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MPPT Control Algorithm Based on Particle Swarm Optimization and Adaptive Linear Active Disturbance Rejection Control

Author

Listed:
  • Miao Zhang

    (School of Automation & Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China)

  • Keyu Zhuang

    (School of Automation & Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China)

  • Tong Zhao

    (School of Automation & Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China)

  • Jingze Xue

    (School of Automation & Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China)

  • Yunlong Gao

    (School of Automation & Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China)

  • Shuai Cui

    (School of Automation & Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China)

  • Zheng Qiao

    (School of Automation & Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China)

Abstract

Aiming at the problem of maximum power point tracking (MPPT) of photovoltaic arrays in photovoltaic power generation systems, a particle swarm optimization (PSO) MPPT method combined with adaptive linear active disturbance rejection control (A-LADRC) algorithm was proposed and designed. In this method, PSO is used to track the maximum power point (MPP), and then the A-LADRC controller was used to track the reference voltage. The controller enhances the anti-interference ability against various external disturbances in the MPPT process and accelerates the response speed of the system. Compared with the perturbation observation method (P&O), traditional PSO and LADRC, the proposed method has good tracking performance and an anti-interference ability under various external disturbances.

Suggested Citation

  • Miao Zhang & Keyu Zhuang & Tong Zhao & Jingze Xue & Yunlong Gao & Shuai Cui & Zheng Qiao, 2022. "MPPT Control Algorithm Based on Particle Swarm Optimization and Adaptive Linear Active Disturbance Rejection Control," Energies, MDPI, vol. 15(23), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:9091-:d:989640
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    References listed on IDEAS

    as
    1. Wafa Hayder & Dezso Sera & Emanuele Ogliari & Abderezak Lashab, 2022. "On Improved PSO and Neural Network P&O Methods for PV System under Shading and Various Atmospheric Conditions," Energies, MDPI, vol. 15(20), pages 1-15, October.
    2. Cheng Liu & Yanming Cheng & Dejun Liu & Guohua Cao & Ilkyoo Lee, 2020. "Research on a LADRC Strategy for Trajectory Tracking Control of Delta High-Speed Parallel Robots," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-12, August.
    3. Sampath Kumar Vankadara & Shamik Chatterjee & Praveen Kumar Balachandran & Lucian Mihet-Popa, 2022. "Marine Predator Algorithm (MPA)-Based MPPT Technique for Solar PV Systems under Partial Shading Conditions," Energies, MDPI, vol. 15(17), pages 1-16, August.
    4. Miao Zhang & Keyu Zhuang & Tong Zhao & Xianli Chen & Jingze Xue & Zheng Qiao & Shuai Cui & Yunlong Gao, 2022. "Bus Voltage Control of Photovoltaic Grid Connected Inverter Based on Adaptive Linear Active Disturbance Rejection," Energies, MDPI, vol. 15(15), pages 1-20, July.
    5. Kakosimos, Panagiotis E. & Kladas, Antonios G., 2011. "Implementation of photovoltaic array MPPT through fixed step predictive control technique," Renewable Energy, Elsevier, vol. 36(9), pages 2508-2514.
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