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Global Maximum Power Point Tracking Based on Intelligent Approach for Photovoltaic System Under Partial Shading Conditions

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Advances in Soft Computing (MICAI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10632))

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Abstract

This paper presents the design of a controller for Maximum Power Point Tracking (MPPT) of a photovoltaic system. The proposed controller relies upon a Recurrent Neuro-Fuzzy (RNF) which is designed as a combination of the concepts of Sugeno fuzzy model and neural network. The controller employs the RNF of four-layer with sixty-four fuzzy rules. Moreover, for the proposed RNF an improved self-tuning method is developed based on the photovoltaic system and its high performance requirements. The principal task of the tuning method is to adjust the parameters of the Fuzzy Logic (FL) in order to minimize the square of the error between actual and reference output. Simulations with practical parameters show that our proposed MPPT using RNF outperform the conventional MPPT controller terms of tracking speed and accuracy.

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Authors and Affiliations

  1. Department of Electrical Engineering, Higher School of Technology, Cadi Ayyad University, Essaouira, Morocco

    Moulay Rachid Douiri

  2. Laboratory of Mathematic Informatics and Applications, Faculty of Sciences, University Mohammed V-Agdal, Rabat, Morocco

    Sidi Mohamed Douiri

Authors
  1. Moulay Rachid Douiri
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  2. Sidi Mohamed Douiri
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Correspondence to Moulay Rachid Douiri .

Editor information

Editors and Affiliations

  1. Universidad Autónoma del Estado de Hidalgo, Pachuca, Mexico

    Félix Castro

  2. INFOTEC Aguascalientes, Aguascalientes, Mexico

    Sabino Miranda-Jiménez

  3. Tecnológico de Monterrey, Atizapán de Zaragoza, Mexico

    Miguel González-Mendoza

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Douiri, M.R., Douiri, S.M. (2018). Global Maximum Power Point Tracking Based on Intelligent Approach for Photovoltaic System Under Partial Shading Conditions. In: Castro, F., Miranda-Jiménez, S., González-Mendoza, M. (eds) Advances in Soft Computing. MICAI 2017. Lecture Notes in Computer Science(), vol 10632. Springer, Cham. https://doi.org/10.1007/978-3-030-02837-4_14

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  • DOI: https://doi.org/10.1007/978-3-030-02837-4_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02836-7

  • Online ISBN: 978-3-030-02837-4

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