The recent shift towards development of clean, sustainable energy sources has provided a new challenge in terms of structural safety and reliability: with aging, manufacturing defects, harsh environmental and operational conditions, and extreme events such as lightning strikes wind turbines can become damaged resulting in production losses and environmental degradation. To monitor the current structural state of the turbine, structural health monitoring (SHM) techniques would be beneficial. Physics based SHM in the form of calibration of a finite element model (FEMs) by inverse techniques is adopted in this research. Fuzzy finite element model updating (FFEMU) techniques for damage severity assessment of a small-scale wind turbine blade are discussed and implemented. The main advantage is the ability of FFEMU to account in a simple way for uncertainty within the problem of model updating. Uncertainty quantification techniques, such as fuzzy sets, enable a convenient mathematical representation of the various uncertainties. Experimental frequencies obtained from modal analysis on a small-scale wind turbine blade were described by fuzzy numbers to model measurement uncertainty. During this investigation, damage severity estimation was investigated through addition of small masses of varying magnitude to the trailing edge of the structure. This structural modification, intended to be in lieu of damage, enabled non-destructive experimental simulation of structural change. A numerical model was constructed with multiple variable additional masses simulated upon the blades trailing edge and used as updating parameters. Objective functions for updating were constructed and minimized using both particle swarm optimization algorithm and firefly algorithm. FFEMU was able to obtain a prediction of baseline material properties of the blade whilst also successfully predicting, with sufficient accuracy, a larger magnitude of structural alteration and its location.
Heather Turnbull and Piotr Omenzetter, "Damage severity assessment in wind turbine blade laboratory model through fuzzy finite element model updating," Proc. SPIE 10169, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2017, 101692E (Presented at SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring: March 29, 2017; Published: 19 April 2017); https://doi.org/10.1117/12.2257917.
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