scholarly journals How to improve the state of the art in metocean measurement datasets

2020 ◽  
Vol 5 (1) ◽  
pp. 285-308
Author(s):  
Erik Quaeghebeur ◽  
Michiel B. Zaaijer

Abstract. We present an analysis of three datasets of 10 min metocean measurement statistics and our resulting recommendations to both producers and users of such datasets. Many of our recommendations are more generally of interest to all numerical measurement data producers. The datasets analyzed originate from offshore meteorological masts installed to support offshore wind farm planning and design: the Dutch OWEZ and MMIJ and the German FINO1. Our analysis shows that such datasets contain issues that users should look out for and whose prevalence can be reduced by producers. We also present expressions to derive uncertainty and bias values for the statistics from information typically available about sample uncertainty. We also observe that the format in which the data are disseminated is sub-optimal from the users' perspective and discuss how producers can create more immediately useful dataset files. Effectively, we advocate using an established binary format (HDF5 or netCDF4) instead of the typical text-based one (comma-separated values), as this allows for the inclusion of relevant metadata and the creation of significantly smaller directly accessible dataset files. Next to informing producers of the advantages of these formats, we also provide concrete pointers to their effective use. Our conclusion is that datasets such as the ones we analyzed can be improved substantially in usefulness and convenience with limited effort.

2019 ◽  
Author(s):  
Erik Quaeghebeur ◽  
Michiel B. Zaaijer

Abstract. We present an analysis of three metocean datasets of 10-minute statistics and our resulting recommendations to both producers and users of such datasets. Many of our recommendations are more generally of interest to all numerical measurement data producers. The datasets analyzed originate from offshore meteorological masts installed to support offshore wind farm planning and design: the Dutch OWEZ and MMIJ, and the German FINO 1. Our analysis shows that such datasets contain issues that users should look out for and whose prevalence can be reduced by producers. We also present expressions to derive uncertainty and bias values for the statistics from information typically available about sample uncertainty. We also observe that the format in which the data is disseminated is sub-optimal from the users' perspective and discuss how producers can create more immediately useful dataset files. Effectively, we advocate using an established binary format (HDF5 or netCDF4) instead of the typical text-based one (comma-separated values), as this allows for the inclusion of relevant metadata and the creation of significantly smaller directly accessible dataset files. Next to informing producers of the advantages of these formats, we also provide concrete pointers to their effective use. Our conclusion is that datasets such as the ones we analyzed can be improved substantially in usefulness and convenience with limited effort.


Author(s):  
Matthias Kretschmer ◽  
Vasilis Pettas ◽  
Po Wen Cheng

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.


2021 ◽  
Author(s):  
Matthias Kretschmer ◽  
Jason Jonkman ◽  
Vasilis Pettas ◽  
Po Wen Cheng

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.


2020 ◽  
Author(s):  
Janna K. Seifert ◽  
Martin Kraft ◽  
Martin Kühn ◽  
Laura J. Lukassen

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.


2019 ◽  
Vol 139 (4) ◽  
pp. 259-268
Author(s):  
Effat Jahan ◽  
Md. Rifat Hazari ◽  
Mohammad Abdul Mannan ◽  
Atsushi Umemura ◽  
Rion Takahashi ◽  
...  

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