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

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.

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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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Wind direction error in the Lillgrund offshore wind farm

2nd IET Renewable Power Generation Conference (RPG 2013) ◽

10.1049/cp.2013.1839 ◽

2013 ◽

Author(s):

Xi Yu ◽

E. Maguire ◽

D. Infield

Keyword(s):

Wind Direction ◽

Wind Farm ◽

Offshore Wind ◽

Offshore Wind Farm ◽

Direction Error

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

Wind Energy Science ◽

10.5194/wes-5-601-2020 ◽

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.

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

International Journal of Renewable Energy Development ◽

10.14710/ijred.2022.38263 ◽

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.

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

10.5194/wes-2019-92 ◽

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.

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Using a Langevin model for the simulation of environmental conditions in an offshore wind farm

Journal of Physics Conference Series ◽

10.1088/1742-6596/1104/1/012023 ◽

2018 ◽

Vol 1104 ◽

pp. 012023 ◽

Cited By ~ 1

Author(s):

Helene Seyr ◽

Michael Muskulus

Keyword(s):

Environmental Conditions ◽

Wind Farm ◽

Offshore Wind ◽

Offshore Wind Farm ◽

Langevin Model

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

Energies ◽

10.3390/en13246492 ◽

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.

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