter using the MATLAB/Simulink software program. This paper proposes a design that maximizes the performance of GA-ANN scheme, and compared with ANN scheme, efficiency of PV module is shown as well as the saving power for both schemes. The results show that GA-ANN has performance about 45% over ANN scheme.

AB - Researchers all over the world are currently moving toward using solar energy resulting from large energy demand and sources of energy as well as the environmental problems, such as dynamic weather conditions. The control of maximum power point tracking (MPPT) meteorological conditions is an essential portion of improving solar power systems. In this paper, we introduce an elastic controller depend on artificial neural network for regulating the MPPT. This controller is employed to the buck–boost DC-to-DC converter using the MATLAB/Simulink software program. This paper proposes a design that maximizes the performance of GA-ANN scheme, and compared with ANN scheme, efficiency of PV module is shown as well as the saving power for both schemes. The results show that GA-ANN has performance about 45% over ANN scheme.

KW - Buck–Boost DC

KW - Genetic algorithm

KW - MPP tracker

KW - Neural networks

KW - Photovoltaic systems

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DO - 10.1007/978-981-10-7868-2_4

M3 - Conference contribution

AN - SCOPUS:85044424005

SN - 9789811078675

T3 - Advances in Intelligent Systems and Computing

SP - 39

EP - 49

BT - Artificial Intelligence and Evolutionary Computations in Engineering Systems - Proceedings of ICAIECES 2017

A2 - Das, Swagatam

A2 - Bayindir, Ramazan

A2 - Dash, Subhransu Sekhar

A2 - Naidu, Paruchuri Chandra

PB - Springer Verlag

T2 - International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems, ICAIECES 2017

Y2 - 27 April 2017 through 29 April 2017

ER -

Ali A, Twala B, Marwala T. Performance of MPPT in photovoltaic systems using GA-ANN optimization scheme. In Das S, Bayindir R, Dash SS, Naidu PC, editors, Artificial Intelligence and Evolutionary Computations in Engineering Systems - Proceedings of ICAIECES 2017. Springer Verlag. 2018. p. 39-49. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-981-10-7868-2_4

Performance of MPPT in photovoltaic systems using GA-ANN optimization scheme

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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