IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v263y2023ipas036054422202504x.html
   My bibliography  Save this article

A modified current sensorless approach for maximum power point tracking of partially shaded photovoltaic systems

Author

Listed:
  • Obeidi, Nabil
  • Kermadi, Mostefa
  • Belmadani, Bachir
  • Allag, Abdelkrim
  • Achour, Lazhar
  • Mesbahi, Nadhir
  • Mekhilef, Saad

Abstract

The present paper proposes a modified current sensorless approach to reduce the implementation cost of maximum power point tracking (MPPT) controller for photovoltaic (PV) systems under partial shading conditions (PSCs). The proposed scheme relies only on the input voltage signal without the need for a current sensor to track the global maximum power point (GMPP). This is performed using a predefined objective function derived from the mathematical model of the buck-boost converter with an adaptive step-size mechanism for quick convergence. In addition, a formula of lower and upper limit duty cycle values for every local peak is incorporated for fast detection of local power peaks. An extensive experimental study, using a hardware prototype composed of a buck-boost converter driven by a dSPACE DS1104, is carried out to validate the proposed control scheme. Experimental results show that the proposed controller is able to accurately track the GMPP with high speed under various PSCs scenarios. Additionally, the proposed scheme reduces the implementation cost by 27.95% compared to conventional MPPT techniques.

Suggested Citation

  • Obeidi, Nabil & Kermadi, Mostefa & Belmadani, Bachir & Allag, Abdelkrim & Achour, Lazhar & Mesbahi, Nadhir & Mekhilef, Saad, 2023. "A modified current sensorless approach for maximum power point tracking of partially shaded photovoltaic systems," Energy, Elsevier, vol. 263(PA).
  • Handle: RePEc:eee:energy:v:263:y:2023:i:pa:s036054422202504x
    DOI: 10.1016/j.energy.2022.125618
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422202504X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2022.125618?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Achour, Lazhar & Bouharkat, Malek & Assas, Ouarda & Behar, Omar, 2017. "Hybrid model for estimating monthly global solar radiation for the Southern of Algeria: (Case study: Tamanrasset, Algeria)," Energy, Elsevier, vol. 135(C), pages 526-539.
    2. Yang Du & Ke Yan & Zixiao Ren & Weidong Xiao, 2018. "Designing Localized MPPT for PV Systems Using Fuzzy-Weighted Extreme Learning Machine," Energies, MDPI, vol. 11(10), pages 1-10, October.
    3. Boukenoui, R. & Ghanes, M. & Barbot, J.-P. & Bradai, R. & Mellit, A. & Salhi, H., 2017. "Experimental assessment of Maximum Power Point Tracking methods for photovoltaic systems," Energy, Elsevier, vol. 132(C), pages 324-340.
    4. Chih-Chiang Hua & Yu-Jun Zhan, 2021. "A Hybrid Maximum Power Point Tracking Method without Oscillations in Steady-State for Photovoltaic Energy Systems," Energies, MDPI, vol. 14(18), pages 1-16, September.
    5. Jately, V. & Arora, S., 2017. "Development of a dual-tracking technique for extracting maximum power from PV systems under rapidly changing environmental conditions," Energy, Elsevier, vol. 133(C), pages 557-571.
    6. Rizzo, Santi Agatino & Scelba, Giacomo, 2015. "ANN based MPPT method for rapidly variable shading conditions," Applied Energy, Elsevier, vol. 145(C), pages 124-132.
    7. Ghasemi-Mobtaker, Hassan & Mostashari-Rad, Fatemeh & Saber, Zahra & Chau, Kwok-wing & Nabavi-Pelesaraei, Ashkan, 2020. "Application of photovoltaic system to modify energy use, environmental damages and cumulative exergy demand of two irrigation systems-A case study: Barley production of Iran," Renewable Energy, Elsevier, vol. 160(C), pages 1316-1334.
    8. Ishaque, Kashif & Salam, Zainal & Shamsudin, Amir & Amjad, Muhammad, 2012. "A direct control based maximum power point tracking method for photovoltaic system under partial shading conditions using particle swarm optimization algorithm," Applied Energy, Elsevier, vol. 99(C), pages 414-422.
    9. Nabavi-Pelesaraei, Ashkan & Azadi, Hossein & Van Passel, Steven & Saber, Zahra & Hosseini-Fashami, Fatemeh & Mostashari-Rad, Fatemeh & Ghasemi-Mobtaker, Hassan, 2021. "Prospects of solar systems in production chain of sunflower oil using cold press method with concentrating energy and life cycle assessment," Energy, Elsevier, vol. 223(C).
    10. Kermadi, Mostefa & Berkouk, El Madjid, 2017. "Artificial intelligence-based maximum power point tracking controllers for Photovoltaic systems: Comparative study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 369-386.
    11. Karami, Nabil & Moubayed, Nazih & Outbib, Rachid, 2017. "General review and classification of different MPPT Techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 1-18.
    Full references (including those not matched with items on IDEAS)

