Wind Tunnel Testing of Subspace Predictive Repetitive Control for Variable Pitch Wind Turbines

Navalkar, Sachin T.; Solingen, Edwin van; Wingerden, Jan‐Willem van

doi:10.1109/tcst.2015.2399452

Cited by 55 publications

(38 citation statements)

References 30 publications

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“…The blades designed and analysed as above were mounted on the test turbine setup used previously in Navalkar et al (2015). As described in this reference, the blades are connected to the hub through pitch servomotors.…”

Section: Testing Environmentmentioning

confidence: 99%

“…LPV controller tuning using IFT has been explored and shown to work in the simulation environment (Navalkar and Van Wingerden, 2015). However, the computational burden and number of experiments required for tuning imply that this methodology is required to be modified to meet the demands of real-time control in the wind tunnel.…”

Section: Iterative Feedforward Tuning For Combined Pitch and Flap Conmentioning

confidence: 99%

“…Data-driven control of wind turbine loads has been demonstrated experimentally in Navalkar et al (2015), where online recursive system identification was combined with online controller synthesis for minimising the periodic turbine loads. However, such a controller would be required to retune itself at every instant the ambient wind conditions change.…”

Section: Introductionmentioning

confidence: 99%

“…However, such a controller would be required to retune itself at every instant the ambient wind conditions change. An alternative methodology for the data-driven alleviation of wind loads has been described in Navalkar and Van Wingerden (2015), and it employs the iterative feedback tuning (IFT) (Hjalmarsson, 2002) methodology to tune the gains of a fixed structure controller, hereby optimising a (convex) performance criterion. While IFT controllers have been used in the industry, they have typically been implemented to converge to linear-time-invariant controller structures (Gevers, 2002).…”

Section: Introductionmentioning

confidence: 99%

“…While IFT controllers have been used in the industry, they have typically been implemented to converge to linear-time-invariant controller structures (Gevers, 2002). The use of IFT for tuning the gains of time-varying controllers, as required for the current application, has been described in the literature (Navalkar and Van Wingerden, 2015) but not yet demonstrated in practice.…”

Section: Introductionmentioning

confidence: 99%

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Wind tunnel tests with combined pitch and free-floating flap control: data-driven iterative feedforward controller tuning

et al. 2016

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Abstract. Wind turbine load alleviation has traditionally been addressed in the literature using either full-span pitch control, which has limited bandwidth, or trailing-edge flap control, which typically shows low control authority due to actuation constraints. This paper combines both methods and demonstrates the feasibility and advantages of such a combined control strategy on a scaled prototype in a series of wind tunnel tests. The pitchable blades of the test turbine are instrumented with free-floating flaps close to the tip, designed such that they aerodynamically magnify the low stroke of high-bandwidth actuators. The additional degree of freedom leads to aeroelastic coupling with the blade flexible modes. The inertia of the flaps was tuned such that instability occurs just beyond the operational envelope of the wind turbine; the system can however be stabilised using collocated closed-loop control. A feedforward controller is shown to be capable of significant reduction of the deterministic loads of the turbine. Iterative feedforward tuning, in combination with a stabilising feedback controller, is used to optimise the controller online in an automated manner, to maximise load reduction. Since the system is non-linear, the controller gains vary with wind speed; this paper also shows that iterative feedforward tuning is capable of generating the optimal gain schedule online.

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Section: Testing Environmentmentioning

confidence: 99%

Section: Iterative Feedforward Tuning For Combined Pitch and Flap Conmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Wind tunnel tests with combined pitch and free-floating flap control: data-driven iterative feedforward controller tuning

et al. 2016

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Fault‐tolerant individual pitch control of floating offshore wind turbines via subspace predictive repetitive control

et al. 2021

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Individual pitch control (IPC) is an effective and widely used strategy to mitigate blade loads in wind turbines. However, conventional IPC fails to cope with blade and actuator faults, and this situation may lead to an emergency shutdown and increased maintenance costs. In this paper, a fault‐tolerant individual pitch control (FTIPC) scheme is developed to accommodate these faults in floating offshore wind turbines (FOWTs), based on a Subspace Predictive Repetitive Control (SPRC) approach. To fulfill this goal, an online subspace identification paradigm is implemented to derive a linear approximation of the FOWT system dynamics. Then, a repetitive control law is formulated to attain load mitigation under operating conditions, both in healthy and faulty conditions. Since the excitation noise used for the online subspace identification may interfere with the nominal power generation of the wind turbine, a novel excitation technique is developed to restrict excitation at specific frequencies. Results show that significant load reductions are achieved by FTIPC, while effectively accommodating blade and actuator faults and while restricting the energy of the persistently exciting control action.

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Weight-scheduling for linear time-variant model predictive wind turbine control toward field testing

Wintermeyer-Kallen

Dickler

Zierath³

et al. 2021

Forsch Ingenieurwes

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Modern multi-megawatt wind turbines require powerful control algorithms which consider several control objectives at the same time and respect process constraints. Model predictive control (MPC) is a promising control method and has been a research topic for years. So far, very few studies evaluated MPC algorithms in field tests. This work aims to prepare a real-time MPC system for a full-scale field test in a 3 MW wind turbine. To this end, we introduce a weight-scheduling scheme for a linear time-variant MPC in order to ensure control operation over the entire operating range from the partial to the full load range. We use a rapid control prototyping process, in particular with comprehensive software-in-the-loop (SiL) tests, in order to design and validate the MPC system for the field test.In this contribution, we present the implementation of the linear time-variant MPC with weight-scheduling to be tested in the field test. With the weight-scheduling for the optimization problem inside the MPC, we achieved good performance over the entire operating range of the wind turbine. In the SiL tests, the proposed MPC algorithm achieved loads, comparable to the baseline controller of the wind turbine and improved the reference tracking of the power output and the rotational speed. The proposed linear time-variant MPC with weight-scheduling is fully validated in the presented software-in-the-loop tests and is ready for full-scale field test in the 3 MW wind turbine. We present the experimental field test results of the introduced MPC system in a separated contribution.

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Wind Tunnel Testing of Subspace Predictive Repetitive Control for Variable Pitch Wind Turbines

Cited by 55 publications

References 30 publications

Wind tunnel tests with combined pitch and free-floating flap control: data-driven iterative feedforward controller tuning

Wind tunnel tests with combined pitch and free-floating flap control: data-driven iterative feedforward controller tuning

Fault‐tolerant individual pitch control of floating offshore wind turbines via subspace predictive repetitive control

Weight-scheduling for linear time-variant model predictive wind turbine control toward field testing

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