On wind turbine down-regulation control strategies and rotor speed set-point

Lio, Wai Hou; Mirzaei, Mahmood; Larsen, Gunner Chr.

doi:10.1088/1742-6596/1037/3/032040

Cited by 24 publications

(34 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Obviously, the derating takes place at suboptimal level on the physical surface of the wind turbine. In addition, wind turbine derating can be accomplished by modifying the rotor speed set point (Lio, Mirzaei, and Larsen 2018). Two levels of power output set points are common in industry to operate the wind turbine at low and near-rated wind speeds.…”

Section: Derated Operationmentioning

confidence: 99%

Distributed Wind Controls: A Research Roadmap for Microgrids, Infrastructure Resilience, and Controls Launchpad (MIRACL)

Reilly

Poudel

Krishnan

et al. 2021

View full text Add to dashboard Cite

Section: Derated Operationmentioning

confidence: 99%

Distributed Wind Controls: A Research Roadmap for Microgrids, Infrastructure Resilience, and Controls Launchpad (MIRACL)

Reilly

Poudel

Krishnan

et al. 2021

View full text Add to dashboard Cite

“…This set of operating quasi-steady points alters the created deficit and turbulence mixing downstream, influencing the downstream turbine operation and loading. Down-regulation can be achieved by either changing the generator torque or pitch angle or both [25]. For this study, the maximum rotational speed (Max-Ω) down-regulation is used, where the generator torque τ g,derated is modified as follows: τ g,derated = P derated /ω rated (2) where P derated is the power demand and ω rated is the rated rotor speed.…”

Section: Definition Of Training Set Data and Outputmentioning

confidence: 99%

T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine Case

et al. 2020

Self Cite

View full text Add to dashboard Cite

Wind farm load assessment is typically conducted using Computational Fluid Dynamics (CFD) or aeroelastic simulations, which need a lot of computer power. A number of applications, for example wind farm layout optimisation, turbine lifetime estimation and wind farm control, requires a simplified but sufficiently detailed model for computing the turbine fatigue load. In addition, the effect of turbine curtailment is particularly important in the calculation of the turbine loads. Therefore, this paper develops a fast and computationally efficient method for wind turbine load assessment in a wind farm, including the wake effects. In particular, the turbine fatigue loads are computed using a surrogate model that is based on the turbine operating condition, for example, power set-point and turbine location, and the ambient wind inflow information. The Turbine to Farm Loads (T2FL) surrogate model is constructed based on a set of high fidelity aeroelastic simulations, including the Dynamic Wake Meandering model and an artificial neural network that uses the Bayesian Regularisation (BR) and Levenberg–Marquardt (LM) algorithms. An ensemble model is used that outperforms model predictions of the BR and LM algorithms independently. Furthermore, a case study of a two turbine wind farm is demonstrated, where the turbine power set-point and fatigue loads can be optimised based on the proposed surrogate model. The results show that the downstream turbine producing more power than the upstream turbine is favourable for minimising the load. In addition, simulation results further demonstrate that the accumulated fatigue damage of turbines can be effectively distributed amongst the turbines in a wind farm using the power curtailment and the proposed surrogate model.

show abstract

“…Constant delta control is typically pursued via estimation of available power. This requires direct measurement of wind speed [18] or estimation thereof [1,19], with suitably slow estimator dynamics [1]. This may be considered a drawback [20].…”

Section: Control Function Name (Based On [9]) Mode In [1]mentioning

confidence: 99%

“…Mirzaei et al [19] proposed a model predictive control strategy for constant delta control optimization. This method required knowledge of the wind speed, for which Lio et al proposed the use of LiDAR [18].…”

Section: Constant Delta Controlmentioning

confidence: 99%

On Wind Turbine Power Delta Control

Elorza¹,

Calleja²,

Pujana-Arrese³

2019

Energies

View full text Add to dashboard Cite

One of the major challenges facing wind energy at the moment is its dependence on dispatchable energy sources to match power supply to demand and provide an adequate spinning reserve. There is no fundamental impediment for this to be done with wind energy when wind conditions are such that sufficient wind power is available. It is, in fact, common for wind farms to participate in primary and secondary frequency regulation via droop curves, curtailment, synthetic inertia, proportional de-loading, and delta control. However, although the literature presents several approaches to turbine-level control functions of this sort, it is not trivial to extract from it a readily industrializable set of algorithms. Said extraction, focused on delta control and the addition of our own contributions, is the purpose of this paper, where we propose an extension of popular torque and pitch control algorithms, which allows delta control without the wind speed observers used by other authors.

show abstract

On wind turbine down-regulation control strategies and rotor speed set-point

Cited by 24 publications

References 9 publications

Distributed Wind Controls: A Research Roadmap for Microgrids, Infrastructure Resilience, and Controls Launchpad (MIRACL)

Distributed Wind Controls: A Research Roadmap for Microgrids, Infrastructure Resilience, and Controls Launchpad (MIRACL)

T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine Case

On Wind Turbine Power Delta Control

Contact Info

Product

Resources

About