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Efficiency optimization of a DSP-based standalone PV system using a stable single input fuzzy logic controller

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  • Farhat, Maissa
  • Barambones, Oscar
  • Sbita, Lassaâd

Abstract

This paper presents a new digital maximum power point tracking control scheme for a standalone photovoltaic (PV) system. It is built up on a single input fuzzy-logic (SIFLC), and based on the constant voltage algorithm. The SIFLC generates a duty cycle (D) signal which is the control one for the DC–DC Boost converter. The proposed SIFLC performances will be compared to these of the well-known P&O algorithm. A stability study for the Mamdani SIFLC controllers is performed and proposed. The Lyapunov method is considered for the stability analysis of the proposed control systems. This MPPT algorithm is then experimentally implemented all around a DSP1104 for a real-time driving. The obtained results show that the proposed SIFLC-MPPT clearly succeeds to track the maximum power point and shows a higher performance than regular MPPTs such as the P&O algorithm. Results of the experimental tests also prove the validity and robustness of the proposed overall PV system control scheme.

Suggested Citation

  • Farhat, Maissa & Barambones, Oscar & Sbita, Lassaâd, 2015. "Efficiency optimization of a DSP-based standalone PV system using a stable single input fuzzy logic controller," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 907-920.
    • Handle: RePEc:eee:rensus:v:49:y:2015:i:c:p:907-920
    DOI: 10.1016/j.rser.2015.04.123
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    Cited by:

    1. Haidar Islam & Saad Mekhilef & Noraisyah Binti Mohamed Shah & Tey Kok Soon & Mehdi Seyedmahmousian & Ben Horan & Alex Stojcevski, 2018. "Performance Evaluation of Maximum Power Point Tracking Approaches and Photovoltaic Systems," Energies, MDPI, vol. 11(2), pages 1-24, February.
    2. Nabipour, M. & Razaz, M. & Seifossadat, S.GH & Mortazavi, S.S., 2017. "A new MPPT scheme based on a novel fuzzy approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 1147-1169.
    3. Ram, J.Prasanth & Rajasekar, N. & Miyatake, Masafumi, 2017. "Design and overview of maximum power point tracking techniques in wind and solar photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1138-1159.
    4. Mohamed Redha Rezoug & Rachid Chenni & Djamel Taibi, 2018. "Fuzzy Logic-Based Perturb and Observe Algorithm with Variable Step of a Reference Voltage for Solar Permanent Magnet Synchronous Motor Drive System Fed by Direct-Connected Photovoltaic Array," Energies, MDPI, vol. 11(2), pages 1-15, February.
    5. Maen Takruri & Maissa Farhat & Oscar Barambones & José Antonio Ramos-Hernanz & Mohammed Jawdat Turkieh & Mohammed Badawi & Hanin AlZoubi & Maswood Abdus Sakur, 2020. "Maximum Power Point Tracking of PV System Based on Machine Learning," Energies, MDPI, vol. 13(3), pages 1-14, February.
    6. Ram, J. Prasanth & Babu, T. Sudhakar & Rajasekar, N., 2017. "A comprehensive review on solar PV maximum power point tracking techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 826-847.
    7. Farhat, Maissa & Barambones, Oscar & Sbita, Lassaad, 2017. "A new maximum power point method based on a sliding mode approach for solar energy harvesting," Applied Energy, Elsevier, vol. 185(P2), pages 1185-1198.
    8. Maissa Farhat & Oscar Barambones & Lassaâd Sbita, 2020. "A Real-Time Implementation of Novel and Stable Variable Step Size MPPT," Energies, MDPI, vol. 13(18), pages 1-18, September.
    9. Enany, Mohamed A. & Farahat, Mohamed A. & Nasr, Ahmed, 2016. "Modeling and evaluation of main maximum power point tracking algorithms for photovoltaics systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1578-1586.
    10. Seyedmahmoudian, M. & Horan, B. & Soon, T. Kok & Rahmani, R. & Than Oo, A. Muang & Mekhilef, S. & Stojcevski, A., 2016. "State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 435-455.
    11. Ben Ali, I. & Turki, M. & Belhadj, J. & Roboam, X., 2018. "Optimized fuzzy rule-based energy management for a battery-less PV/wind-BWRO desalination system," Energy, Elsevier, vol. 159(C), pages 216-228.

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    Keywords

    BOOST; FUZZY; MPPT; PVG; P&O;
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