IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i4p2015-d1072402.html
   My bibliography  Save this article

Regional Load Frequency Control of BP-PI Wind Power Generation Based on Particle Swarm Optimization

Author

Listed:
  • Jikai Sun

    (College of Electrical Engineering, Qingdao University, Qingdao 266071, China)

  • Mingrui Chen

    (School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Linghe Kong

    (College of Electrical Engineering, Qingdao University, Qingdao 266071, China)

  • Zhijian Hu

    (School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore)

  • Veerapandiyan Veerasamy

    (School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore)

Abstract

The large-scale integration of wind turbines (WTs) in renewable power generation induces power oscillations, leading to frequency aberration due to power unbalance. Hence, in this paper, a secondary frequency control strategy called load frequency control (LFC) for power systems with wind turbine participation is proposed. Specifically, a backpropagation (BP)-trained neural network-based PI control approach is adopted to optimize the conventional PI controller to achieve better adaptiveness. The proposed controller was developed to realize the timely adjustment of PI parameters during unforeseen changes in system operation, to ensure the mutual coordination among wind turbine control circuits. In the meantime, the improved particle swarm optimization (IPSO) algorithm is utilized to adjust the initial neuron weights of the neural network, which can effectively improve the convergence of optimization. The simulation results demonstrate that the proposed IPSO-BP-PI controller performed evidently better than the conventional PI controller in the case of random load disturbance, with a significant reduction to near 10 s in regulation time and a final stable error of less than 10 − 3 for load frequency. Additionally, compared with the conventional PI controller counterpart, the frequency adjustment rate of the IPSO-BP-PI controller is significantly improved. Furthermore, it achieves higher control accuracy and robustness, demonstrating better integration of wind energy into traditional power systems.

Suggested Citation

  • Jikai Sun & Mingrui Chen & Linghe Kong & Zhijian Hu & Veerapandiyan Veerasamy, 2023. "Regional Load Frequency Control of BP-PI Wind Power Generation Based on Particle Swarm Optimization," Energies, MDPI, vol. 16(4), pages 1-15, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:2015-:d:1072402
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/4/2015/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/4/2015/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mohamed Mokhtar & Mostafa I. Marei & Mariam A. Sameh & Mahmoud A. Attia, 2022. "An Adaptive Load Frequency Control for Power Systems with Renewable Energy Sources," Energies, MDPI, vol. 15(2), pages 1-22, January.
    2. Mokhtar Shouran & Fatih Anayi & Michael Packianather & Monier Habil, 2022. "Different Fuzzy Control Configurations Tuned by the Bees Algorithm for LFC of Two-Area Power System," Energies, MDPI, vol. 15(2), pages 1-39, January.
    3. Naser Azim Mohseni & Navid Bayati, 2022. "Robust Multi-Objective H 2 /H ∞ Load Frequency Control of Multi-Area Interconnected Power Systems Using TS Fuzzy Modeling by Considering Delay and Uncertainty," Energies, MDPI, vol. 15(15), pages 1-18, July.
    4. Md Jahidur Rahman & Tahar Tafticht & Mamadou Lamine Doumbia & Ntumba Marc-Alain Mutombo, 2021. "Dynamic Stability of Wind Power Flow and Network Frequency for a High Penetration Wind-Based Energy Storage System Using Fuzzy Logic Controller," Energies, MDPI, vol. 14(14), pages 1-18, July.
    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. Bashar Abbas Fadheel & Noor Izzri Abdul Wahab & Ali Jafer Mahdi & Manoharan Premkumar & Mohd Amran Bin Mohd Radzi & Azura Binti Che Soh & Veerapandiyan Veerasamy & Andrew Xavier Raj Irudayaraj, 2023. "A Hybrid Grey Wolf Assisted-Sparrow Search Algorithm for Frequency Control of RE Integrated System," Energies, MDPI, vol. 16(3), pages 1-28, January.
    2. Md Jahidur Rahman & Tahar Tafticht & Mamadou Lamine Doumbia & Iqbal Messaïf, 2023. "Optimal Inverter Control Strategies for a PV Power Generation with Battery Storage System in Microgrid," Energies, MDPI, vol. 16(10), pages 1-36, May.
    3. Nandakumar Sundararaju & Arangarajan Vinayagam & Veerapandiyan Veerasamy & Gunasekaran Subramaniam, 2022. "A Chaotic Search-Based Hybrid Optimization Technique for Automatic Load Frequency Control of a Renewable Energy Integrated Power System," Sustainability, MDPI, vol. 14(9), pages 1-27, May.
    4. José Calixto Lopes & Thales Sousa, 2022. "Transmission System Electromechanical Stability Analysis with High Penetration of Renewable Generation and Battery Energy Storage System Application," Energies, MDPI, vol. 15(6), pages 1-23, March.
    5. Awadh Ba Wazir & Ahmed Althobiti & Abdullah A. Alhussainy & Sultan Alghamdi & Mahendiran Vellingiri & Thangam Palaniswamy & Muhyaddin Rawa, 2024. "A Comparative Study of Load Frequency Regulation for Multi-Area Interconnected Grids Using Integral Controller," Sustainability, MDPI, vol. 16(9), pages 1-53, May.
    6. Yicong Wang & Chang Liu & Ji Han & Haoyu Tan & Fangchao Ke & Dongyin Zhang & Cong Wei & Shihong Miao, 2022. "A Distributed Frequency Regulation Method for Multi-Area Power System Considering Optimization of Communication Structure," Energies, MDPI, vol. 15(18), pages 1-18, September.

    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:gam:jeners:v:16:y:2023:i:4:p:2015-:d:1072402. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.