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Design and implementation of ANFIS-reference model controller based MPPT using FPGA for photovoltaic system

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  • Aldair, Ammar A.
  • Obed, Adel A.
  • Halihal, Ali F.

Abstract

The aim of this work is to demonstrate the usefulness of Adaptive Neuro Fuzzy Inference System (ANFIS) for tracking Maximum Power Point (MPP) in stand-alone photovoltaic system. Maximum Power Point Tracking (MPPT) is one of approaches which boost efficiency of PhotoVoltaic (PV) cells by the load matching between the PV cells and the load. The key problem is that maximum power is not achieved because PV cells power is affected by weather conditions such as the solar irradiation and the temperature, thus, the MPP is changed during daylight hours and year seasons. Therefore, it is necessary to design an appropriate controller based on one of techniques to track MPP. These techniques are based on true or estimated searching mechanism to MPP. True searching mechanism based techniques like incremental conductance method and perturb and observe method are efficient but they are less stable, more oscillatory about MPP and sensitive to a high frequency noise. Generally, estimated searching mechanism based techniques like constant voltage method and fractional open circuit voltage method are less efficient, but they are stable and no sensitive to a high frequency noise. In this paper, the ANFIS-reference model method in addition to the incremental conductance method and constant voltage method have been studied, designed and implemented using Field Programmable Gate Array (FPGA) board to compare the performance of each method. The proposed ANFIS-reference model controller is efficient since it has been trained offline using Matlab tool with practical data sets. Based on our knowledge, this paper is the first paper which introduces practical implementation of ANFIS-reference model based MPPT for photovoltaic system using FPGA board. The results reveal that the ANFIS-reference model controller has more efficient and better dynamic response than the incremental conductance method and constant voltage method.

Suggested Citation

  • Aldair, Ammar A. & Obed, Adel A. & Halihal, Ali F., 2018. "Design and implementation of ANFIS-reference model controller based MPPT using FPGA for photovoltaic system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2202-2217.
    • Handle: RePEc:eee:rensus:v:82:y:2018:i:p3:p:2202-2217
    DOI: 10.1016/j.rser.2017.08.071
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    References listed on IDEAS

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    1. Mousazadeh, Hossein & Keyhani, Alireza & Javadi, Arzhang & Mobli, Hossein & Abrinia, Karen & Sharifi, Ahmad, 2009. "A review of principle and sun-tracking methods for maximizing solar systems output," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1800-1818, October.
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    1. Rezk, Hegazy & AL-Oran, Mazen & Gomaa, Mohamed R. & Tolba, Mohamed A. & Fathy, Ahmed & Abdelkareem, Mohammad Ali & Olabi, A.G. & El-Sayed, Abou Hashema M., 2019. "A novel statistical performance evaluation of most modern optimization-based global MPPT techniques for partially shaded PV system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    2. Subramanian Vasantharaj & Vairavasundaram Indragandhi & Vairavasundaram Subramaniyaswamy & Yuvaraja Teekaraman & Ramya Kuppusamy & Srete Nikolovski, 2021. "Efficient Control of DC Microgrid with Hybrid PV—Fuel Cell and Energy Storage Systems," Energies, MDPI, vol. 14(11), pages 1-18, June.
    3. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    4. CH Hussaian Basha & C Rani, 2020. "Different Conventional and Soft Computing MPPT Techniques for Solar PV Systems with High Step-Up Boost Converters: A Comprehensive Analysis," Energies, MDPI, vol. 13(2), pages 1-27, January.
    5. Rami Alamoudi & Osman Taylan & Mehmet Azmi Aktacir & Enrique Herrera-Viedma, 2021. "Designing a Solar Photovoltaic System for Generating Renewable Energy of a Hospital: Performance Analysis and Adjustment Based on RSM and ANFIS Approaches," Mathematics, MDPI, vol. 9(22), pages 1-24, November.

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