Results 111 to 120 of about 3,629 (222)

Segmented Gurney Flaps for Improved Wind Turbine Wake Recovery

Wind Energy, Volume 29, Issue 3, March 2026.
ABSTRACT When wind passes through the rotor of a wind turbine, the velocity is decreased while turbulence is increased. The region of decreased wind speed behind the rotor is known as the wind turbine wake and is bounded by a complex structure of helical vortices. This structure occurs to be more stable in low ambient turbulence and low tip speed ratio
Nirav Dangi, Koen Boorsma, Edwin Bot, Wim Bierbooms, Wei Yu   +4 more
wiley   +1 more source

On Wind Directions Estimated by Nacelle Lidar Under Different Reconstruction Methods

Wind Energy, Volume 29, Issue 3, March 2026.
ABSTRACT The wind direction is closely linked to the power performance and structural loads of wind turbines. Conventional nacelle‐mounted vanes or sonic anemometers face errors associated with airflow distortions caused by turbine blades. Nacelle‐mounted lidar systems offer line‐of‐sight speed measurements from multiple positions ahead of the rotor ...
Feng Guo, Wenhao Xu, Zhaoyu Zhang, David Schlipf, Paolo Schito, Alberto Zasso, Zhen Gao, Yujin Dong   +7 more
wiley   +1 more source

Out With the Old: Empirical Trends in U.S. Land‐Based Wind Turbine Decommissioning and Repowering

Wind Energy, Volume 29, Issue 3, March 2026.
ABSTRACT A growing number of wind turbines (WTs) across the globe are now reaching or exceeding their expected service lifetime; WT decommissioning is on the rise. Accordingly, questions pertaining to WT end‐of‐life have risen in importance in policy and practice. Yet, research on the various factors relating to WT decommissioning is relatively sparse.
Joseph Rand, Louisa Kramer, Ben Hoen, Jay Diffendorfer, Christopher Garrity   +4 more
wiley   +1 more source

TSG‐Net: A Multiscale Decomposition and Spatio‐Temporal Graph Neural Network Framework for High‐Precision Wind Power Forecasting

Wind Energy, Volume 29, Issue 3, March 2026.
ABSTRACT Wind energy's intermittency poses significant challenges for power grid stability. Existing forecasting methods exhibit notable limitations: traditional machine learning models struggle with long‐term temporal dependencies, while deep learning approaches often overlook spatial relationships among turbines.
YuChen Zhang
wiley   +1 more source

Multiclass Dataset for Intelligent Detection of Wind Turbine Blade Defects Using Drone Imagery. [PDF]

Sci Data
Ji L, Cheng J, Wu S.
europepmc   +1 more source

Modeling Temperature Responses of a Wind Turbine Blade Section Under Climate Chamber Conditions – Part 1: Challenges for FEM Simulations

Wind Energy, Volume 29, Issue 3, March 2026.
ABSTRACT The rapid expansion of wind energy infrastructure over the past 20–30 years has led up to a situation where advanced non‐destructive testing (NDT) technologies are the need‐of‐the‐hour, not only for new wind turbine blades (WTBs) that are being installed, but also for older infrastructure which is reaching their designed lifetime.
Somsubhro Chaudhuri, Rainer Krankenhagen, Ivana Lapšanská, Michael Stamm   +3 more
wiley   +1 more source

Optimization and Experimental Investigation of 3D Printed Micro Wind Turbine Blade Made of PLA Material. [PDF]

Materials (Basel), 2023
Arivalagan S, Sappani R, Čep R, Kumar MS.   +3 more
europepmc   +1 more source

Wind Turbine Blade Design [PDF]

, 2014
openaire   +1 more source

AI‐Driven Deep Learning Framework for Detecting Subtle Surface Defects on Wind Turbine Blades

Wind Energy, Volume 29, Issue 3, March 2026.
ABSTRACT Wind turbine blade surface defect detection is of great significance in ensuring the safety and operational efficiency of wind power systems. However, accurately detecting subtle and small‐scale defects remains challenging under complex imaging conditions.
Shoutu Li, Jiawei Pan, Ling Liu, Jin Zhao, Nianxi Yue, Zhen Wu   +5 more
wiley   +1 more source

Multi-scale defect detection technology for wind turbine blade surfaces based on the SASED-YOLO algorithm. [PDF]

Sci Rep
Lv F, Wang R, Wang Y, Zhou F, Bian X, Guo N.   +5 more
europepmc   +1 more source