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Improved Virtual Inertia of PMSG-Based Wind Turbines Based on Multi-Objective Model-Predictive Control

Author

Listed:
  • Shiyao Qin

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China
    China Electric Power Research Institute, Beijing 100192, China)

  • Yuyang Chang

    (School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China)

  • Zhen Xie

    (School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China)

  • Shaolin Li

    (China Electric Power Research Institute, Beijing 100192, China)

Abstract

In the case of a high penetration rate of wind energy conversion systems, the conventional virtual inertia control of permanent magnet synchronous generators (PMSG) has an insufficient support capability for system frequency, leading to an unstable system frequency and a slower response. Considering the finite control set model predictive control has multi-objective regulation capabilities and efficient tracking capabilities, and an improved multi-objective model-predictive control is proposed in this paper for PMSG-based wind turbines with virtual inertia based on its mathematical model. With the prediction model, the optimal control of the current and the frequency of the PMSG-based wind turbines can be obtained. Since the shaft torque changes rapidly under high virtual inertia, shaft oscillation may occur under this scenario. To address this problem, the electromagnetic torque is set as an additional optimization objective, which effectively suppresses the oscillation. Furthermore, based on accurate short-term wind speed forecasting, a dynamic weight coefficient strategy is proposed, which can reasonably distribute the weight coefficients according to the working conditions. Finally, simulations are carried out on a 2 MW PMSG-based wind turbine platform, and the effectiveness of the proposed control strategies is verified.

Suggested Citation

  • Shiyao Qin & Yuyang Chang & Zhen Xie & Shaolin Li, 2021. "Improved Virtual Inertia of PMSG-Based Wind Turbines Based on Multi-Objective Model-Predictive Control," Energies, MDPI, vol. 14(12), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3612-:d:576749
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    References listed on IDEAS

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    1. Mojtaba Nasiri & Saleh Mobayen & Behdad Faridpak & Afef Fekih & Arthur Chang, 2020. "Small-Signal Modeling of PMSG-Based Wind Turbine for Low Voltage Ride-Through and Artificial Intelligent Studies," Energies, MDPI, vol. 13(24), pages 1-18, December.
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    Cited by:

    1. Amina Mseddi & Omar Naifar & Mohamed Rhaima & Lassaad Mchiri & Abdellatif Ben Makhlouf, 2023. "Robust Control for Torque Minimization in Wind Hybrid Generators: An H ∞ Approach," Mathematics, MDPI, vol. 11(16), pages 1-23, August.
    2. Jonglak Pahasa & Potejanasak Potejana & Issarachai Ngamroo, 2021. "Multi-Objective Decentralized Model Predictive Control for Inverter Air Conditioner Control of Indoor Temperature and Frequency Stabilization in Microgrid," Energies, MDPI, vol. 14(21), pages 1-22, October.
    3. 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.

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