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A novel MPPT algorithm based on optimized artificial neural network by using FPSOGSA for standalone photovoltaic energy systems

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Abstract

Maximum power point tracking (MPPT) algorithms are used to maximize the output power of the photovoltaic (PV) panel under different temperature and irradiance conditions in photovoltaic energy sources (PVES). In this paper, a novel MPPT method based on optimized artificial neural network by using hybrid particle swarm optimization and gravitational search algorithm based on fuzzy logic (FPSOGSA) is proposed to track the operation of the PV panel in maximum power point (MPP). The performance of the proposed MPPT approach is tested by doing the simulation and experimental studies under different environmental conditions. The proposed method is compared with the conventional perturb and observation (P&O) method for standalone PVES. The results of the comparison the obtained from the simulation and experimental studies demonstrate that the proposed MPPT method provides the reduction oscillations around the MPP and the increased maximum power yield of the PV system in the steady state.

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Acknowledgments

This study was supported by Kocaeli University Scientific Research Projects Unit. Project No: 2012/067.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Technology Faculty, Duzce University, Duzce, Turkey

    Serhat Duman

  2. Department of Electrical Engineering, Engineering Faculty, Kocaeli University, Kocaeli, Turkey

    Nuran Yorukeren

  3. Department of Electrical and Electronics Engineering, Karadeniz Technical University, 61080, Trabzon, Turkey

    Ismail H. Altas

Authors
  1. Serhat Duman
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  2. Nuran Yorukeren
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  3. Ismail H. Altas
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Corresponding author

Correspondence to Serhat Duman.

Appendix

Appendix

See Table 8.

Table 8 The parameters of the FPSOGSA algorithm
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Duman, S., Yorukeren, N. & Altas, I.H. A novel MPPT algorithm based on optimized artificial neural network by using FPSOGSA for standalone photovoltaic energy systems. Neural Comput & Applic 29, 257–278 (2018). https://doi.org/10.1007/s00521-016-2447-9

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  • DOI: https://doi.org/10.1007/s00521-016-2447-9

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