Damage detection method for wind turbine blades based on dynamics analysis and mode shape difference curvature information

• Proposing a new damage detection method for wind turbine blades.
• A reasonably finite element model was carried out using model updating technique.
• The locations and sizes of damages are detected through dynamics analysis and MSDC.
• This method is an efficient tool for the condition monitoring of wind turbine blade.

Blades are among the key components of wind turbines. Blade damage is one of the most common types of structural defects and can cause catastrophic structural failure. Therefore, it is highly desirable to detect and diagnose blade damage as early as possible. In this paper, we propose a method for blade damage detection and diagnosis. This method incorporates finite element method (FEM) for dynamics analysis (modal analysis and response analysis) and the mode shape difference curvature (MSDC) information for damage detection/diagnosis. Finite element models of wind turbine blades have been built and modified via frequency comparison with experimental data and the formula for the model updating technique. Our numerical simulation results have demonstrated that the proposed technique can detect the spatial locations of damages for wind turbine blades. Changes in natural frequencies and modes for smaller size blades with damage are found to occur at lower frequencies and lower modes than in the larger sized blade case. The relationship between modal parameters and damage information (location, size) is very complicated especially for larger size blades. Moreover, structure and dynamic characters for larger size blades are different from those for smaller sized blades. Therefore, dynamic response analysis for a larger sized wind turbine blade with a multi-layer composite material based on aerodynamic loads’ (including lift forces and drag forces) calculation has been carried out and improved the efficiency and precision to damage detection by combining (MSDC) information. This method provides a low cost and efficient non-destructive tool for wind turbine blade condition monitoring.