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

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.

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

2021 ◽  
Vol 6 (5) ◽  
pp. 1247-1262
Author(s):  
Matthias Kretschmer ◽  
Jason Jonkman ◽  
Vasilis Pettas ◽  
Po Wen Cheng
Keyword(s):  
Environmental Conditions ◽  
Wind Direction ◽  
Wind Farm ◽  
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 database of the operating offshore wind farm alpha ventus. The approach is described in detail and covers the 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 with discrepancies of less than 10 % for most of the cases compared to the measurements. 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.

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

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.

scholarly journals How to improve your metocean datasets

2019 ◽  
Author(s):  
Erik Quaeghebeur ◽  
Michiel B. Zaaijer
Keyword(s):  
Wind Farm ◽  
Measurement Data ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Farm Planning ◽  
Planning And Design ◽  
Binary Format ◽  
The Creation ◽  
Effective Use

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.

scholarly journals Analysing uncertainties in offshore wind farm power output using measure–correlate–predict methodologies

2020 ◽  
Vol 5 (2) ◽  
pp. 601-621
Author(s):  
Michael Denis Mifsud ◽  
Tonio Sant ◽  
Robert Nicholas Farrugia
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Power Output ◽  
Wind Farm ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Offshore Wind ◽  
Energy Yield ◽  
Offshore Wind Farm ◽  
Power And Energy

Abstract. This paper investigates the uncertainties resulting from different measure–correlate–predict (MCP) methods to project the power and energy yield from a wind farm. The analysis is based on a case study that utilises short-term data acquired from a lidar wind measurement system deployed at a coastal site in the northern part of the island of Malta and long-term measurements from the island's international airport. The wind speed at the candidate site is measured by means of a lidar system. The predicted power output for a hypothetical offshore wind farm from the various MCP methodologies is compared to the actual power output obtained directly from the input of lidar data to establish which MCP methodology best predicts the power generated. The power output from the wind farm is predicted by inputting wind speed and direction derived from the different MCP methods into windPRO® (https://www.emd.dk/windpro, last access: 8 May 2020). The predicted power is compared to the power output generated from the actual wind and direction data by using the normalised mean absolute error (NMAE) and the normalised mean-squared error (NMSE). This methodology will establish which combination of MCP methodology and wind farm configuration will have the least prediction error. The best MCP methodology which combines prediction of wind speed and wind direction, together with the topology of the wind farm, is that using multiple linear regression (MLR). However, the study concludes that the other MCP methodologies cannot be discarded as it is always best to compare different combinations of MCP methodologies for wind speed and wind direction, together with different wake models and wind farm topologies.

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
Keyword(s):  
Wind Farm ◽  
State Of The Art ◽  
Measurement Data ◽  
The State ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Farm Planning ◽  
Planning And Design ◽  
Binary Format ◽  
Effective Use

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.

scholarly journals Improved Evaluation of The Wind Power Potential of a Large Offshore Wind Farm Using Four Analytical Wake Models

2021 ◽  
Vol 11 (1) ◽  
pp. 35-48
Author(s):  
Mohammed Amine Hassoine ◽  
Fouad Lahlou ◽  
Adnane Addaim ◽  
Abdessalam Ait Madi
Keyword(s):  
Wind Direction ◽  
Power Output ◽  
Power Loss ◽  
Wind Farm ◽  
Offshore Wind ◽  
Total Power ◽  
Offshore Wind Farm ◽  
Wake Effect ◽  
Power Deficit ◽  
Total Power Output

