Validation of the standalone implementation of the dynamic wake meandering model for power production

Keck, Rolf-Erik

doi:10.1002/we.1777

Cited by 22 publications

(25 citation statements)

References 25 publications

(41 reference statements)

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“…The DWM model was validated for loads and power production using the data in the Egmond aan Zee wind farm for two conditions, one under free-stream wind condition and the other under the conditions created by wakes from upwind turbines [21]. Validation of the DWM model without an aeroelastic model for power production was carried out in [23] by comparing with the data measured in Horns Rev, Lillgrund, Nysted, and Weingermeer wind farms. Validation of the DWM model for tower load prediction was carried out in [24] using the measurements in the Nysted II wind farm.…”

Section: The Dynamic Wake Meandering Modelmentioning

confidence: 99%

A Review on the Meandering of Wind Turbine Wakes

Yang

Sotiropoulos

2019

Energies

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Meandering describes the large-scale, low frequency motions of wind turbine wakes, which could determine wake recovery rates, impact the loads exerted on turbine structures, and play a critical role in the design and optimal control of wind farms. This paper presents a comprehensive review of previous work related to wake meandering. Emphasis is placed on the origin and characteristics of wake meandering and computational models, including both the dynamic wake meandering models and large-eddy simulation approaches. Future research directions in the field are also discussed.

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Section: The Dynamic Wake Meandering Modelmentioning

confidence: 99%

A Review on the Meandering of Wind Turbine Wakes

Yang

Sotiropoulos

2019

Energies

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“…Since the prototype was first proposed by Larsen et al [2], DWM has been constantly extended for a wider range of application. In the present study for AEP estimation, we adopt the standalone version of DWM, where a lookup table about the mean wake-deficit profile is pre-calculated for a range of operating conditions [3,4].…”

Section: Dynamic Wake Meanderingmentioning

confidence: 99%

Estimation of annual energy production using dynamic wake meandering in combination with ambient CFD solutions

Hahn

Machefaux

Hristov

et al. 2016

J. Phys.: Conf. Ser.

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Abstract. In the present study, combination of the standalone dynamic wake meandering (DWM) model with Reynolds-averaged Navier-Stokes (RANS) CFD solutions for ambient ABL flows is introduced, and its predictive performance for annual energy production (AEP) is evaluated against Vestas' SCADA data for six operating wind farms over semi-complex terrains under neutral conditions. The performances of conventional linear and quadratic wake superposition techniques are also compared, together with the in-house implemention of successive hierarchical merging approaches. As compared to our standard procedure based on the Jensen model in WindPRO, the overall results are promising, leading to a significant improvement in AEP accuracy for four of the six sites. While the conventional linear superposition shows the best performance for the improved four sites, the hierarchical square superposition shows the least deteriorated result for the other two sites. IntroductionAs investments on large-scale wind farms grow, precise estimation of annual energy production (AEP) is becoming increasingly important, especially during the planning phase of a project, in order to guarantee business case certainty. The challenge of AEP evaluation mainly originates from its multi-disciplinary nature, where prediction uncertainties from various elementary sources such as ambient ABLs, wakes, power curves and risk mitigation strategies are all blended together. Preliminary uncertainty analyses suggest that no single uncertainty source plays a dominant role but rather the most limiting factor varies case by case [1]. Therefore, integration of most advanced models for all individual elementary physics would be a key step towards improved AEP accuracy.Vestas has devoted substantial efforts to ensure most up-to-date CFD capabilities, including highfidelity simulation techniques as well as steady/unsteady Reynolds-averaged Navier-Stokes (RANS) models. In our standard micro-siting approach, AEP is estimated by combining a wind resource file (RSF) from RANS simulation with the Jensen model within the software WindPRO. The objective of the present study is to introduce the dynamic wake meandering (DWM) model to our process in order to account for wake physics of higher complexity at an affordable cost, and validate its predictive performance for AEP against Vestas' SCADA data for various operating wind farms.

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“…This formulation allows for the build‐up of both wake deficit and wake‐added TI over a row of turbines. Further details regarding the method for running multiple turbine simulations using the standalone DWM model are found in Keck et al …”

Section: The Standalone Dynamic Wake Meandering Modelmentioning

confidence: 99%

“…As described previously, the principle of using the standalone DWM model for wind farm simulations closely resembles that of using the model for multiple turbine simulations along a row suggested by Keck et al (Figure ). This means that the DWM model is used to simulate the evolution of a single wake from a turbine inside the wind farm and that the role of the field model is to simulate the inflow conditions to the studied turbine (equations –).…”

Section: Wind Farm Simulation With the Standalone Dwm Modelmentioning

confidence: 99%

“…However, if the model is used solely to simulate power production of a wind farm, the response of the aeroelastic model is not required. Therefore, an alternative application where the DWM model is used as a standalone model is proposed by Keck et al While this formulation lacks the ability to predict turbine loads, the advantages are increased computational speed and a reduced level of model complexity.…”

Section: Introductionmentioning

confidence: 99%

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A pragmatic approach to wind farm simulations using the dynamic wake meandering model

Keck

Undheim

2014

Wind Energy

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This paper presents a computationally efficient method for using the dynamic wake meandering model to conduct simulations of wind farm power production. The method is based on creating a database, which contains the time and rotor-averaged wake effect at any point downstream of a wake-emitting turbine operating in arbitrary ambient conditions and at an arbitrary degree of wake influence. This database is later used as a look-up table at runtime to estimate the operating conditions at all turbines in the wind farm, thus eliminating the need to run the dynamic wake meandering model at runtime.By using the proposed method, the time required to conduct wind farm simulations is reduced by three orders of magnitude compared with running the standalone dynamic wake meandering model at runtime. As a result, the wind farm production dynamics for a farm of 100 turbines at 10,000 different sets of ambient conditions run on a normal laptop in 1 h.The method is validated against full scale measurements from the Smøla and OWEZ wind farms, and fair agreement is achieved.

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Validation of the standalone implementation of the dynamic wake meandering model for power production

Cited by 22 publications

References 25 publications

A Review on the Meandering of Wind Turbine Wakes

A Review on the Meandering of Wind Turbine Wakes

Estimation of annual energy production using dynamic wake meandering in combination with ambient CFD solutions

A pragmatic approach to wind farm simulations using the dynamic wake meandering model

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