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PSO-Based Model Predictive Control for Load Frequency Regulation with Wind Turbines

Author

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  • Wei Fan

    (School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, 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

With the high penetration of wind turbines, many issues need to be addressed in relation to load frequency control (LFC) to ensure the stable operation of power grids. The particle swarm optimization-based model predictive control (PSO-MPC) approach is presented to address this issue in the context of LFC with the participation of wind turbines. The classical MPC model was modified to incorporate the particle swarm optimization algorithm for the power generation model to regulate the system frequency. In addition to addressing the unpredictability of wind turbine generation, the presented PSO-MPC strategy not only addresses the randomness of wind turbine generation, but also reduces the computation burden of traditional MPC. The simulation results validate the effectiveness and feasibility of the PSO-MPC approach as compared with other state-of-the-art strategies.

Suggested Citation

  • Wei Fan & Zhijian Hu & Veerapandiyan Veerasamy, 2022. "PSO-Based Model Predictive Control for Load Frequency Regulation with Wind Turbines," Energies, MDPI, vol. 15(21), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:8219-:d:962637
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    References listed on IDEAS

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    1. Ting-Hsuan Chien & Yu-Chuan Huang & Yuan-Yih Hsu, 2020. "Neural Network-Based Supplementary Frequency Controller for a DFIG Wind Farm," Energies, MDPI, vol. 13(20), pages 1-15, October.
    2. 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.
    3. Veerapandiyan Veerasamy & Noor Izzri Abdul Wahab & Rajeswari Ramachandran & Arangarajan Vinayagam & Mohammad Lutfi Othman & Hashim Hizam & Jeevitha Satheeshkumar, 2019. "Automatic Load Frequency Control of a Multi-Area Dynamic Interconnected Power System Using a Hybrid PSO-GSA-Tuned PID Controller," Sustainability, MDPI, vol. 11(24), pages 1-20, December.
    4. Muntasir A. Magzoub & Thamer Alquthami, 2022. "Optimal Design of Automatic Generation Control Based on Simulated Annealing in Interconnected Two-Area Power System Using Hybrid PID—Fuzzy Control," Energies, MDPI, vol. 15(4), pages 1-15, February.
    5. Ramesh Kumar Behara & Akshay Kumar Saha, 2022. "Artificial Intelligence Control System Applied in Smart Grid Integrated Doubly Fed Induction Generator-Based Wind Turbine: A Review," Energies, MDPI, vol. 15(17), pages 1-56, September.
    6. Xingkang Jin & Wen Tan & Yarong Zou & Zijian Wang, 2022. "Active Disturbance Rejection Control for Wind Turbine Fatigue Load," Energies, MDPI, vol. 15(17), pages 1-15, August.
    7. Sadeq D. Al-Majidi & Mohammed Kh. AL-Nussairi & Ali Jasim Mohammed & Adel Manaa Dakhil & Maysam F. Abbod & Hamed S. Al-Raweshidy, 2022. "Design of a Load Frequency Controller Based on an Optimal Neural Network," Energies, MDPI, vol. 15(17), pages 1-28, August.
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    Cited by:

    1. Shihao Xie & Yun Zeng & Jing Qian & Fanjie Yang & Youtao Li, 2023. "CPSOGSA Optimization Algorithm Driven Cascaded 3DOF-FOPID-FOPI Controller for Load Frequency Control of DFIG-Containing Interconnected Power System," Energies, MDPI, vol. 16(3), pages 1-18, January.
    2. Sadeq D. Al-Majidi & Hisham Dawood Salman Altai & Mohammed H. Lazim & Mohammed Kh. Al-Nussairi & Maysam F. Abbod & Hamed S. Al-Raweshidy, 2023. "Bacterial Foraging Algorithm for a Neural Network Learning Improvement in an Automatic Generation Controller," Energies, MDPI, vol. 16(6), pages 1-19, March.

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