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Optimized Adaptive Perturb and Observe Maximum Power Point Tracking Control for Photovoltaic Generation

Author

Listed:
  • Luigi Piegari

    (Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
    These authors contributed equally to this work.)

  • Renato Rizzo

    (Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy
    These authors contributed equally to this work.)

  • Ivan Spina

    (Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy)

  • Pietro Tricoli

    (School of Electronic, Electrical and Computer Engineering, University of Birmingham, Gisbert Kapp Building, Birmingham B15 2TT, UK
    These authors contributed equally to this work.)

Abstract

The power extracted from PV arrays is usually maximized using maximum power point tracking algorithms. One of the most widely used techniques is the perturb & observe algorithm, which periodically perturbs the operating point of the PV array, sometime with an adaptive perturbation step, and compares the PV power before and after the perturbation. This paper analyses the most suitable perturbation step to optimize maximum power point tracking performance and suggests a design criterion to select the parameters of the controller. Using this proposed adaptive step, the MPPT perturb & observe algorithm achieves an excellent dynamic response by adapting the perturbation step to the actual operating conditions of the PV array. The proposed algorithm has been validated and tested in a laboratory using a dual input inductor push-pull converter. This particular converter topology is an efficient interface to boost the low voltage of PV arrays and effectively control the power flow when input or output voltages are variable. The experimental results have proved the superiority of the proposed algorithm in comparison of traditional perturb & observe and incremental conductance techniques.

Suggested Citation

  • Luigi Piegari & Renato Rizzo & Ivan Spina & Pietro Tricoli, 2015. "Optimized Adaptive Perturb and Observe Maximum Power Point Tracking Control for Photovoltaic Generation," Energies, MDPI, vol. 8(5), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:5:p:3418-3436:d:48794
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    Citations

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    Cited by:

    1. 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.
    2. Shahrooz Hajighorbani & Mohd Amran Mohd Radzi & Mohd Zainal Abidin Ab Kadir & Suhaidi Shafie & Muhammad Ammirrul Atiqi Mohd Zainuri, 2016. "Implementing a Novel Hybrid Maximum Power Point Tracking Technique in DSP via Simulink/MATLAB under Partially Shaded Conditions," Energies, MDPI, vol. 9(2), pages 1-25, January.
    3. Yun-Gyeong Oh & Woo-Young Choi & Jung-Min Kwon, 2021. "Design of a Step-Up DC–DC Converter for Standalone Photovoltaic Systems with Battery Energy Storages," Energies, MDPI, vol. 15(1), pages 1-11, December.
    4. 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.
    5. Petr Mastny & Jan Moravek & Martin Vojtek & Jiri Drapela, 2017. "Hybrid Photovoltaic Systems with Accumulation—Support for Electric Vehicle Charging," Energies, MDPI, vol. 10(7), pages 1-24, June.
    6. 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.
    7. Muhammad Hafeez Mohamed Hariri & Mohd Khairunaz Mat Desa & Syafrudin Masri & Muhammad Ammirrul Atiqi Mohd Zainuri, 2020. "Grid-Connected PV Generation System—Components and Challenges: A Review," Energies, MDPI, vol. 13(17), pages 1-28, August.
    8. Chendi Li & Yuanrui Chen & Dongbao Zhou & Junfeng Liu & Jun Zeng, 2016. "A High-Performance Adaptive Incremental Conductance MPPT Algorithm for Photovoltaic Systems," Energies, MDPI, vol. 9(4), pages 1-17, April.
    9. Jian Zhao & Xuesong Zhou & Youjie Ma & Yiqi Liu, 2017. "Analysis of Dynamic Characteristic for Solar Arrays in Series and Global Maximum Power Point Tracking Based on Optimal Initial Value Incremental Conductance Strategy under Partially Shaded Conditions," Energies, MDPI, vol. 10(1), pages 1-23, January.
    10. Ming-Fa Tsai & Chung-Shi Tseng & Kuo-Tung Hung & Shih-Hua Lin, 2021. "A Novel DSP-Based MPPT Control Design for Photovoltaic Systems Using Neural Network Compensator," Energies, MDPI, vol. 14(11), pages 1-20, June.
    11. 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.
    12. Gianluca Brando & Domenico Pietro Coiro & Marino Coppola & Adolfo Dannier & Andrea Del Pizzo & Ivan Spina, 2016. "Perturb and Observe Control for an Embedded Point Pivoted Absorber," Energies, MDPI, vol. 9(11), pages 1-14, November.
    13. Shahrooz Hajighorbani & Mohd Amran Mohd Radzi & Mohd Zainal Abidin Ab Kadir & Suhaidi Shafie, 2015. "Dual Search Maximum Power Point (DSMPP) Algorithm Based on Mathematical Analysis under Shaded Conditions," Energies, MDPI, vol. 8(10), pages 1-31, October.
    14. Flavio Ciccarelli & Luigi Pio Di Noia & Renato Rizzo, 2018. "Integration of Photovoltaic Plants and Supercapacitors in Tramway Power Systems," Energies, MDPI, vol. 11(2), pages 1-14, February.
    15. 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.
    16. Li, Qiyu & Zhao, Shengdun & Wang, Mengqi & Zou, Zhongyue & Wang, Bin & Chen, Qixu, 2017. "An improved perturbation and observation maximum power point tracking algorithm based on a PV module four-parameter model for higher efficiency," Applied Energy, Elsevier, vol. 195(C), pages 523-537.
    17. Slimane Hadji & Jean-Paul Gaubert & Fateh Krim, 2018. "Real-Time Genetic Algorithms-Based MPPT: Study and Comparison (Theoretical an Experimental) with Conventional Methods," Energies, MDPI, vol. 11(2), pages 1-17, February.

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