The objective of this paper is to investigate the ability of analytical wake models to estimate the wake effects between wind turbines (WTs). The interaction of multiple wakes reduces the total power output produced by a large offshore wind farm (LOFWF). This power loss is due to the effect of turbine spacing (WTS), if the WTs are too close, the power loss is very significant. Therefore, the optimization of turbine positions within the offshore wind farm requires an understanding of the interaction of wakes inside the wind farm. To better understand the wake effect, the Horns Rev 1 offshore wind farm has been studied with four wake models, Jensen, Larsen, Ishihara, and Frandsen. A comparative study of the wake models has been performed in several situations and configurations, single and multiple wakes are taken into consideration. Results from the Horns Rev1 offshore wind farm case have  been evaluated and compared to observational data, and also  with the previous studies. The power output of a row of WTs is sensitive to the wind direction. For example, if a row of ten turbines is aligned with the 270° wind direction, the full wake condition of WTs is reached and the power deficit limit predicted by Jensen model exceeds 70%. When a wind direction changes only of  10° (260° and 280°), the deficit limit reduces to 30%. The obtained results show that a significant power deficit occurs when the turbines are arranged in an aligned manner. The findings also showed that all four models gave acceptable predictions of the total power output. The comparison between the calculated and reported power output of Horns Revs 1 showed that the differences ranged from - 8.27 MW (12.49%) to 15.27 MW (23.06%) for the Larsen and Frandsen models, respectively.

scholarly journals Analysing Uncertainties in Offshore Wind Farm Power Output using Measure Correlate Predict Methodologies

2019 ◽  
Author(s):  
Michael Denis Mifsud ◽  
Tonio Sant ◽  
Robert Nicholas Farrugia
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Power Output ◽  
Wind Farm ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Offshore Wind ◽  
Energy Yield ◽  
Offshore Wind Farm ◽  
The Mean

Abstract. This paper investigates the uncertainties resulting from different Measure-Correlate-Predict methods to project the power and energy yield from a wind farm. The analysis is based on a case study that utilizes short-term data acquired from a LiDAR wind measurement system deployed at a coastal site in the northern part of the island of Malta and long-term measurements from the island’s international airport. The wind speed at the candidate site is measured by means of a LiDAR system. The predicted power output for a hypothetical offshore wind farm from the various MCP methodologies is compared to the actual power output obtained directly from the input of LiDAR data to establish which MCP methodology best predicts the power generated. The power output from the wind farm is predicted by inputting wind speed and direction derived from the different MCP methods into windPRO® (https://www.emd.dk/windpro). The predicted power is compared to the power output generated from the actual wind and direction data by using the Mean Squared Error (MSE) and the Mean Absolute Error (MAE) measures. This methodology will establish which combination of MCP methodology and wind farm configuration will have the least prediction error. The best MCP methodology which combines prediction of wind speed and wind direction, together with the topology of the wind farm, is that using Artificial Neural Networks. However, the study concludes that the other MCP methodologies cannot be discarded as it is always best to compare different combinations of MCP methodologies for wind speed and wind direction, together with different wake models and wind farm topologies.

scholarly journals Wind Characteristics in the Taiwan Strait: A Case Study of the First Offshore Wind Farm in Taiwan

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6492
Author(s):  
Ke-Sheng Cheng ◽  
Cheng-Yu Ho ◽  
Jen-Hsin Teng
Keyword(s):  
Standard Deviation ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Direction ◽  
Wind Farm ◽  
Sea Breeze ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Wind Speed Profile ◽  
Mean Wind Speed

This study analyzed the wind speed data of the met mast in the first commercial-scale offshore wind farm of Taiwan from May 2017 to April 2018. The mean wind speed and standard deviation, wind rose, histogram, wind speed profile, and diurnal variation of wind speed with associated changes in wind direction revealed some noteworthy findings. First, the standard deviation of the corresponding mean wind speed is somewhat high. Second, the Hellmann exponent is as low as 0.05. Third, afternoons in winter and nights and early mornings in summer have the highest and lowest wind speed in a year, respectively. Regarding the histogram, the distribution probability of wind is bimodal, which can be depicted as a mixture of two gamma distributions. In addition, the corresponding change between the hourly mean wind speed and wind direction revealed that the land–sea breeze plays a significant role in wind speed distribution, wind profile, and wind energy production. The low Hellmann exponent is discussed in detail. To further clarify the effect of the land–sea breeze for facilitating future wind energy development in Taiwan, we propose some recommendations.

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

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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