    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. Nabil Obeidi & Mostefa Kermadi & Bachir Belmadani & Abdelkarim Allag & Lazhar Achour & Saad Mekhilef, 2022. "A Current Sensorless Control of Buck-Boost Converter for Maximum Power Point Tracking in Photovoltaic Applications," Energies, MDPI, vol. 15(20), pages 1-21, October.
    2. Belhachat, Faiza & Larbes, Cherif, 2017. "Global maximum power point tracking based on ANFIS approach for PV array configurations under partial shading conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 875-889.
    3. Rezk, Hegazy & AL-Oran, Mazen & Gomaa, Mohamed R. & Tolba, Mohamed A. & Fathy, Ahmed & Abdelkareem, Mohammad Ali & Olabi, A.G. & El-Sayed, Abou Hashema M., 2019. "A novel statistical performance evaluation of most modern optimization-based global MPPT techniques for partially shaded PV system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    4. Abderrazek Saoudi & Saber Krim & Mohamed Faouzi Mimouni, 2021. "Enhanced Intelligent Closed Loop Direct Torque and Flux Control of Induction Motor for Standalone Photovoltaic Water Pumping System," Energies, MDPI, vol. 14(24), pages 1-21, December.
    5. Guo, Lei & Meng, Zhuo & Sun, Yize & Wang, Libiao, 2018. "A modified cat swarm optimization based maximum power point tracking method for photovoltaic system under partially shaded condition," Energy, Elsevier, vol. 144(C), pages 501-514.
    6. Pantua, Conrad Allan Jay & Calautit, John Kaiser & Wu, Yupeng, 2021. "Sustainability and structural resilience of building integrated photovoltaics subjected to typhoon strength winds," Applied Energy, Elsevier, vol. 301(C).
    7. Ham, Jeonggyun & Shin, Yunchan & Cho, Honghyun, 2022. "Comparison of thermal performance between a surface and a volumetric absorption solar collector using water and Fe3O4 nanofluid," Energy, Elsevier, vol. 239(PC).
    8. Hong, Ying-Yi & Pula, Rolando A., 2022. "Detection and classification of faults in photovoltaic arrays using a 3D convolutional neural network," Energy, Elsevier, vol. 246(C).
    9. Abu Eldahab, Yasser E. & Saad, Naggar H. & Zekry, Abdalhalim, 2017. "Enhancing the tracking techniques for the global maximum power point under partial shading conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1173-1183.
    10. Hu, Yusha & Li, Jigeng & Hong, Mengna & Ren, Jingzheng & Man, Yi, 2022. "Industrial artificial intelligence based energy management system: Integrated framework for electricity load forecasting and fault prediction," Energy, Elsevier, vol. 244(PB).
    11. Llera, Rocio & Vigil, Miguel & Díaz-Díaz, Sara & Martínez Huerta, Gemma Marta, 2022. "Prospective environmental and techno-economic assessment of steam production by means of heat pipes in the steel industry," Energy, Elsevier, vol. 239(PD).
    12. Nubia Ilia Ponce de León Puig & Leonardo Acho & José Rodellar, 2018. "Design and Experimental Implementation of a Hysteresis Algorithm to Optimize the Maximum Power Point Extracted from a Photovoltaic System," Energies, MDPI, vol. 11(7), pages 1-24, July.
    13. Xu, Liang & Liu, Yangyang & Bai, Wenshuai & Tan, Zhaoyang & Xue, Wei, 2022. "Design and control of energy-saving double side-stream extractive distillation for the benzene/isopropanol/water separation," Energy, Elsevier, vol. 239(PA).
    14. PraveenKumar, Seepana & Agyekum, Ephraim Bonah & Kumar, Abhinav & Velkin, Vladimir Ivanovich, 2023. "Performance evaluation with low-cost aluminum reflectors and phase change material integrated to solar PV modules using natural air convection: An experimental investigation," Energy, Elsevier, vol. 266(C).
    15. Nisar, Shahida & Benbi, Dinesh Kumar & Toor, Amardeep Singh, 2021. "Energy budgeting and carbon footprints of three tillage systems in maize-wheat sequence of north-western Indo-Gangetic Plains," Energy, Elsevier, vol. 229(C).
    16. Jately, Vibhu & Azzopardi, Brian & Joshi, Jyoti & Venkateswaran V, Balaji & Sharma, Abhinav & Arora, Sudha, 2021. "Experimental Analysis of hill-climbing MPPT algorithms under low irradiance levels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    17. Amir, A. & Amir, A. & Selvaraj, J. & Rahim, N.A., 2016. "Study of the MPP tracking algorithms: Focusing the numerical method techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 350-371.
    18. Zhu, Y. & Wei, Z. & Li, Y.X. & Du, H.X. & Guo, Y., 2022. "Energy and atmosphere system planning of coal-dependent cities based on an interval minimax-regret coupled joint-probabilistic cost-benefit approach," Energy, Elsevier, vol. 239(PB).
    19. Su, Zixiang & Yang, Liu, 2022. "Peak shaving strategy for renewable hybrid system driven by solar and radiative cooling integrating carbon capture and sewage treatment," Renewable Energy, Elsevier, vol. 197(C), pages 1115-1132.
    20. Vo Thanh, Hung & Lee, Kang-Kun, 2022. "Application of machine learning to predict CO2 trapping performance in deep saline aquifers," Energy, Elsevier, vol. 239(PE).

    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:eee:energy:v:263:y:2023:i:pa:s036054422202504x. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.