IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v239y2025ics0960148125000011.html
   My bibliography  Save this article

A robust MPPT method based on optimizable Gaussian process regression and high order sliding mode control for solar systems under partial shading conditions

Author

Listed:
  • Yılmaz, Mehmet
  • Çorapsız, Muhammed Fatih

Abstract

This study proposes a new hybrid approach for maximum power point tracking (MPPT) under partial shading conditions. The approach consists of a high order sliding mode controller (HOSMC) and a hyperparameters optimized Gaussian Process Regression (GPR). The proposed hybrid method consists of two stages. In the first stage, the model created from real-time data was trained using optimized hyperparameters GPR method, one of the machine learning methods. Moreover, at this phase, a model is constructed utilizing temperature and irradiance values as inputs, with the output being the voltage value. Using this model, the reference voltage values required for HOSMC in different scenarios were generated. In the second stage, the error expression was obtained by comparing the generated reference voltage value with the PV input voltage value. The control signal is generated to minimize the error value and MPPT is performed. The Incremental Conductance (INC) algorithm, a traditional optimization technique, and the Gray Wolf Optimization (GWO), a metaheuristic optimization algorithm, were used to compare the suggested hybrid method in order to show its efficacy. Data from different months and seasons (February, May, August and October) were used to determine the scenarios. The irradiance and temperature values used in the scenarios were obtained from real-time data. Simulation studies were carried out using Matlab/Simulink. When the proposed hybrid method was compared with the INC and GWO algorithms, it was seen that it had the fastest convergence time and the largest efficiency value in all scenarios.

