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A High-Performance Adaptive Incremental Conductance MPPT Algorithm for Photovoltaic Systems

Author

Listed:
  • Chendi Li

    (School of Electric Power, South China University of Technology, Guangzhou 510640, China)

  • Yuanrui Chen

    (School of Electric Power, South China University of Technology, Guangzhou 510640, China)

  • Dongbao Zhou

    (School of Electric Power, South China University of Technology, Guangzhou 510640, China)

  • Junfeng Liu

    (School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China)

  • Jun Zeng

    (School of Electric Power, South China University of Technology, Guangzhou 510640, China)

Abstract

The output characteristics of photovoltaic (PV) arrays vary with the change of environment, and maximum power point (MPP) tracking (MPPT) techniques are thus employed to extract the peak power from PV arrays. Based on the analysis of existing MPPT methods, a novel incremental conductance (INC) MPPT algorithm is proposed with an adaptive variable step size. The proposed algorithm automatically regulates the step size to track the MPP through a step size adjustment coefficient, and a user predefined constant is unnecessary for the convergence of the MPPT method, thus simplifying the design of the PV system. A tuning method of initial step sizes is also presented, which is derived from the approximate linear relationship between the open-circuit voltage and MPP voltage. Compared with the conventional INC method, the proposed method can achieve faster dynamic response and better steady state performance simultaneously under the conditions of extreme irradiance changes. A Matlab/Simulink model and a 5 kW PV system prototype controlled by a digital signal controller (TMS320F28035) were established. Simulations and experimental results further validate the effectiveness of the proposed method.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:4:p:288-:d:68289
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    References listed on IDEAS

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    4. Po-Chen Cheng & Bo-Rei Peng & Yi-Hua Liu & Yu-Shan Cheng & Jia-Wei Huang, 2015. "Optimization of a Fuzzy-Logic-Control-Based MPPT Algorithm Using the Particle Swarm Optimization Technique," Energies, MDPI, vol. 8(6), pages 1-23, June.
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    Cited by:

    1. Hegazy Rezk & Mohammed Mazen Alhato & Mujahed Al-Dhaifallah & Soufiene Bouallègue, 2021. "A Sine Cosine Algorithm-Based Fractional MPPT for Thermoelectric Generation System," Sustainability, MDPI, vol. 13(21), pages 1-17, October.
    2. Jong-Chan Kim & Jun-Ho Huh & Jae-Sub Ko, 2019. "Improvement of MPPT Control Performance Using Fuzzy Control and VGPI in the PV System for Micro Grid," Sustainability, MDPI, vol. 11(21), pages 1-27, October.
    3. 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.
    4. Jose Miguel Espi & Jaime Castello, 2019. "A Novel Fast MPPT Strategy for High Efficiency PV Battery Chargers," Energies, MDPI, vol. 12(6), pages 1-16, March.
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    6. Eyal Amer & Alon Kuperman & Teuvo Suntio, 2019. "Direct Fixed-Step Maximum Power Point Tracking Algorithms with Adaptive Perturbation Frequency," Energies, MDPI, vol. 12(3), pages 1-16, January.
    7. Marco Balato & Carlo Petrarca, 2020. "The Impact of Reconfiguration on the Energy Performance of the Distributed Maximum Power Point Tracking Approach in PV Plants," Energies, MDPI, vol. 13(6), pages 1-19, March.
    8. Eneko Artetxe & Jokin Uralde & Oscar Barambones & Isidro Calvo & Imanol Martin, 2023. "Maximum Power Point Tracker Controller for Solar Photovoltaic Based on Reinforcement Learning Agent with a Digital Twin," Mathematics, MDPI, vol. 11(9), pages 1-21, May.
    9. 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.
    10. Pi-Yun Chen & Kuei-Hsiang Chao & Bo-Jyun Liao, 2018. "Joint Operation between a PSO-Based Global MPP Tracker and a PV Module Array Configuration Strategy under Shaded or Malfunctioning Conditions," Energies, MDPI, vol. 11(8), pages 1-16, August.
    11. Mohamed Derbeli & Cristian Napole & Oscar Barambones & Jesus Sanchez & Isidro Calvo & Pablo Fernández-Bustamante, 2021. "Maximum Power Point Tracking Techniques for Photovoltaic Panel: A Review and Experimental Applications," Energies, MDPI, vol. 14(22), pages 1-31, November.
    12. Bijan Rahmani & Weixing Li, 2016. "Proposing Wavelet-Based Low-Pass Filter and Input Filter to Improve Transient Response of Grid-Connected Photovoltaic Systems," Energies, MDPI, vol. 9(8), pages 1-15, August.
    13. Arsad, A.Z. & Zuhdi, A.W. Mahmood & Azhar, A.D. & Chau, C.F. & Ghazali, A., 2025. "Advancements in maximum power point tracking for solar charge controllers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 210(C).
    14. Mohamed Louzazni & Daniel Tudor Cotfas & Petru Adrian Cotfas, 2020. "Management and Performance Control Analysis of Hybrid Photovoltaic Energy Storage System under Variable Solar Irradiation," Energies, MDPI, vol. 13(12), pages 1-23, June.
    15. Jose Miguel Espi & Jaime Castello, 2019. "New Fast MPPT Method Based on a Power Slope Detector for Single Phase PV Inverters," Energies, MDPI, vol. 12(22), pages 1-20, November.
    16. Kuei-Hsiang Chao & Meng-Cheng Wu, 2016. "Global Maximum Power Point Tracking (MPPT) of a Photovoltaic Module Array Constructed through Improved Teaching-Learning-Based Optimization," Energies, MDPI, vol. 9(12), pages 1-18, November.
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