scholarly journals Computational Investigation of the Causes of Wind Turbine Blade Damage at Japan’s Wind Farm in Complex Terrain

2018 ◽  
Vol 06 (03) ◽  
pp. 152-167 ◽  
Author(s):  
Takanori Uchida
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Complex Terrain ◽  
Wind Farm ◽  
Wind Turbine Blade ◽  
Computational Investigation

Research on Wind Turbine Blade Surface Damage Fault On-Line Monitoring and Diagnosis System

2013 ◽  
Author(s):  
Yongxin Feng ◽  
Tao Yang ◽  
Xiaowen Deng ◽  
Qingshui Gao ◽  
Chu Zhang ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Farm ◽  
Turbine Blades ◽  
Surface Damage ◽  
Wind Turbine Blade ◽  
Offshore Wind ◽  
Blade Surface ◽  
Diagnosis System ◽  
On Line

The basic fault types of wind turbine blades are introduced, a novel blade surface damage detection method based on machine vision is being suggested. The network of wind turbine blade surface damage fault on-line monitoring and fault diagnosis system has already been developed. The system architecture, software modules and functions are described, and given application example illustrates the usefulness and effectiveness of this system. The result shows that this system can monitor the surface damage failure of the blade in real time, and can effectively reduce the blade’s maintenance costs for wind farms, especially offshore wind farm.

scholarly journals Numerical Reproducibility of Terrain-induced Turbulence in Complex Terrain by Large-eddy Simulation (LES) Technique

2018 ◽  
Author(s):  
Takanori UCHIDA
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Farm ◽  
Simulated Data ◽  
Wind Turbine Blade ◽  
Eddy Simulation ◽  
Minute Period ◽  
Large Eddy ◽  
Horizontal Grid ◽  
Simulated Time

In the present study, field observation wind data from the time of the wind turbine blade damage accident on Shiratakiyama Wind Farm were analyzed in detail. In parallel, high-resolution LES turbulence simulations were performed in order to examine the model’s ability to numerically reproduce terrain-induced turbulence. The comparison of the observed and simulated time series (1 second average values) from a 10 minute period from the time of the accident led to the conclusion that the settings of the horizontal grid resolution and time increment are important to numerically reproduce the terrain-induced turbulence that caused the wind turbine blade damage accident on Shiratakiyama Wind Farm. A spectral analysis of the same set of observed and simulated data revealed that the simulated data reproduced the energy cascade of the actual terrain-induced turbulence well.

scholarly journals Radar-derived precipitation climatology for wind turbine blade leading edge erosion

2020 ◽  
Vol 5 (1) ◽  
pp. 331-347 ◽  
Author(s):  
Frederick Letson ◽  
Rebecca J. Barthelmie ◽  
Sara C. Pryor
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Farm ◽  
Leading Edge ◽  
Wind Turbine Blade ◽  
Novel Approach ◽  
The Usa ◽  
Farm Operators ◽  
Deployment Planning ◽  
Blade Leading Edge

Abstract. Wind turbine blade leading edge erosion (LEE) is a potentially significant source of revenue loss for wind farm operators. Thus, it is important to advance understanding of the underlying causes, to generate geospatial estimates of erosion potential to provide guidance in pre-deployment planning, and ultimately to advance methods to mitigate this effect and extend blade lifetimes. This study focuses on the second issue and presents a novel approach to characterizing the erosion potential across the contiguous USA based solely on publicly available data products from the National Weather Service dual-polarization radar. The approach is described in detail and illustrated using six locations distributed across parts of the USA that have substantial wind turbine deployments. Results from these locations demonstrate the high spatial variability in precipitation-induced erosion potential, illustrate the importance of low-probability high-impact events to cumulative annual total kinetic energy transfer and emphasize the importance of hail as a damage vector.

scholarly journals Wind Turbine Blade Nondestructive Testing with a Transportable Radiography System

2011 ◽  
Vol 2011 ◽  
pp. 1-6 ◽  
Author(s):  
J. G. Fantidis ◽  
C. Potolias ◽  
D. V. Bandekas
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Turbines ◽  
Wind Farm ◽  
Neutron Radiography ◽  
Wind Turbine Blade ◽  
Operating Life ◽  
Lightning Strikes ◽  
Radiography System ◽  
Wide Range

