Adjoint-based model predictive control of wind farms: Beyond the quasi steady-state power maximization * *This work has been funded by the Ministry for Sciences and Culture of the Federal State of Lower Saxony, Germany as part of the PhD Programme on System Integration of Renewable Energies (SEE) and by the German Ministry of Economic Affairs and Energy (BMWi) in the scope of the WIMS-Cluster project (FKZ 0324005).

Vali, Mehdi; Petrović, Vlaho; Boersma, Sjoerd; Wingerden, Jan‐Willem van; Kühn, Martin

doi:10.1016/j.ifacol.2017.08.382

Cited by 22 publications

(25 citation statements)

References 9 publications

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“…Empirically, good results have been achieved with cell dimensions of about 30 − 50 m in width and length, resulting in N with a typical value on the order of 10 3 − 10 4 for medium-sized wind farms (e.g., Vali et al, 2016Vali et al, , 2017Doekemeijer et al, 2016, 15 2017; Boersma et al, 2017b). Such a number of states may seem very small for LES simulations, yet is very high for control purposes.…”

Section: Discretization Boundary Conditions and Computational Costmentioning

confidence: 99%

“…As the primary energy demand in Europe is projected to decrease by 200 million tonnes of oil equivalent from 2016 to 2040, there is an additional shift in the energy 15 source used to meet this demand (International Energy Agency, 2017). Shortly after 2030, onshore and offshore wind energy are projected to become the main source of electricity for the European Union.…”

Section: Introductionmentioning

confidence: 99%

“…In the work presented here, the model described in Boersma et al (2017b) is used, which is a light-weight two-dimensional LES code with wind farm control as its main objective. This dynamic surrogate model, named 15 "WindFarmSimulator" (WFSim), includes yaw and axial induction actuation, turbine-induced turbulence effects, and spatially and temporally varying inflow profiles, with a moderate computational cost (10 1 − 10 2 ms per timestep). 2 The second component of the closed-loop framework is an algorithm that adjusts the surrogate model's parameters to improve its accuracy online using flow and/or turbine measurements (e.g., SCADA data, LiDAR measurements, met masts).…”

Section: Introductionmentioning

confidence: 99%

“…In terms of control, this turns into a dual estimation problem, in which both the model state and a subset of model parameters 20 are estimated online. Currently, the optimization algorithms presented in Munters and Meyers (2017) and Vali et al (2017) have assumed full state knowledge, conveniently ignoring the step of model adaptation. Literature on state reconstruction and model calibration for dynamical wind farm models is sparse, limited to linear low-order models and/or common estimation algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…predictive optimization method for dynamical models (e.g., Goit and Meyers, 2015;Vali et al, 2017;Siniscalchi-Minna et al, 2018). A more in-depth discussion on optimization algorithms for the framework of Fig.…”

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confidence: 99%

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Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control

Doekemeijer¹,

Boersma²,

Pao³

et al. 2018

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Abstract. Wind farm control often relies on computationally inexpensive surrogate models to predict the dynamics inside a farm. However, the reliability of these models over the spectrum of wind farm operation remains questionable due to the many uncertainties in the atmospheric conditions and tough-to-model flow dynamics at a range of spatial and temporal scales relevant for control. A closed-loop control framework is proposed in which a simplified dynamical LES model is calibrated and used for optimization in real time. This paper presents an estimation solution with an Ensemble Kalman filter (EnKF) at its core, 5 which calibrates the surrogate model to the actual atmospheric conditions. The estimator is tested in high-fidelity simulations of a nine-turbine wind farm. Using exclusively turbine SCADA measurements, the adaptability to modeling errors and changes in atmospheric conditions (TI, wind speed) is shown. Convergence is reached within 400 seconds of operation, after which the estimation error in flow fields is negligible. At a low computational cost of 1.2 s on an 8-core CPU, this algorithm shows comparable accuracy to the state of the art from the literature while being approximately two orders of magnitude faster. Using 10 the calibration solution presented, the surrogate model can be used for accurate forecasting and optimization.

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Section: Discretization Boundary Conditions and Computational Costmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control

Doekemeijer¹,

Boersma²,

Pao³

et al. 2018

Preprint

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Review of wake management techniques for wind turbines

Houck

2021

Wind Energy

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Summary The progression of wind turbine technology has led to wind turbines being incredibly optimized machines often approaching their theoretical maximum production capabilities. When placed together in arrays to make wind farms, however, they are subject to wake interference that greatly reduces downstream turbines' power production, increases structural loading and maintenance, reduces their lifetimes, and ultimately increases the levelized cost of energy. Development of techniques to manage wakes and operate larger and larger arrays of turbines more efficiently is now a crucial field of research. Herein, four wake management techniques in various states of development are reviewed. These include axial induction control, wake steering, the latter two combined, and active wake control. Each of these is reviewed in terms of its control strategies and use for power maximization, load reduction, and ancillary services. By evaluating existing research, several directions for future research are suggested.

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Maximizing wind farm power output with the helix approach: Experimental validation and wake analysis using tomographic particle image velocimetry

van der Hoek,

den Abbeele,

Simao Ferreira

et al. 2024

Wind Energy

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Wind farm control can play a key role in reducing the negative impact of wakes on wind turbine power production. The helix approach is a recent innovation in the field of wind farm control, which employs individual blade pitch control to induce a helical velocity profile in a wind turbine wake. This forced meandering of the wake has turned out to be very effective for the recovery of the wake, increasing the power output of downstream turbines by a significant amount. This paper presents a wind tunnel study with two scaled wind turbine models of which the upstream turbine is operated with the helix approach. We used tomographic particle image velocimetry to study the dynamic behavior of the wake under the influence of the helix excitation. The measured flow fields confirm the wake recovery capabilities of the helix approach compared with normal operation. Additional emphasis is put on the effect of the helix approach on the breakdown of blade tip vortices, a process that plays an important role in re‐energizing the wake. Measurements indicate that the breakdown of tip vortices and the resulting destabilization of the wake are enhanced significantly with the helix approach. Finally, turbine measurements show that the helix approach was able to increase the combined power for this particular two‐turbine setup by as much as 15%.

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Cited by 22 publications

References 9 publications

Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control

Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control

Review of wake management techniques for wind turbines

Maximizing wind farm power output with the helix approach: Experimental validation and wake analysis using tomographic particle image velocimetry

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