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Smart Technologies for Energy, Environment and Sustainable Development pp 93–101Cite as

Maximum Power Point Tracker for Standalone PV System Using Neural Networks

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Part of the book series: Lecture Notes on Multidisciplinary Industrial Engineering ((LNMUINEN))

Abstract

In this work, designing and implementation of a maximum power point tracker (MPPT) based on an artificial neural network is proposed. The output voltage of the selected photovoltaic array is controlled by a DC to DC boost converter in a way that the PV array generates the available possible maximum power correspond to the available solar irradiance and temperature. The neural network (NN) is capable of forecasting the required terminal voltage of the PV array in order to generate the possible maximum power. The pulse width modulation (PWM) signal, which drives the boost converter, is generated through a raspberry pi according to the forecasted terminal voltage. The terminal voltage of the PV array is controlled by changing the duty ratio of the PWM signal accordingly. The impact of the implemented NN toward the response time and the accuracy is discussed. NN based MPPT can provide a reliable solution.

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References

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Authors and Affiliations

  1. Faculty of Engineering and Science, University of Agder (UiA), 4898, Grimstad, Norway

    K. M. S. Y. Konara & Mohan Lal Kolhe

  2. Faculty of Engineering, University of Ruhuna, Galle, Sri Lanka

    K. M. S. Y. Konara, M. A. N. Sanjeewa, W. T. V. S. Fernando, G. M. N. Priyashantha & J. W. G. S. Weerasinghe

Authors
  1. K. M. S. Y. Konara
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  2. Mohan Lal Kolhe
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  3. M. A. N. Sanjeewa
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  4. W. T. V. S. Fernando
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  5. G. M. N. Priyashantha
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  6. J. W. G. S. Weerasinghe
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Corresponding author

Correspondence to K. M. S. Y. Konara .

Editor information

Editors and Affiliations

  1. Electrical Power Engineering (Smart Grid and Renewable Energy), Faculty of Engineering and Science, University of Agder, Kristiansand, Norway

    Mohan Lal Kolhe

  2. Water Technology and Management, National Environmental Engineering Research Institute (NEERI), Council of Scientific and Industrial Research, Nagpur, India

    Pawan Kumar Labhasetwar

  3. Department of Electrical Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, India

    H. M. Suryawanshi

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© 2019 Springer Nature Singapore Pte Ltd.

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Konara, K.M.S.Y., Kolhe, M.L., Sanjeewa, M.A.N., Fernando, W.T.V.S., Priyashantha, G.M.N., Weerasinghe, J.W.G.S. (2019). Maximum Power Point Tracker for Standalone PV System Using Neural Networks. In: Kolhe, M., Labhasetwar, P., Suryawanshi, H. (eds) Smart Technologies for Energy, Environment and Sustainable Development. Lecture Notes on Multidisciplinary Industrial Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-13-6148-7_10

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  • DOI: https://doi.org/10.1007/978-981-13-6148-7_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6147-0

  • Online ISBN: 978-981-13-6148-7

  • eBook Packages: EnergyEnergy (R0)

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