Simulated Wind Turbine Blade Damage Detection Data

Encompassing simulated structural dynamic response data from a 5 MW offshore wind turbine model, created to develop algorithms for detecting shear web disbond damage. The analysis used blade flap-wise acceleration and root-pitching moment as key indicators, achieving 100% probability of detection under optimized wind speed ranges in specific shear conditions. The data was generated by Noah J. Myrent and published in 2020.

Use Cases

• Train a classifier to detect shear web disbond presence using blade flap-wise acceleration and root-pitching moment signals.
• Develop regression models to quantify disbond length severity based on the blade's structural dynamic response.
• Analyze the robustness of damage detection algorithms across different wind conditions, including laminar and horizontal shear flows.
• Validate condition monitoring systems by correlating simulated blade measurements with disbond severity levels.

Strengths

• Data is based on a detailed 5 MW offshore utility-scale wind turbine computer model.
• Detection algorithms achieved 100% probability of detection under specified optimized wind speed ranges.
• Analysis includes sensitivity studies across laminar, 30% horizontal shear, and 60% horizontal shear conditions.

Limitations

• Data is entirely simulated from computer models, not measured from physical turbines.
• The scope is limited to a specific damage type (shear web disbond) on a single turbine model.
• The dataset's size, format, and specific column structure are unknown.

Provenance

Source

Noah J. Myrent via Dryad.

Collection Method

Data generated from computer simulations of a 5 MW offshore wind turbine model.

Freshness

Last updated in 2020.

Geography

Model based on an offshore utility-scale wind turbine; specific location not stated.