Editorial for the Special Issue “Remote Sensing of Atmospheric Conditions for Wind Energy Applications”

Hasager, Charlotte Bay; Sjöholm, Mikael

doi:10.3390/rs11070781

Cited by 5 publications

(3 citation statements)

References 31 publications

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“…Wind power has become an important source of global renewable energy [1,2]. As wind turbines (WTs) often fail in extreme environments, including sleet, wind gusts, and lightning strikes [3], wind turbine blades (WTBs) monitoring, such as fault prognostics, health monitoring, and early failure warning, etc., is deemed an important task to ensure their maintenance of normal operation [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Motion Blur Removal for Uav-Based Wind Turbine Blade Images Using Synthetic Datasets

et al. 2021

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Unmanned air vehicle (UAV) based imaging has been an attractive technology to be used for wind turbine blades (WTBs) monitoring. In such applications, image motion blur is a challenging problem which means that motion deblurring is of great significance in the monitoring of running WTBs. However, an embarrassing fact for these applications is the lack of sufficient WTB images, which should include better pairs of sharp images and blurred images captured under the same conditions for network model training. To overcome the challenge of image pair acquisition, a training sample synthesis method is proposed. Sharp images of static WTBs were first captured, and then video sequences were prepared by running WTBs at different speeds. The blurred images were identified from the video sequences and matched to the sharp images using image difference. To expand the sample dataset, rotational motion blurs were simulated on different WTBs. Synthetic image pairs were then produced by fusing sharp images and images of simulated blurs. Finally, a total of 4000 image pairs were obtained. To conduct motion deblurring, a hybrid deblurring network integrated with DeblurGAN and DeblurGANv2 was deployed. The results show that the integration of DeblurGANv2 and Inception-ResNet-v2 provides better deblurred images, in terms of both metrics of signal-to-noise ratio (80.138) and structural similarity (0.950) than those obtained from the comparable networks of DeblurGAN and MobileNet-DeblurGANv2.

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Section: Introductionmentioning

confidence: 99%

Motion Blur Removal for Uav-Based Wind Turbine Blade Images Using Synthetic Datasets

et al. 2021

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“…Unlike onshore sites, where a meteorological mast (met mast) can be constructed with relative ease, building offshore met masts for the same purpose can involve serious technical and financial challenges. As an alternative to met masts, wind resource assessment using light detection and ranging (LiDAR) has received significant interest in the wind energy community [3][4][5][6][7]. The current work aims to investigate the performance of a scanning Doppler LiDAR for wind resource assessment in the nearshore region, which is considered promising for the development of fixed-bottom offshore wind farms.…”

Section: Introductionmentioning

confidence: 99%

Coastal Wind Measurements Using a Single Scanning LiDAR

et al. 2020

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A wind measurement campaign using a single scanning light detection and ranging (LiDAR) device was conducted at the Hazaki Oceanographical Research Station (HORS) on the Hazaki coast of Japan to evaluate the performance of the device for coastal wind measurements. The scanning LiDAR was deployed on the landward end of the HORS pier. We compared the wind speed and direction data recorded by the scanning LiDAR to the observations obtained from a vertical profiling LiDAR installed at the opposite end of the pier, 400 m from the scanning LiDAR. The best practice for offshore wind measurements using a single scanning LiDAR was evaluated by comparing results from a total of nine experiments using several different scanning settings. A two-parameter velocity volume processing (VVP) method was employed to retrieve the horizontal wind speed and direction from the radial wind speed. Our experiment showed that, at the current offshore site with a negligibly small vertical wind speed component, the accuracy of the scanning LiDAR wind speeds and directions was sensitive to the azimuth angle setting, but not to the elevation angle setting. In addition to the validations for the 10-minute mean wind speeds and directions, the application of LiDARs for the measurement of the turbulence intensity (TI) was also discussed by comparing the results with observations obtained from a sonic anemometer, mounted at the seaward end of the HORS pier, 400 m from the scanning LiDAR. The standard deviation obtained from the scanning LiDAR measurement showed a greater fluctuation than that obtained from the sonic anemometer measurement. However, the difference between the scanning LiDAR and sonic measurements appeared to be within an acceptable range for the wind turbine design. We discuss the variations in data availability and accuracy based on an analysis of the carrier-to-noise ratio (CNR) distribution and the goodness of fit for curve fitting via the VVP method.

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“…Classic anemometry is limited in its possibilities to study the induction zone of a wind farm since just a limited number of masts can be placed in front of it due to mainly financial constraints. In the last decade, the remote sensing methods Doppler wind lidar (light detection and ranging) has become a common tool in many fields of wind energy research and applications (Hasager and Sjöholm, 2019). Lidar devices offer the possibility to scan whole wind fields with ranges of several kilometres.…”

Section: Introductionmentioning

confidence: 99%

Offshore wind farm global blockage measured with scanning lidar

Schneemann¹,

Theuer²,

Rott³

et al. 2020

Preprint

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Abstract. The objective of this paper was the experimental investigation of the accumulated induction effect of a large offshore wind farm as a whole, i.e. the global blockage effect, in relation to atmospheric stability estimates and wind farm operational states. We measured the inflow of a 400 MW offshore wind farm in the German North Sea with a scanning long-range Doppler wind lidar. A methodology to reduce the statistical variability of different lidar scans at comparable measurement conditions was introduced and an extensive uncertainty assessment of the averaged wind fields was performed to be able to identify the global blockage effect which is small compared to e.g. wind turbine wake effects and ambient variations in the inflow. Our results showed a significant decrease in wind speed at platform height in front of the wind farm of 4.5 % within an accuracy range between 2.5 % and 6.5 % with the turbines operating at high thrust coefficients in a stably stratified atmosphere, which we interpreted as global blockage. In contrast, at unstable stratification and similar operating conditions we identified no wind speed deficit. We discussed the significance of our measurements, possible sources of error in long-range scanning lidar campaigns and give recommendations how to measure small flow effects like global blockage with scanning Doppler lidar. In conclusion, we provide strong evidence for the existence of global blockage in large offshore wind farms in stable stratification and the turbines operating at a high thrust coefficient by planar lidar wind field measurements. We conclude that global blockage is dependant on atmospheric stratification.

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Editorial for the Special Issue “Remote Sensing of Atmospheric Conditions for Wind Energy Applications”

Cited by 5 publications

References 31 publications

Motion Blur Removal for Uav-Based Wind Turbine Blade Images Using Synthetic Datasets

Motion Blur Removal for Uav-Based Wind Turbine Blade Images Using Synthetic Datasets

Coastal Wind Measurements Using a Single Scanning LiDAR

Offshore wind farm global blockage measured with scanning lidar

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