Effects of Wind Farm Down-Regulation in the Offshore Wind Farm Alpha Ventus

ASME 2019 2nd International Offshore Wind Technical Conference ◽

10.1115/iowtc2019-7554 ◽

2019 ◽

Author(s):

Matthias Kretschmer ◽

Vasilis Pettas ◽

Po Wen Cheng

Keyword(s):

Wind Turbine ◽

Wind Direction ◽

Energy Production ◽

Wind Farm ◽

Measurement Data ◽

Offshore Wind ◽

Offshore Wind Farm ◽

Down Regulation ◽

Maximum Reduction ◽

High Reduction

Abstract In recent years wind turbine down-regulation has been used or investigated for a variety of applications such as wind farm power optimisation, energy production curtailment and lifetime management. This study presents results from measurement data of tower loads and power obtained from two turbines located in the German offshore wind farm alpha ventus. The free streaming turbine, located closely to a fully equipped meteorological mast, was down-regulated to 50% for a period of 8 months, while the downwind turbine was operating normally. The results are compared to periods where both turbines were operated in normal conditions. Changes in loads and power are analysed according to incoming wind direction and magnitude. Results show a high reduction in the loads of the down regulated turbine, up to a level of 40%. For the turbine in wake the effects in loads are more prominent, showing a maximum reduction of 30%, compared to the effects in power and are seen in a wider sector of about 20° for loads and 10° for power.

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FAST.Farm load validation for single wake situations at alpha ventus

10.5194/wes-2021-37 ◽

2021 ◽

Author(s):

Matthias Kretschmer ◽

Jason Jonkman ◽

Vasilis Pettas ◽

Po Wen Cheng

Keyword(s):

Wind Direction ◽

Wind Farm ◽

Measurement Data ◽

Offshore Wind ◽

Simulation Tool ◽

Offshore Wind Farm ◽

Load Distributions ◽

Model Transfer ◽

Turbine Model ◽

Good Agreement

Abstract. The main objective of the presented work is the validation of the simulation tool FAST.Farm for the calculation of power and structural loads in single wake situations; the basis for the validation is the measurement data base of the operating offshore wind farm alpha ventus. The approach is described in detail and covers calibration of the aeroelastic turbine model, transfer of environmental conditions to simulations and comparison between simulations and adequately filtered measurements. It is shown that FAST.Farm accurately predicts power and structural load distributions over wind direction. Additionally, the frequency response of the structure is investigated and it is calculated by FAST.Farm in good agreement with the measurements. In general, the calculation of fatigue loads is improved with a wake-added turbulence model added to FAST.Farm in the course of this study.

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Correlations of power output fluctuations in an offshore wind farm using high-resolution SCADA data

10.5194/wes-2020-96 ◽

2020 ◽

Author(s):

Janna K. Seifert ◽

Martin Kraft ◽

Martin Kühn ◽

Laura J. Lukassen

Keyword(s):

Wind Turbine ◽

Wind Turbines ◽

Power Output ◽

Wind Farm ◽

Clustering Algorithm ◽

Free Field ◽

Measurement Data ◽

Offshore Wind ◽

Offshore Wind Farm ◽

Output Fluctuations

Abstract. The correlation of power output fluctuations of wind turbines in free field are investigated, taking into account the challenge of varying correlation states due to variable flow and wind turbine conditions within the wind farm. Based on eight months of 1 Hz SCADA data, measured at an offshore wind farm with 80 wind turbines, the influence of different parameters on the correlation of power output fluctuations is analysed. It is found that the correlation of power output fluctuations of wind turbines depends on the location of the wind turbines within the wind farm as well as the inflow conditions (free-stream or wake). Wind direction investigations show that the correlation is highest for streamwise aligned pairs and decreases towards spanwise pairs. Most importantly, the highly variable measurement data in a free-field wind farm has considerable influence on the identification of different correlation states. To account for that, the clustering algorithm k-means is used to group wind turbine pairs with similar correlations. The main outcome is that next to the location of a wind turbine pair in the wind farm the standard deviation in their power output and their power differences are suitable parameters to describe the correlation of power output fluctuations.

