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A Flexible Maximum Power Point Tracking Control Strategy Considering Both Conversion Efficiency and Power Fluctuation for Large-inertia Wind Turbines

Author

Listed:
  • Hongmin Meng

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Tingting Yang

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Ji-zhen Liu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Zhongwei Lin

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

Abstract

In wind turbine control, maximum power point tracking (MPPT) control is the main control mode for partial-load regimes. Efficiency potentiation of energy conversion and power smoothing are both two important control objectives in partial-load regime. However, on the one hand, low power fluctuation signifies inefficiency of energy conversion. On the other hand, enhancing efficiency may increase output power fluctuation as well. Thus the two objectives are contradictory and difficult to balance. This paper proposes a flexible MPPT control framework to improve the performance of both conversion efficiency and power smoothing, by adaptively compensating the torque reference value. The compensation was determined by a proposed model predictive control (MPC) method with dynamic weights in the cost function, which improved control performance. The computational burden of the MPC solver was reduced by transforming the cost function representation. Theoretical analysis proved the good stability and robustness. Simulation results showed that the proposed method not only kept efficiency at a high level, but also reduced power fluctuations as much as possible. Therefore, the proposed method could improve wind farm profits and power grid reliability.

Suggested Citation

  • Hongmin Meng & Tingting Yang & Ji-zhen Liu & Zhongwei Lin, 2017. "A Flexible Maximum Power Point Tracking Control Strategy Considering Both Conversion Efficiency and Power Fluctuation for Large-inertia Wind Turbines," Energies, MDPI, vol. 10(7), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:939-:d:103833
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    References listed on IDEAS

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    1. Kusiak, Andrew & Li, Wenyan & Song, Zhe, 2010. "Dynamic control of wind turbines," Renewable Energy, Elsevier, vol. 35(2), pages 456-463.
    2. Bououden, S. & Chadli, M. & Filali, S. & El Hajjaji, A., 2012. "Fuzzy model based multivariable predictive control of a variable speed wind turbine: LMI approach," Renewable Energy, Elsevier, vol. 37(1), pages 434-439.
    3. Silvio Simani, 2015. "Overview of Modelling and Advanced Control Strategies for Wind Turbine Systems," Energies, MDPI, vol. 8(12), pages 1-24, November.
    4. Jain, Achin & Schildbach, Georg & Fagiano, Lorenzo & Morari, Manfred, 2015. "On the design and tuning of linear model predictive control for wind turbines," Renewable Energy, Elsevier, vol. 80(C), pages 664-673.
    5. Dinh-Chung Phan & Shigeru Yamamoto, 2015. "Maximum Energy Output of a DFIG Wind Turbine Using an Improved MPPT-Curve Method," Energies, MDPI, vol. 8(10), pages 1-19, October.
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    Cited by:

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    2. Jae Woong Shim & Heejin Kim & Kyeon Hur, 2019. "Incorporating State-of-Charge Balancing into the Control of Energy Storage Systems for Smoothing Renewable Intermittency," Energies, MDPI, vol. 12(7), pages 1-13, March.

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