FarmConners wind farm flow control benchmark –   Part 1: Blind test results

Göçmen, Tuhfe; Campagnolo, Filippo; Duc, Thomas; Eguinoa, Irene; Andersen, Søren Juhl; Petrović, Vlaho; Imširović, Lejla; Braunbehrens, Robert; Liew, Jaime; Baungaard, Mads; Laan, P. van der; Qian, Guowei; Aparicio-Sanchez, Maria; González-Lope, Rubén; Dighe, Vinit V.; Becker, Marcus; Broek, Maarten J. van den; Wingerden, Jan‐Willem van; Stock, Adam; Cole, Matthew O. T.; Ruisi, Renzo; Bossanyi, Ervin; Requate, Niklas; Strnad, Simon; Schmidt, Jonas; Vollmer, Lukas; Sood, Ishaan; Meyers, Johan

doi:10.5194/wes-7-1791-2022

Cited by 12 publications

(14 citation statements)

References 79 publications

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“…Forecasting and modelling dynamics for large spatiotemporal regions impacts the stability of the electrical grid [24], where particular significant weather changes such as ramp events are challenging to model [25]. Similarly, capturing the correct dynamic wake interaction is crucial for developing robust wind farm control strategies with reduced uncertainties [2,26,27].…”

Section: Dynamics and Correlations Of Ps-rommentioning

confidence: 99%

“…However, the computational cost of LES is large and therefore prohibitive, as the simulated time is often limited to relative short periods of 10 − 30mins for high resolution wind farm flows. This naturally presents challenges when attempting to perform code validation against measurements [1,2] or when using LES to verify simpler dynamics models [3,4]. First and second order statistics of the simulations and the measurements might appear different although they could merely be different subsets from the same statistical distributions.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic wind farm flow generation using a reduced order model of LES

Andersen

León

2023

J. Phys.: Conf. Ser.

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Large eddy simulations are computationally expensive, which typically limits the simulation time and hence the statistics useful for validation. Here, a reduced order model with a stochastic engine is used to generate stochastic inflow to 48 wind turbines in the Lillgrund wind farm. The stochastic flow realization captures the power production and more importantly the correct cross-correlations in power and blade root flapwise bending moments over the large spatiotemporal region, which the wind farm covers. The analysis show how the cross-correlation increase further into the wind farm, where the highly turbulent flow becomes increasingly self-organized and how the dynamic interaction across the entire wind farm is captured by the first eight modes. Overall, the reduced order model captures the correct dynamics at a fraction of the computational cost, effectively generalizing the LES results beyond a relatively short simulation period.

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Section: Dynamics and Correlations Of Ps-rommentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stochastic wind farm flow generation using a reduced order model of LES

Andersen

León

2023

J. Phys.: Conf. Ser.

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“…Such benefits are achieved via optimised distribution of set-points among the turbines [6,7,8]. The core of these optimisations is a low-fidelity wind farm simulator, such as PyWake [9], FLORIS [10], and many more [11], where the performance of a wind farm configuration can be quickly estimated in the optimisation loop using either gradient-free or gradient-based methods [12]. Low-fidelity wind farm simulators are typically steady-state, and therefore unable to capture the dynamic nature of the wind farm.…”

Section: Introductionmentioning

confidence: 99%

Wind Farm Control Optimisation Under Load Constraints Via Surrogate Modelling

Liew,

Riva,

Friis-Møller

et al. 2024

J. Phys.: Conf. Ser.

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In the field of wind farm control, wake steering has shown the potential to increase the power output of a wind farm by deflecting wakes away from downstream turbines. However, in some wake steering scenarios, the fatigue damage experienced by the turbines can increase, particularly when the wakes partially overlap a downstream rotor. It is for this reason that fatigue load constraints should be introduced into the control optimisation process. Unfortunately, wind turbine loads are notoriously difficult to predict, requiring expensive aeroelastic simulations. In this study, we present a wind farm control optimisation with load constraints using surrogate models to estimate the fatigue damage of each turbine in a wind farm designed for maximum energy production. We use the state-of-the-art aeroelastic wind farm simulator, HAWC2Farm, to produce a comprehensive data set of fatigue loads, which is then used to train surrogate models for rapid execution during an optimisation loop. The inputs of the surrogate model are chosen using the most significant modes from a proper orthogonal decomposition. Artificial neural networks are used for the surrogate models, and the wind farm control optimisation is carried out using OpenMDAO. Finally, a wind farm control optimisation with load constraints using wake steering is performed. The presented methodology for surrogate modelling and control optimisation is significant to produce accurate set point optimisations for wind farms while recognising the implications to turbine fatigue loads.

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“…Finally, we propose a simple strategy for sensitivity mitigation, based on appropriately chosen optimisation constraints, which is effective even for this realistic and complex wake steering optimisation problem. To the best of the authors' knowledge, previous parametric investigations into wake optimisation sensitivity (Rak and Santos Pereira, 2022;Göçmen et al, 2022) have not studied the effect of optimiser class or analysed sensitivity from a statistical perspective, and have not presented possible strategies for sensitivity mitigation.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis of wake steering optimisation for wind farm power maximisation

Gori

Laizet²,

Wynn³

2023

Preprint

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Abstract. Modern large–scale wind farms consist of multiple turbines clustered together, usually in well–structured formations. Clustering has a number of drawbacks during a wind farm’s operation, as some of the downstream turbines will inevitably operate in the wake of those upstream, with a significant reduction in power output and an increase in fatigue loads. Wake steering, a control strategy in which upstream wind turbines are misaligned with the wind to redirect their wakes away from downstream turbines, is a promising strategy to mitigate power losses. The purpose of this work is to investigate the sensitivity of open-loop wake steering optimisation in which an internal predictive wake model is used to determine the farm power output as a function of the turbine yaw angles. Three different layouts are investigated with increasing levels of complexity. A simple 2×1 farm layout in aligned conditions is first considered, allowing for a careful investigation of sensitivity to wake models and operational set-points. A medium-complexity case of a generic 5×5 farm layout in aligned conditions is examined, to enable the study of a more complex design space. The final layout investigated is the Horns Rev wind farm (80 turbines), for which there has been very little study of the performance or sensitivity of wake steering optimisation. Overall, the results indicate a strong sensitivity of wake steering strategies to both analytical wake model choice, and to the particular implementation of algorithms used for optimisation. Significant variability can be observed in both farm power improvement and optimal yaw settings, depending on the optimisation set-up. Through a statistical analysis of the impact of optimiser initialisation and a study of the multi-modal and discontinuous nature of the underlying farm power objective functions, this study shows that the uncovered sensitivities represent a fundamental challenge to robustly identifying globally optimal solutions for the high-dimensional optimisation problems arising from realistic wind farm layouts. This paper proposes a simple strategy for sensitivity mitigation by introducing additional optimisation constraints, leading to higher farm power improvements and more consistent, coherent, and practicable optimal yaw angle settings.

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FarmConners wind farm flow control benchmark – Part 1: Blind test results

Cited by 12 publications

References 79 publications

Stochastic wind farm flow generation using a reduced order model of LES

Stochastic wind farm flow generation using a reduced order model of LES

Wind Farm Control Optimisation Under Load Constraints Via Surrogate Modelling

Sensitivity analysis of wake steering optimisation for wind farm power maximisation

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