Wind turbines are becoming widely used as they are an environmentally friendly way for energy production without emissions; however, they are exposed to a corrosive environment. In addition, as wind turbines typically are the tallest structures in the surrounding area of a wind farm, it is expected that they will attract direct lightning strikes several times during their operating life. The purpose of this paper is to show that the radiography with a transportable unit is a solution to find defects in the wind turbine blade and reduce the cost of inspection. A transportable neutron radiography system, incorporating an Sb–Be source, has been simulated using the MCNPX code. The simulated system has a wide range of radiography parameters.

scholarly journals Study on wind turbine blade defect detection system based on imaging array

2019 ◽  
Vol 118 ◽  
pp. 02041 ◽  
Author(s):  
Ningning Zhang ◽  
Chengzhi Lu ◽  
Anmin Wang
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Defect Detection ◽  
Wind Farm ◽  
Detection Efficiency ◽  
Detection System ◽  
Turbine Blades ◽  
Wind Turbine Blade ◽  
Quality Analysis ◽  
Defect Recognition

Currently in the process of wind farm inspection, wind turbine blade appearance inspection mainly adopts the telescope or high-definition cameras, low detection efficiency, labor intensity and the precision is limited, in order to solve this problem, a kind of wind turbine blades defect recognition system based on image array is proposed. Through the joint of array camera and image processing server, the functions of the image acquisition, processing, and defect recognition and detection results output are implemented. The software of artificial intelligence deep learning based on neural network algorithm is used to identify the defects of blade image, and output quality analysis report, to realize automatic detection of wind turbine blade surface defect. The field measurement results show that the system greatly improves the efficiency and accuracy of wind turbine blade defect detection.

scholarly journals Numerical Investigation of Terrain-Induced Turbulence in Complex Terrain by Large-Eddy Simulation (LES) Technique

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2638 ◽  
Author(s):  
Takanori Uchida
Keyword(s):  
Wind Speed ◽  
Large Eddy Simulation ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Turbulence Intensity ◽  
Field Observation ◽  
Wind Farm ◽  
Wind Turbine Blade ◽  
Eddy Simulation ◽  
Large Eddy

In the present study, field observation wind data from the time of the wind turbine blade damage accident on Shiratakiyama Wind Farm were analyzed in detail. In parallel, high-resolution large-eddy simulation (LES) turbulence simulations were performed in order to examine the model’s ability to numerically reproduce terrain-induced turbulence (turbulence intensity) under strong wind conditions (8.0–9.0 m/s at wind turbine hub height). Since the wind velocity and time acquired from the numerical simulation are dimensionless, they are converted to full scale. As a consequence, both the standard deviation of the horizontal wind speed (m/s) and turbulence intensity evaluated from the field observation and simulated wind data are successfully in close agreement. To investigate the cause of the wind turbine blade damage accident on Shiratakiyama Wind Farm, a power spectral analysis was performed on the fluctuating components of the observed time series data of wind speed (1 s average values) for a 10 min period (total of 600 data) by using a fast Fourier transform (FFT). It was suggested that the terrain-induced turbulence which caused the wind turbine blade damage accident on Shiratakiyama Wind Farm was attributable to rapid wind speed and direction fluctuations which were caused by vortex shedding from Tenjogadake (elevation: 691.1 m) located upstream of the wind farm.

Noise analysis of the wind turbine blade

2013 ◽  
Author(s):  
Gwochung Tsai ◽  
Yita Wang ◽  
Yuhchung Hu ◽  
Jaching Jiang
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Noise Analysis ◽  
Wind Turbine Blade

DYNAMIC ANALYSIS OF THE FLUID FLOW OVER A WIND TURBINE BLADE

2017 ◽  
Author(s):  
Aldemir Ap Cavalini Jr ◽  
João Marcelo Vedovoto ◽  
Renata Rocha
Keyword(s):  
Fluid Flow ◽  
Dynamic Analysis ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Turbine Blade
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