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Improved Perturb and Observation Method Based on Support Vector Regression

Author

Listed:
  • Bicheng Tan

    (School of Optical and Electronic Information, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China)

  • Xin Ke

    (School of Optical and Electronic Information, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China)

  • Dachuan Tang

    (School of Optical and Electronic Information, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China)

  • Sheng Yin

    (School of Optical and Electronic Information, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China)

Abstract

Solar energy is the most valuable renewable energy source due to its abundant storage and is pollution-free. The output power of photovoltaic (PV) arrays will vary with external conditions, such as irradiance and temperature fluctuations. Therefore, an increase in the energy conversion rate is inseparable from maximum power point tracking (MPPT). The existing MPPT technology cannot either balance the tracking speed and tracking accuracy, or the implementation cost is too high due to the complexity of the calculation. In this paper, a new maximum power point tracking (MPPT) method was proposed. It improves the traditional perturb and observation (P&O) method by introducing the support vector regression (SVR) algorithm. In this method, the current maximum power point voltage is predicted by the trained model and compared with the current operating voltage to predict a reasonable step size. The boost DC/ DC (Direct current-Direct current converter) convert system applying the improved method and the traditional P&O was simulated in MATLAB-Simulink, respectively. The results of the simulation show that compared with the traditional P&O method, the proposed new method both improves the convergence time and tracking accuracy.

Suggested Citation

  • Bicheng Tan & Xin Ke & Dachuan Tang & Sheng Yin, 2019. "Improved Perturb and Observation Method Based on Support Vector Regression," Energies, MDPI, vol. 12(6), pages 1-11, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:1151-:d:216911
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Aranzazu D. Martin & Juan M. Cano & Reyes S. Herrera & Jesus R. Vazquez, 2019. "Wireless Sliding MPPT Control of Photovoltaic Systems in Distributed Generation Systems," Energies, MDPI, vol. 12(17), pages 1-16, August.
    2. Can Ding & Yiyuan Zhou & Qingchang Ding & Kaiming Li, 2022. "Integrated Carbon-Capture-Based Low-Carbon Economic Dispatch of Power Systems Based on EEMD-LSTM-SVR Wind Power Forecasting," Energies, MDPI, vol. 15(5), pages 1-27, February.
    3. Lan Li & Hao Wang & Xiangping Chen & Abid Ali Shah Bukhari & Wenping Cao & Lun Chai & Bing Li, 2019. "High Efficiency Solar Power Generation with Improved Discontinuous Pulse Width Modulation (DPWM) Overmodulation Algorithms," Energies, MDPI, vol. 12(9), pages 1-18, May.
    4. N. Kanagaraj & Hegazy Rezk & Mohamed R. Gomaa, 2020. "A Variable Fractional Order Fuzzy Logic Control Based MPPT Technique for Improving Energy Conversion Efficiency of Thermoelectric Power Generator," Energies, MDPI, vol. 13(17), pages 1-18, September.
    5. Efrain Mendez & Alexandro Ortiz & Pedro Ponce & Israel Macias & David Balderas & Arturo Molina, 2020. "Improved MPPT Algorithm for Photovoltaic Systems Based on the Earthquake Optimization Algorithm," Energies, MDPI, vol. 13(12), pages 1-24, June.
    6. Amjad Ali & K. Almutairi & Muhammad Zeeshan Malik & Kashif Irshad & Vineet Tirth & Salem Algarni & Md. Hasan Zahir & Saiful Islam & Md Shafiullah & Neeraj Kumar Shukla, 2020. "Review of Online and Soft Computing Maximum Power Point Tracking Techniques under Non-Uniform Solar Irradiation Conditions," Energies, MDPI, vol. 13(12), pages 1-37, June.

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