Assessment of Wind Turbine Component Loads Under Yaw-Offset Conditions

Damiani, Rick; Dana, Scott; Annoni, Jennifer; Fleming, Paul; Roadman, Jason; Dam, Jeroen van; Dykes, Katherine

doi:10.5194/wes-2017-38

Cited by 24 publications

(36 citation statements)

References 12 publications

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“…To estimate the yaw position uncertainty, we compared operational data from an NREL turbine with a nearby meteorological measuring mast (NWTC Information Portal, 2019); these data were examined previously by Fleming et al (2018), Annoni et al (2018), and Damiani et al (2018). In the present study, the wind direction recorded at the turbine was compared to the wind direction measured on the upstream meteorological mast.…”

Section: Uncertainty Estimatesmentioning

confidence: 99%

“…Based on the shape of the distribution in Figure 2, we parameterize the yaw misalignment as a two-sided exponential distribution, termed the Laplace distribution, given by Figure 2. Errors in the yaw position y y y for a test turbine at the National Wind Technology Center at NREL (Fleming et al, 2018;Annoni et al, 2018;Damiani et al, 2018). The solid blue and green lines show Laplace and Gaussian distributions, respectively.…”

Section: Uncertainty Estimatesmentioning

confidence: 99%

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Wake steering optimization under uncertainty

Quick¹,

King²,

King³

et al. 2019

Preprint

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Abstract. Turbines in wind power plants experience significant power losses when wakes from upstream turbines affect the energy production of downstream turbines. A promising plant-level control strategy to reduce these losses is wake steering, where upstream turbines are yawed to direct wakes away from downstream turbines. However, there are significant uncertain- ties in many aspects of the wake steering problem. For example, in-field sensors do not give perfect information and inflow to the plant is complex and difficult to forecast with available information, even over short time periods. Here, we formulate and solve an optimization under uncertainty (OUU) problem for determining optimal plant-level wake steering strategies in the presence of uncorrelated uncertainties in the direction, speed, turbulence intensity, and shear of the incoming wind, as well as in turbine yaw positions. The OUU wake steering strategy is first examined for a two-turbine test case to explore the impacts of different types of inflow uncertainties, and is then demonstrated for a more realistic 11-turbine wind power plant. Of the sources of uncertainty considered, we find that wake steering strategies are most sensitive to uncertainties in the wind speed and direction. The OUU strategy also tends to favor smaller yaw angles when maximizing expected power production. Ultimately, the plant-level wake steering strategy formulated using the OUU approach yields 0.48 % more expected annual energy production than the deterministic strategy when considering stochastic inputs. Thus, not only does the present OUU strategy produce more power in realistic conditions, it also reduces risk by prescribing strategies that call for less extreme yaw angles.

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Section: Uncertainty Estimatesmentioning

confidence: 99%

Section: Uncertainty Estimatesmentioning

confidence: 99%

Wake steering optimization under uncertainty

Quick¹,

King²,

King³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…When superimposing an additional low-velocity zone, tower shadow or shear for example, the yaw-symmetry is disturbed. Asymmetric load distributions for turbines exposed to sheared inflow were recently reported by Damiani et al (2017). They showed that vertical wind shear causes asymmetric distributions of angle of attack and relative flow velocity in the course of a blade revolution.…”

mentioning

confidence: 97%

“…The so-called Mexnext project revealed modeling deficiencies while shedding light on complex unsteady flow 5 phenomena during yaw. In a recent paper by Damiani et al (2017) damage equivalent loads and extreme loads under yaw misalignment are measured and predicted for a fully instrumented wind turbine. They observed rather complex, inflow-dependent load distributions for yaw angle offsets.…”

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confidence: 99%

Wind tunnel study on power and loads optimization of two yaw-controlled model wind turbines

Bartl

Mühle

Sætran

2018

Preprint

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Abstract. In this experimental wind tunnel study the effects of intentional yaw misalignment on the power production and loads of a downstream turbine are investigated for full and partial wake overlap situations. Power, thrust force and yaw moment are measured on both the upstream and downstream turbine. The influence of inflow turbulence level and streamwise turbine separation distance are analyzed for full wake overlap situations. For partial wake overlap the concept of downstream turbine yawing for yaw moment mitigation is examined for different lateral offset positions. 5Results indicate that upstream turbine yaw misalignment is able to increase the combined power production of the two turbines for both partial and full wake overlap setups. For aligned turbine setups the combined power is increased between 3.5% and 11% depending on the inflow turbulence level and turbine separation distance. The increase in combined power is at the expense of increased yaw moments on both upstream and downstream turbine. For partial wake overlap situations, yaw moments on the downstream turbine can be mitigated through upstream turbine yawing, while simultaneously increasing the combined 10 power production. A final test case demonstrates the concept of opposed downstream turbine yawing in partial wake situations, which is shown to reduce its yaw moments and increasing its power production by up to 5%.

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“…Likewise the wind speed component perpendicular to the rotor is also decreases by a cosine relation and with the wind speed having a cubic impact on power a cosine³-relationship is more dominant for smaller angles (Burton et al, 2001 andMamidipudi et al, 2011). In addition to power losses, yaw errors are also increasing turbine loads (Wan et al, 2015 andDamiani et al, 2017). 10…”

mentioning

confidence: 99%

Determination of optimal wind turbine alignment into the wind and detection of alignment changes with SCADA data

Mittelmeier¹,

Kühn²

2018

Preprint

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Abstract. Upwind horizontal axis wind turbines need to be aligned with the main wind direction to maximize energy yield.Attempts have been made to improve the yaw alignment with advanced measurement equipment but most of these techniques introduce additional costs and rely on alignment tolerances with the rotor axis or the true north.Turbines that are well aligned after commissioning, may suffer an alignment degradation during their operational lifetime. 10Such changes need to be detected as soon as possible to minimize power losses. The objective of this paper is to propose a three-step methodology to improve turbine alignment and detect changes during operational lifetime with standard nacelle metrology (met) mast instruments (here: two cup anemometer and one wind vane).In step one, a reference turbine and an external undisturbed reference wind signal, e.g. met mast or lidar are used to determine flow corrections for the nacelle wind direction instruments to obtain a turbine alignment with optimal power 15 production. Secondly a nacelle wind speed correction is enabling the application of the previous step without additional external measurement equipment.Step three is a monitoring application and allows to detect alignment changes on the wind direction measurement device by means of a flow equilibrium between the two anemometers behind the rotor.The three steps are demonstrated at two 2MW turbines together with a ground based lidar. A first order multi linear regression model gives sufficient correction of the flow distortion behind the rotor for our purposes and two wind vane 20 alignment changes are detected with an accuracy of ±1.4 ° within three days of operation after the change is introduced.We could show, that standard turbine equipment is able to align a turbine with sufficient accuracy and changes to its alignment can be detected in a reasonable short time which helps to minimize power losses.

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Assessment of Wind Turbine Component Loads Under Yaw-Offset Conditions

Cited by 24 publications

References 12 publications

Wake steering optimization under uncertainty

Wake steering optimization under uncertainty

Wind tunnel study on power and loads optimization of two yaw-controlled model wind turbines

Determination of optimal wind turbine alignment into the wind and detection of alignment changes with SCADA data

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