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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
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    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.
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