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Maximum Power Point Tracking for Brushless DC Motor-Driven Photovoltaic Pumping Systems Using a Hybrid ANFIS-FLOWER Pollination Optimization Algorithm

Author

Listed:
  • Neeraj Priyadarshi

    (Department of Electrical and Electronics Engineering, Millia Institute of Technology, Purnea 854301, India)

  • Sanjeevikumar Padmanaban

    (Department of Energy Technology, Aalborg University, 6700 Esbjerg, Denmark)

  • Lucian Mihet-Popa

    (Faculty of Engineering, Østfold University College, Kobberslagerstredet 5, 1671 Kråkeroy-Fredrikstad, Norway)

  • Frede Blaabjerg

    (Center for Reliable Power Electronics (CORPE), Department of Energy Technology, Aalborg University, Aalborg 9220, Denmark)

  • Farooque Azam

    (Department of Electrical and Electronics Engineering, Millia Institute of Technology, Purnea 854301, India)

Abstract

In this research paper, a hybrid Artificial Neural Network (ANN)-Fuzzy Logic Control (FLC) tuned Flower Pollination Algorithm (FPA) as a Maximum Power Point Tracker (MPPT) is employed to amend root mean square error (RMSE) of photovoltaic (PV) modeling. Moreover, Gaussian membership functions have been considered for fuzzy controller design. This paper interprets the Luo converter occupied brushless DC motor (BLDC)-directed PV water pump application. Experimental responses certify the effectiveness of the suggested motor-pump system supporting diverse operating states. The Luo converter, a newly developed DC-DC converter, has high power density, better voltage gain transfer and superior output waveform and can track optimal power from PV modules. For BLDC speed control there is no extra circuitry, and phase current sensors are enforced for this scheme. The most recent attempt using adaptive neuro-fuzzy inference system (ANFIS)-FPA-operated BLDC directed PV pump with advanced Luo converter, has not been formerly conferred.

Suggested Citation

  • Neeraj Priyadarshi & Sanjeevikumar Padmanaban & Lucian Mihet-Popa & Frede Blaabjerg & Farooque Azam, 2018. "Maximum Power Point Tracking for Brushless DC Motor-Driven Photovoltaic Pumping Systems Using a Hybrid ANFIS-FLOWER Pollination Optimization Algorithm," Energies, MDPI, vol. 11(5), pages 1-16, April.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1067-:d:143348
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    Citations

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    Cited by:

    1. Xinmin Li & Guokai Jiang & Wei Chen & Tingna Shi & Guozheng Zhang & Qiang Geng, 2019. "Commutation Torque Ripple Suppression Strategy of Brushless DC Motor Considering Back Electromotive Force Variation," Energies, MDPI, vol. 12(10), pages 1-14, May.
    2. Tehzeeb-ul Hassan & Rabeh Abbassi & Houssem Jerbi & Kashif Mehmood & Muhammad Faizan Tahir & Khalid Mehmood Cheema & Rajvikram Madurai Elavarasan & Farman Ali & Irfan Ahmad Khan, 2020. "A Novel Algorithm for MPPT of an Isolated PV System Using Push Pull Converter with Fuzzy Logic Controller," Energies, MDPI, vol. 13(15), pages 1-20, August.
    3. Sergio Saponara & Lucian Mihet-Popa, 2019. "Energy Storage Systems and Power Conversion Electronics for E-Transportation and Smart Grid," Energies, MDPI, vol. 12(4), pages 1-9, February.
    4. Mohamed Dahbi & Said Doubabi & Ahmed Rachid, 2018. "Current Spikes Minimization Method for Three-Phase Permanent Magnet Brushless DC Motor with Real-Time Implementation," Energies, MDPI, vol. 11(11), pages 1-14, November.
    5. Ahmed Al Mansur & Md. Ruhul Amin & Kazi Khairul Islam, 2019. "Performance Comparison of Mismatch Power Loss Minimization Techniques in Series-Parallel PV Array Configurations," Energies, MDPI, vol. 12(5), pages 1-21, March.
    6. Mariam A. Sameh & Mostafa I. Marei & M. A. Badr & Mahmoud A. Attia, 2021. "An Optimized PV Control System Based on the Emperor Penguin Optimizer," Energies, MDPI, vol. 14(3), pages 1-16, February.
    7. Abdelbasset Krama & Laid Zellouma & Boualaga Rabhi & Shady S. Refaat & Mansour Bouzidi, 2018. "Real-Time Implementation of High Performance Control Scheme for Grid-Tied PV System for Power Quality Enhancement Based on MPPC-SVM Optimized by PSO Algorithm," Energies, MDPI, vol. 11(12), pages 1-26, December.
    8. Neeraj Priyadarshi & Sanjeevikumar Padmanaban & Dan M. Ionel & Lucian Mihet-Popa & Farooque Azam, 2018. "Hybrid PV-Wind, Micro-Grid Development Using Quasi-Z-Source Inverter Modeling and Control—Experimental Investigation," Energies, MDPI, vol. 11(9), pages 1-15, August.

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