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A model-free adaptive controller with tracking error differential for collective pitching of wind turbines

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  • Li, Jianshen
  • Wang, Shuangxin
  • Li, Yaguang

Abstract

The aerodynamic power captured by wind turbines is generally limited by pitch control when it operates above rated wind speed. However, problems such as actuator constraints, unmeasured system states, and stochastic wind speed fluctuations, make it difficult to establish an accurate model of the controlled collective pitch system. This paper proposes an improved model-free adaptive controller (MFAC) by considering the differential of tracking error in cost function (MFAC with the differential of tracking error, DE-MFAC), to compensate for potential effect of the system dynamic characteristics under random disturbance. Meanwhile, a new parameter β is introduced to adjust the impact of the compensation module to alleviate the contradiction between rapidity and hypertonicity. Then, the bounded-input bounded-output (BIBO) stability and monotonic convergence of the tracking error are derived by the contraction mapping. Furthermore, a Simulink/FAST platform is established and simulated under various wind conditions. The results show that, compared with the baseline PI and the MFAC based on compact form dynamic linearization (CFDL-MFAC), the proposed DE-MFAC is capable of handling the time-delay and large inertia of collective pitch systems, which not only significantly makes improvements in robustness and dynamic response, but also limits the average fluctuating amplitude of generator power within 1%.

Suggested Citation

  • Li, Jianshen & Wang, Shuangxin & Li, Yaguang, 2020. "A model-free adaptive controller with tracking error differential for collective pitching of wind turbines," Renewable Energy, Elsevier, vol. 161(C), pages 435-447.
    • Handle: RePEc:eee:renene:v:161:y:2020:i:c:p:435-447
    DOI: 10.1016/j.renene.2020.06.140
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    References listed on IDEAS

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    1. Lasheen, Ahmed & Elshafei, Abdel Latif, 2016. "Wind-turbine collective-pitch control via a fuzzy predictive algorithm," Renewable Energy, Elsevier, vol. 87(P1), pages 298-306.
    2. Lasheen, Ahmed & Saad, Mohamed S. & Emara, Hassan M. & Elshafei, Abdel Latif, 2019. "Tube-based explicit model predictive output-feedback controller for collective pitching of wind turbines," Renewable Energy, Elsevier, vol. 131(C), pages 549-562.
    3. Dong Liu & Guang-Hong Yang, 2018. "Model-free adaptive control design for nonlinear discrete-time processes with reinforcement learning techniques," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(11), pages 2298-2308, August.
    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. Abdelbaky, Mohamed Abdelkarim & Liu, Xiangjie & Jiang, Di, 2020. "Design and implementation of partial offline fuzzy model-predictive pitch controller for large-scale wind-turbines," Renewable Energy, Elsevier, vol. 145(C), pages 981-996.
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    Cited by:

    1. Li, Jianshen & Wang, Shuangxin, 2021. "Dual multivariable model-free adaptive individual pitch control for load reduction in wind turbines with actuator faults," Renewable Energy, Elsevier, vol. 174(C), pages 293-304.

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