Model Predictive Wind Turbine Control with Move-Blocking Strategy for Load Alleviation and Power Leveling

Jassmann, Uwe; Dickler, Sebastian; Zierath, János; Hakenberg, Mathias; Abel, Dirk

doi:10.1088/1742-6596/753/5/052021

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…coriolis force blade i F coupl,edge edge-wise internal forces F coupl,flap flap-wise internal forces w g,edge,i = sin(ψ i ) • cos(ϑ i ) ∼ F g,edge,i sufficiently models F g,edge,i . The global thrust force F T and the aerodynamic torque T P [3] on the other hand are calculated by…”

Section: Static Nonlinear Functionmentioning

confidence: 99%

“…The 'LS' submodel's states x LS are used for identifying the structural dynamics and therefore contain the essential WT parts. The choice of states is based on a reduced WT description as it is used mostly in the state of the art [1,3,4,5], consisting of a reduced drivetrain and often a tower-model. It is to mention, that neither in the state of the art nor in a general description of multibody dynamics, accelerations are considered as system states.…”

Section: Linear Dynamic Systemmentioning

confidence: 99%

“…As those state estimators are model-based, the system model of a WT is a crucial part. Most often, reduced order WT models are derived for model-based control systems [1,3,4,5]. These contain drivetrain models with one or two degrees of freedom, which do not represent the sought individual rotor blade deflections.…”

Section: Introductionmentioning

confidence: 99%

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Data-driven state estimation toward blade individual load-reduction of wind turbines¹

Klein

Konrad

Abel

2022

J. Phys.: Conf. Ser.

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As wind turbines tend to be build larger every year, the importance of load-reducing controls to damp the rotor blade oscillations increases. For a control algorithm to perform blade individual load-reducing strategies, online information about each rotor blade’s deflection is needed. An approach for wind turbine state estimation with special attention to the rotor blade deflections is given using a Hammerstein model driven by simulation data in a Kalman Filter (KF). The Hammerstein’s linear submodel is mainly derived as time discrete state space system via least squares, while nonlinear effects are defined in terms of their proportionality. The model noise definition required by the KF is calculated by the system model’s one-step prediction error. The simulation results show the estimate to have a standard deviation of less than 2 % in edge and 9% in flap-wise direction when using an appropriate sensor configuration. Using state-of-the-art sensors, standard deviations of less than 4% and 25% respectively are achieved. The approach is particularly attractive due to the low level of tuning required. The usage of the state estimation in a closed control loop as well as the Hammerstein model in a model-based control system offer room for promising further investigations.

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Section: Static Nonlinear Functionmentioning

confidence: 99%

Section: Linear Dynamic Systemmentioning

confidence: 99%

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Data-driven state estimation toward blade individual load-reduction of wind turbines¹

Klein

Konrad

Abel

2022

J. Phys.: Conf. Ser.

Self Cite

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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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Herausforderungen bei der praktischen Umsetzung von modellbasierter prädiktiven Regelung von Windenergieanlagen

Wintermeyer-Kallen

Basler

Konrad

et al. 2023

Forsch Ingenieurwes

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Wind energy plays a significant role in renewable energies. The increasing demand for wind energy has caused wind turbines (WT) to grow steadily larger, which means that the control objectives are no longer solely to maximize the energy produced but to control mechanical loads, among other objectives actively. Model-based WT control, particularly model predictive control (MPC), has been the focus of research for the last decades. Nevertheless, only a few practical investigations of MPC for WTs in field tests exist.This paper highlights some key challenges and pitfalls when applying MPC for WTs. We render these critical points based on the experience of a recently conducted field test and discuss possible solutions for these challenges. In doing so, we highlight the following three critical areas: Firstly, we show how the design and practical operation of an MPC system can take into account the nonlinear properties of the WT. In particular, we address the highly varying sensitivity to the pitch angle and the dynamic responses of the rotor speed and mechanical loads to the actuator commands over the partial and full load ranges. Secondly, we discuss the problem of having limited computational capacities on real-time platforms, restricting the possible complexity of the MPC algorithm. Lastly, we show how some safety aspects decisively influence the design and operation of the control algorithm.

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Model Predictive Wind Turbine Control with Move-Blocking Strategy for Load Alleviation and Power Leveling

Cited by 6 publications

References 18 publications

Data-driven state estimation toward blade individual load-reduction of wind turbines¹

Data-driven state estimation toward blade individual load-reduction of wind turbines¹

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

Herausforderungen bei der praktischen Umsetzung von modellbasierter prädiktiven Regelung von Windenergieanlagen

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Model Predictive Wind Turbine Control with Move-Blocking Strategy for Load Alleviation and Power Leveling

Cited by 6 publications

References 18 publications

Data-driven state estimation toward blade individual load-reduction of wind turbines1

Data-driven state estimation toward blade individual load-reduction of wind turbines1

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

Herausforderungen bei der praktischen Umsetzung von modellbasierter prädiktiven Regelung von Windenergieanlagen

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Product

Resources

About

Data-driven state estimation toward blade individual load-reduction of wind turbines¹

Data-driven state estimation toward blade individual load-reduction of wind turbines¹