Suggested Citation

  • Yılmaz, Mehmet & Çorapsız, Muhammed Fatih, 2025. "A robust MPPT method based on optimizable Gaussian process regression and high order sliding mode control for solar systems under partial shading conditions," Renewable Energy, Elsevier, vol. 239(C).
    • Handle: RePEc:eee:renene:v:239:y:2025:i:c:s0960148125000011
    DOI: 10.1016/j.renene.2025.122339
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148125000011
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2025.122339?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. Zhang, Yuhu & Ren, Jing & Pu, Yanru & Wang, Peng, 2020. "Solar energy potential assessment: A framework to integrate geographic, technological, and economic indices for a potential analysis," Renewable Energy, Elsevier, vol. 149(C), pages 577-586.
    2. Rizzo, Santi Agatino & Scelba, Giacomo, 2015. "ANN based MPPT method for rapidly variable shading conditions," Applied Energy, Elsevier, vol. 145(C), pages 124-132.
    3. Yılmaz, Mehmet & Kaleli, Alirıza & Çorapsız, Muhammed Fatih, 2023. "Machine learning based dynamic super twisting sliding mode controller for increase speed and accuracy of MPPT using real-time data under PSCs," Renewable Energy, Elsevier, vol. 219(P1).
    4. Habib Benbouhenni & Nicu Bizon, 2021. "Third-Order Sliding Mode Applied to the Direct Field-Oriented Control of the Asynchronous Generator for Variable-Speed Contra-Rotating Wind Turbine Generation Systems," Energies, MDPI, vol. 14(18), pages 1-20, September.
    5. Eltamaly, Ali M. & Al-Saud, M.S. & Abokhalil, Ahmed G. & Farh, Hassan M.H., 2020. "Simulation and experimental validation of fast adaptive particle swarm optimization strategy for photovoltaic global peak tracker under dynamic partial shading," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    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. Novie Ayub Windarko & Muhammad Nizar Habibi & Bambang Sumantri & Eka Prasetyono & Moh. Zaenal Efendi & Taufik, 2021. "A New MPPT Algorithm for Photovoltaic Power Generation under Uniform and Partial Shading Conditions," Energies, MDPI, vol. 14(2), pages 1-22, January.
    2. Mohamed Zaghloul-El Masry & Abdallah Mohammed & Fathy Amer & Roaa Mubarak, 2023. "New Hybrid MPPT Technique Including Artificial Intelligence and Traditional Techniques for Extracting the Global Maximum Power from Partially Shaded PV Systems," Sustainability, MDPI, vol. 15(14), pages 1-30, July.
    3. Adeel Feroz Mirza & Majad Mansoor & Qiang Ling & Muhammad Imran Khan & Omar M. Aldossary, 2020. "Advanced Variable Step Size Incremental Conductance MPPT for a Standalone PV System Utilizing a GA-Tuned PID Controller," Energies, MDPI, vol. 13(16), pages 1-25, August.
    4. Ahmed G. Abo-Khalil & Walied Alharbi & Abdel-Rahman Al-Qawasmi & Mohammad Alobaid & Ibrahim M. Alarifi, 2021. "Maximum Power Point Tracking of PV Systems under Partial Shading Conditions Based on Opposition-Based Learning Firefly Algorithm," Sustainability, MDPI, vol. 13(5), pages 1-18, March.
    5. Celikel, Resat & Yilmaz, Musa & Gundogdu, Ahmet, 2022. "A voltage scanning-based MPPT method for PV power systems under complex partial shading conditions," Renewable Energy, Elsevier, vol. 184(C), pages 361-373.
    6. 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.
    7. Wu, Yubo & Du, Jianqiang & Liu, Guangxin & Ma, Danzhu & Jia, Fengrui & Klemeš, Jiří Jaromír & Wang, Jin, 2022. "A review of self-cleaning technology to reduce dust and ice accumulation in photovoltaic power generation using superhydrophobic coating," Renewable Energy, Elsevier, vol. 185(C), pages 1034-1061.
    8. Başoğlu, Mustafa Engin & Çakır, Bekir, 2016. "Comparisons of MPPT performances of isolated and non-isolated DC–DC converters by using a new approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1100-1113.
    9. Arnulf Jäger-Waldau, 2020. "The Untapped Area Potential for Photovoltaic Power in the European Union," Clean Technol., MDPI, vol. 2(4), pages 1-7, October.
    10. Ali M. Eltamaly, 2021. "A Novel Strategy for Optimal PSO Control Parameters Determination for PV Energy Systems," Sustainability, MDPI, vol. 13(2), pages 1-28, January.
    11. Belkaid, A. & Colak, I. & Isik, O., 2016. "Photovoltaic maximum power point tracking under fast varying of solar radiation," Applied Energy, Elsevier, vol. 179(C), pages 523-530.
    12. Li, Senji & Chen, Zhenwu & Liu, Xing & Zhang, Xiaochun & Zhou, Yong & Gu, Wenbo & Ma, Tao, 2021. "Numerical simulation of a novel pavement integrated photovoltaic thermal (PIPVT) module," Applied Energy, Elsevier, vol. 283(C).
    13. 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.
    14. Abdul Hayy Haziq Mohamad & Muhamad Rias K. V. Zainuddin & Rossazana Ab-Rahim, 2023. "Does Renewable Energy Transition in the USA and China Overcome Environmental Degradation?," International Journal of Energy Economics and Policy, Econjournals, vol. 13(6), pages 234-243, November.
    15. Xiaotuan Li & Panfei Yang & Yuntao Zou, 2023. "An Empirical Investigation of the “Mezzogiorno Trap” in China’s Agricultural Economy: Insights from Data Envelopment Analysis (2015–2021)," Agriculture, MDPI, vol. 13(9), pages 1-30, September.
    16. Wang, Tiantian & Wang, Yanhua & Wang, Ke & Fu, Sha & Ding, Li, 2024. "Five-dimensional assessment of China's centralized and distributed photovoltaic potential: From solar irradiation to CO2 mitigation," Applied Energy, Elsevier, vol. 356(C).
    17. Naamane Debdouche & Brahim Deffaf & Habib Benbouhenni & Zarour Laid & Mohamed I. Mosaad, 2023. "Direct Power Control for Three-Level Multifunctional Voltage Source Inverter of PV Systems Using a Simplified Super-Twisting Algorithm," Energies, MDPI, vol. 16(10), pages 1-32, May.
    18. Majed A. Alotaibi & Ali M. Eltamaly, 2021. "A Smart Strategy for Sizing of Hybrid Renewable Energy System to Supply Remote Loads in Saudi Arabia," Energies, MDPI, vol. 14(21), pages 1-24, October.
    19. Ahmad M. A. Malkawi & Abdallah Odat & Ahmad Bashaireh, 2022. "A Novel PV Maximum Power Point Tracking Based on Solar Irradiance and Circuit Parameters Estimation," Sustainability, MDPI, vol. 14(13), pages 1-14, June.
    20. Tamir Shaqarin, 2023. "Particle Swarm Optimization with Targeted Position-Mutated Elitism (PSO-TPME) for Partially Shaded PV Systems," Sustainability, MDPI, vol. 15(5), pages 1-23, February.

    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:renene:v:239:y:2025:i:c:s0960148125000011. 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/renewable-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.