Abstract Summary
New wind power installations surpassed 90 GW in 2020 – a 53% growth compared to 2019. This magnitude of growth is necessary to meet the ambitious COP26 global climate targets whilst maintaining the supply stability for an ever increasing environmentally conscious consumer population. To increase energy capture and cost efficiency, turbines are being located in remote offshore locations, with their blades being constructed from lighter, more flexible materials. In general, monitoring of turbines remains a manual process with inspections carried out at pre defined intervals driving operation and maintenance costs prohibitively high. This research will develop a vibration based structural health monitoring (VBSHM) methodology for remote monitoring and damage severity assessment of a laboratory scale wind turbine blade under simulated wind like excitation. The methodology will exploit the fact that structural degradation will manifest itself through a notable shift in pre defined damage sensitive features and use this to predict damage accrued on the structure. The finite element model updating (FEMU) procedure adopted involves the creation of a “digital twin” by minimising a fitness function containing the discrepancy between model responses and observed dynamic responses. The application of deterministic FEMU can be considered idealistic as uncertainty can have a non negligible influence on the accuracy of the final solution. To this end, the authors incorporated non probabilistic fuzzy theory, modelling membership functions of output parameters to build membership functions associated with input parameters. This accounts for limitations associated with determinism and enables modelling and measurement errors to be accounted for in a meaningful way. The method was demonstrated on a 2.36m blade from a 5kW domestic wind turbine subject to wind like excitation. Operational modal analysis techniques were used to obtain dynamic responses of the structure with metaheuristic optimization algorithms implemented to calibrate the numerical models using a modified version of the Abaqus2matlab toolbox. Through this process, a digital twin of the baseline structure was successfully constructed, with longitudinal modulus and shear modulus calibrated to reduce the maximum percentage deviation in natural frequencies from 19.4% to 1.4%. This calibrated model was then used as a baseline for further damage detection studies. To facilitate damage severity assessment non destructively, two typical field observed damages were considered. Localized stiffness reduction, comparable to transverse cracking, was replicated by adding small masses to the blade whilst gradual boundary degradation was simulated through addition of neoprene sheet to increase joint flexibility. The VBSHM developed was able to detect with sufficient accuracy all five damage scenarios (0.05kg, 0.10kg, 0.20kg and 0.40kg on the blade’s trailing edge only and 0.20kg on both trailing and leading edges). Benefits of considering uncertainty were demonstrated through creation of membership functions for each scenario to prevent false alarms and provide confidence in the results. Boundary degradation was successfully identified experimentally however the analytical sensitivity of responses to variation in rotational and translational springs was insufficient to facilitate updating using the analytical model created. This contribution highlights the ability to account for uncertainties in a non computationally expensive and intuitive way and
