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Designing Localized MPPT for PV Systems Using Fuzzy-Weighted Extreme Learning Machine

Author

Listed:
  • Yang Du

    (Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China)

  • Ke Yan

    (College of Information Engineering, China Jiliang University, Hangzhou 310018, China)

  • Zixiao Ren

    (Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China)

  • Weidong Xiao

    (School of Electrical and Information Engineering, The University of Sydney, Western Avenue, Sydney, NSW 2006, Australia)

Abstract

A maximum power point tracker (MPPT) should be designed to deal with various weather conditions, which are different from region to region. Customization is an important step for achieving the highest solar energy harvest. The latest development of modern machine learning provides the possibility to classify the weather types automatically and, consequently, assist localized MPPT design. In this study, a localized MPPT algorithm is developed, which is supported by a supervised weather-type classification system. Two classical machine learning technologies are employed and compared, namely, the support vector machine (SVM) and extreme learning machine (ELM). The simulation results show the outperformance of the proposed method in comparison with the traditional MPPT design.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2615-:d:173152
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    References listed on IDEAS

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    Cited by:

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    2. Grażyna Frydrychowicz-Jastrzębska & Artur Bugała, 2021. "Solar Tracking System with New Hybrid Control in Energy Production Optimization from Photovoltaic Conversion for Polish Climatic Conditions," Energies, MDPI, vol. 14(10), pages 1-26, May.
    3. Ke Yan & Xudong Wang & Yang Du & Ning Jin & Haichao Huang & Hangxia Zhou, 2018. "Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy," Energies, MDPI, vol. 11(11), pages 1-15, November.
    4. Fabio Corti & Antonino Laudani & Gabriele Maria Lozito & Alberto Reatti, 2020. "Computationally Efficient Modeling of DC-DC Converters for PV Applications," Energies, MDPI, vol. 13(19), pages 1-18, September.
    5. Andrés Tobón & Julián Peláez-Restrepo & Jhon Montano & Mariana Durango & Jorge Herrera & Asier Ibeas, 2020. "MPPT of a Photovoltaic Panels Array with Partial Shading Using the IPSM with Implementation Both in Simulation as in Hardware," Energies, MDPI, vol. 13(4), pages 1-17, February.
    6. Yinxiao Zhu & Moon Keun Kim & Huiqing Wen, 2018. "Simulation and Analysis of Perturbation and Observation-Based Self-Adaptable Step Size Maximum Power Point Tracking Strategy with Low Power Loss for Photovoltaics," Energies, MDPI, vol. 12(1), pages 1-20, December.
    7. Ke Yan & Yuting Dai & Meiling Xu & Yuchang Mo, 2019. "Tunnel Surface Settlement Forecasting with Ensemble Learning," Sustainability, MDPI, vol. 12(1), pages 1-11, December.
    8. Luigi Costanzo & Massimo Vitelli, 2019. "A Novel MPPT Technique for Single Stage Grid-Connected PV Systems: T4S," Energies, MDPI, vol. 12(23), pages 1-13, November.
    9. Ahmed Al Mansur & Md. Ruhul Amin & Kazi Khairul Islam, 2019. "Performance Comparison of Mismatch Power Loss Minimization Techniques in Series-Parallel PV Array Configurations," Energies, MDPI, vol. 12(5), pages 1-21, March.
    10. 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.
    11. 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.

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