Multivariable control strategy for variable speed, variable pitch wind turbines

Boukhezzar, Boubekeur; Lupu, L.; Siguerdidjane, Houria; Hand, M.

doi:10.1016/j.renene.2006.06.010

Cited by 324 publications

(203 citation statements)

References 8 publications

(11 reference statements)

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“…Our proposed controller has an intermediate behavior between Jonkman's and Boukhezzar's controllers. The electrical power follows the reference, independently of the wind fluctuations, with a settling time of one second, as can be expected [see Equation (10)] when using parameter K α = 1.5 × 10 6 . When parameter K α = 1.5 × 10 5 is used, similar results are obtained but the settling time is increased.…”

Section: (Bukhezzar's Controller) and [11] (Jonkman's Controller)supporting

confidence: 63%

“…Thus, the proposed controller more precisely reaches the desired power reference than exponentially stable controllers, such as [10].…”

Section: Resultsmentioning

confidence: 97%

“…λ For a perfectly rigid low-speed shaft, a single-mass model for a wind turbine can be considered [10,[15][16][17],…”

Section: System Modelingmentioning

confidence: 99%

“…Thus, our proposed controller used with Equation (11), to approximatė ω g , requires few WT parameters. By contrast, most of the torque controllers in the literature [10,[15][16][17] require many WT parameters, which restricts controller applicability when not all of the required parameters are available.…”

Section: Torque Controllermentioning

confidence: 99%

“…The pitch and torque controllers are then presented in Section 4. Finally, in Section 5, the proposed controllers are validated with the FAST aeroelastic wind turbine simulator and their performance is compared to the controllers proposed in [10,11] to highlight the improvements of the provided method.…”

Section: Introductionmentioning

confidence: 99%

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Power Control Design for Variable-Speed Wind Turbines

et al. 2012

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This paper considers power generation control in variable-speed variable-pitch horizontal-axis wind turbines operating at high wind speeds. A dynamic chattering torque control and a proportional integral (PI) pitch control strategy are proposed and validated using the National Renewable Energy Laboratory wind turbine simulator FAST (Fatigue, Aerodynamics, Structures, and Turbulence) code. Validation results show that the proposed controllers are effective for power regulation and demonstrate high-performances for all other state variables (turbine and generator rotational speeds; and smooth and adequate evolution of the control variables) for turbulent wind conditions. To highlight the improvements of the provided method, the proposed controllers are compared to relevant previously published studies.

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Section: (Bukhezzar's Controller) and [11] (Jonkman's Controller)supporting

confidence: 63%

“…Thus, the proposed controller more precisely reaches the desired power reference than exponentially stable controllers, such as [10].…”

Section: Resultsmentioning

confidence: 97%

“…λ For a perfectly rigid low-speed shaft, a single-mass model for a wind turbine can be considered [10,[15][16][17],…”

Section: System Modelingmentioning

confidence: 99%

Section: Torque Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Power Control Design for Variable-Speed Wind Turbines

et al. 2012

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Wind turbines sustainable power generation subject to sensor faults: Observer‐based MPC approach

Ghanbarpour

Bayat

Jalilvand

2019

Int Trans Electr Energ Syst

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Summary A novel approach for increasing the sustainability and reliability of energy conversion in a wind turbine (WT) system in the presence of uncertainties/disturbances and sensors fault is proposed. To this aim, an active robust fault tolerant model predictive controller (FT‐MPC) is designed for WTs under partial load condition by combining the advantages of the sliding mode observer (SMO) and MPC approaches. Since the WTs are subject to physical/constructive constraints, first, an online MPC is designed to guarantee all constraints satisfaction. Then, the consequences of sensors fault on the control system's performance are demonstrated. An efficient approach is proposed based on the SMO for estimating the sensors faults and the states simultaneously. The most significant contribution of the observer‐based FT‐MPC controller is that it is not only robust against uncertainties/disturbances but also using the proposed strategy the challenges of nonlinearity, constraints, and sensor faults can be effectively handled.

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