Wind turbine blade shear web disbond detection using rotor blade operational sensing and data analysis

Area of Science:

• Structural dynamics
• Wind energy engineering
• Damage detection

Background:

• Wind turbine blades are critical components susceptible to structural damage.
• Shear web disbonds can compromise blade integrity and operational safety.
• Reliable methods for detecting and quantifying these defects are essential for predictive maintenance.

Purpose of the Study:

• To develop and validate algorithms for detecting and quantifying shear web disbonds in wind turbine blades.
• To characterize the structural dynamic response indicative of disbond presence and severity.
• To assess the robustness of detection algorithms under various aerodynamic conditions.

Main Methods:

• Finite element modeling of a 5 MW offshore wind turbine blade.
• Simulation of shear web disbond scenarios with varying lengths.
• Analysis of flap-wise acceleration and root-pitching moment data.
• Aerodynamic sensitivity studies to validate algorithm performance.

Main Results:

• Flap-wise acceleration and root-pitching moment were identified as key indicators of shear web disbonds.
• A combined algorithm using blade and non-blade measurements achieved 100% probability of detection in optimized wind speed ranges.
• Detection performance was evaluated under laminar, 30% horizontal shear, and 60% horizontal shear conditions.

Conclusions:

• The developed algorithms effectively detect and quantify shear web disbonds in wind turbine blades.
• Structural dynamic response measurements provide reliable data for damage assessment.
• The findings contribute to enhanced structural health monitoring and maintenance strategies for wind turbines.