IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v9y2016i4p288-d68289.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/9/4/288/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/9/4/288/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Sivakumar, P. & Abdul Kader, Abdullah & Kaliavaradhan, Yogeshraj & Arutchelvi, M., 2015. "Analysis and enhancement of PV efficiency with incremental conductance MPPT technique under non-linear loading conditions," Renewable Energy, Elsevier, vol. 81(C), pages 543-550.
    3. Kuei-Hsiang Chao, 2015. "A High Performance PSO-Based Global MPP Tracker for a PV Power Generation System," Energies, MDPI, vol. 8(7), pages 1-18, July.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Syed Zulqadar Hassan & Hui Li & Tariq Kamal & Uğur Arifoğlu & Sidra Mumtaz & Laiq Khan, 2017. "Neuro-Fuzzy Wavelet Based Adaptive MPPT Algorithm for Photovoltaic Systems," Energies, MDPI, vol. 10(3), pages 1-16, March.
    15. 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.
    16. Naoufel Zitouni & Rabiaa Gammoudi & Rim Attafi & Dhafer Mezgahni, 2023. "Developed and Intelligent Structure of a Control for PV Water Treatment System," Energies, MDPI, vol. 16(18), pages 1-30, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    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. 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.
    4. Hannan, M.A. & Ali, Jamal A. & Mohamed, Azah & Hussain, Aini, 2018. "Optimization techniques to enhance the performance of induction motor drives: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1611-1626.
    5. Yi Jin & Wenhui Hou & Guiqiang Li & Xiao Chen, 2017. "A Glowworm Swarm Optimization-Based Maximum Power Point Tracking for Photovoltaic/Thermal Systems under Non-Uniform Solar Irradiation and Temperature Distribution," Energies, MDPI, vol. 10(4), pages 1-13, April.
    6. 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.
    7. 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.
    8. G, Dileep. & Singh, S.N., 2017. "Selection of non-isolated DC-DC converters for solar photovoltaic system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 1230-1247.
    9. Sergio Ignacio Serna-Garcés & Daniel Gonzalez Montoya & Carlos Andres Ramos-Paja, 2016. "Sliding-Mode Control of a Charger/Discharger DC/DC Converter for DC-Bus Regulation in Renewable Power Systems," Energies, MDPI, vol. 9(4), pages 1-27, March.
    10. Alexandro Ortiz & Efrain Mendez & Israel Macias & Arturo Molina, 2022. "Earthquake Algorithm-Based Voltage Referenced MPPT Implementation through a Standardized Validation Frame," Energies, MDPI, vol. 15(23), pages 1-24, November.
    11. Li, Shaowu, 2016. "Linear equivalent models at the maximum power point based on variable weather parameters for photovoltaic cell," Applied Energy, Elsevier, vol. 182(C), pages 94-104.
    12. Moacyr A. G. de Brito & Victor A. Prado & Edson A. Batista & Marcos G. Alves & Carlos A. Canesin, 2021. "Design Procedure to Convert a Maximum Power Point Tracking Algorithm into a Loop Control System," Energies, MDPI, vol. 14(15), pages 1-17, July.
    13. 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.
    14. Jong-Chan Kim & Jun-Ho Huh & Jae-Sub Ko, 2020. "Optimization Design and Test Bed of Fuzzy Control Rule Base for PV System MPPT in Micro Grid," Sustainability, MDPI, vol. 12(9), pages 1-25, May.
    15. Yilmaz, Unal & Kircay, Ali & Borekci, Selim, 2018. "PV system fuzzy logic MPPT method and PI control as a charge controller," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 994-1001.
    16. Syed Zulqadar Hassan & Hui Li & Tariq Kamal & Uğur Arifoğlu & Sidra Mumtaz & Laiq Khan, 2017. "Neuro-Fuzzy Wavelet Based Adaptive MPPT Algorithm for Photovoltaic Systems," Energies, MDPI, vol. 10(3), pages 1-16, March.
    17. Sheik Mohammed, S. & Devaraj, D. & Imthias Ahamed, T.P., 2016. "A novel hybrid Maximum Power Point Tracking Technique using Perturb & Observe algorithm and Learning Automata for solar PV system," Energy, Elsevier, vol. 112(C), pages 1096-1106.
    18. Sidra Mumtaz & Saghir Ahmad & Laiq Khan & Saima Ali & Tariq Kamal & Syed Zulqadar Hassan, 2018. "Adaptive Feedback Linearization Based NeuroFuzzy Maximum Power Point Tracking for a Photovoltaic System," Energies, MDPI, vol. 11(3), pages 1-15, March.
    19. Hong, Ying-Yi & Beltran, Angelo A. & Paglinawan, Arnold C., 2018. "A robust design of maximum power point tracking using Taguchi method for stand-alone PV system," Applied Energy, Elsevier, vol. 211(C), pages 50-63.
    20. Gul Filiz Tchoketch Kebir & Cherif Larbes & Adrian Ilinca & Thameur Obeidi & Selma Tchoketch Kebir, 2018. "Study of the Intelligent Behavior of a Maximum Photovoltaic Energy Tracking Fuzzy Controller," Energies, MDPI, vol. 11(12), pages 1-20, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:9:y:2016:i:4:p:288-:d:68289. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.