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Fatigue Life Analysis of Offshore Wind Turbine Support Structures in an Offshore Wind Farm

ASME 2018 1st International Offshore Wind Technical Conference ◽

10.1115/iowtc2018-1061 ◽

2018 ◽

Author(s):

Bryan Nelson ◽

Yann Quéméner

Keyword(s):

Wind Turbine ◽

Wind Turbines ◽

Wind Farm ◽

The Body ◽

Offshore Wind ◽

Offshore Wind Turbine ◽

Analysis Tool ◽

Support Structures ◽

Offshore Wind Farm ◽

Actuator Disk

This study evaluated, by time-domain simulations, the fatigue lives of several jacket support structures for 4 MW wind turbines distributed throughout an offshore wind farm off Taiwan’s west coast. An in-house RANS-based wind farm analysis tool, WiFa3D, has been developed to determine the effects of the wind turbine wake behaviour on the flow fields through wind farm clusters. To reduce computational cost, WiFa3D employs actuator disk models to simulate the body forces imposed on the flow field by the target wind turbines, where the actuator disk is defined by the swept region of the rotor in space, and a body force distribution representing the aerodynamic characteristics of the rotor is assigned within this virtual disk. Simulations were performed for a range of environmental conditions, which were then combined with preliminary site survey metocean data to produce a long-term statistical environment. The short-term environmental loads on the wind turbine rotors were calculated by an unsteady blade element momentum (BEM) model of the target 4 MW wind turbines. The fatigue assessment of the jacket support structure was then conducted by applying the Rainflow Counting scheme on the hot spot stresses variations, as read-out from Finite Element results, and by employing appropriate SN curves. The fatigue lives of several wind turbine support structures taken at various locations in the wind farm showed significant variations with the preliminary design condition that assumed a single wind turbine without wake disturbance from other units.

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A Study of Energy Production Change according to Atmospheric Stability and Equivalent Wind Speed in the Offshore Wind Farm using CFD Program

Journal of Environmental Science International ◽

10.5322/jesi.2016.25.2.247 ◽

2016 ◽

Vol 25 (2) ◽

pp. 247-257

Author(s):

Geon-Hwa Ryu ◽

◽

Dong-Hyeok Kim ◽

Hwa-Woon Lee ◽

Soon-Young Park ◽

...

Keyword(s):

Wind Speed ◽

Energy Production ◽

Wind Farm ◽

Atmospheric Stability ◽

Offshore Wind ◽

Offshore Wind Farm ◽

Production Change

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Correlations of power output fluctuations in an offshore wind farm using high-resolution SCADA data

Wind Energy Science ◽

10.5194/wes-6-997-2021 ◽

2021 ◽

Vol 6 (4) ◽

pp. 997-1014

Author(s):

Janna Kristina Seifert ◽

Martin Kraft ◽

Martin Kühn ◽

Laura J. Lukassen

Keyword(s):

Wind Turbine ◽

Wind Turbines ◽

Power Output ◽

Wind Farm ◽

Offshore Wind ◽

Offshore Wind Farm ◽

Flow Conditions ◽

Output Fluctuations ◽

Power Difference ◽

Time Correlations

Abstract. Space–time correlations of power output fluctuations of wind turbine pairs provide information on the flow conditions within a wind farm and the interactions of wind turbines. Such information can play an essential role in controlling wind turbines and short-term load or power forecasting. However, the challenges of analysing correlations of power output fluctuations in a wind farm are the highly varying flow conditions. Here, we present an approach to investigate space–time correlations of power output fluctuations of streamwise-aligned wind turbine pairs based on high-resolution supervisory control and data acquisition (SCADA) data. The proposed approach overcomes the challenge of spatially variable and temporally variable flow conditions within the wind farm. We analyse the influences of the different statistics of the power output of wind turbines on the correlations of power output fluctuations based on 8 months of measurements from an offshore wind farm with 80 wind turbines. First, we assess the effect of the wind direction on the correlations of power output fluctuations of wind turbine pairs. We show that the correlations are highest for the streamwise-aligned wind turbine pairs and decrease when the mean wind direction changes its angle to be more perpendicular to the pair. Further, we show that the correlations for streamwise-aligned wind turbine pairs depend on the location of the wind turbines within the wind farm and on their inflow conditions (free stream or wake). Our primary result is that the standard deviations of the power output fluctuations and the normalised power difference of the wind turbines in a pair can characterise the correlations of power output fluctuations of streamwise-aligned wind turbine pairs. Further, we show that clustering can be used to identify different correlation curves. For this, we employ the data-driven k-means clustering algorithm to cluster the standard deviations of the power output fluctuations of the wind turbines and the normalised power difference of the wind turbines in a pair. Thereby, wind turbine pairs with similar power output fluctuation correlations are clustered independently from their location. With this, we account for the highly variable flow conditions inside a wind farm, which unpredictably influence the correlations.

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