Structural health monitoring in the context of a Micon 65/13 horizontal axis wind turbine was described in this paper as a process in statistical pattern recognition. Simulation data from a calibrated model with less than 8% error in the fi rst 14 natural frequencies of vibration was used to study the operational response under various wind states as well as the effects of three types of damage in the blade, low speed shaft and yaw joint. It was shown that vertical wind shear and turbulent winds lead to different modal contributions in the operational response of the turbine suggesting that the sensitivity of operational data to damage depends on the wind loads. It is also shown that there is less than a 4% change in the wind turbine natural frequencies given a 25% reduction in the stiffness at the root of one blade. The modal assurance criterion was used to analyse the corresponding changes in modal defl ections, and this criterion exhibited nearly orthogonal changes because of the three damage scenarios suggesting that the modal defl ection determines which damage is observable at a given frequency for a given wind state. The` modal contribution is calculated as a damage feature, which changes as much as 100% for 50% reductions in blade root stiffness, but only the blade damage is detected using this feature. Operational data was used to study variations in the forced blade response to determine the likelihood that small levels of damage can be detected amidst variations in wind speed across the rotor plane. The standard deviation in measured data was shown to be smallest for the span and edge-wise measurements at 1P due to gravity, which provides the dominant forcing function at this frequency. A 3% change in the response in the span and edge-wise directions because of damage is required to detect a change of three standard deviations in contrast to the 90% change in fl ap direction response that is required to detect a similar change because of damage. The dynamic displacement in the span direction is then used to extract a damage feature from the simulation data that provides the ability to both locate and quantify the reduction in stiffness in the blade root.

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The research presented in this article focuses on a 9-m CX-100 wind turbine blade, designed by a team led by Sandia National Laboratories and manufactured by TPI Composites Inc. The key difference between the 9-m blade and baseline CX-100 blades is that this blade contains fabric wave defects of controlled geometry inserted at specified locations along the blade length. The defect blade was tested at the National Wind Technology Center at the National Renewable Energy Laboratory using a schedule of cycles at increasing load level until failure was detected. Researchers used digital image correlation, shearography, acoustic emission, fiber-optic strain sensing, thermal imaging, and piezoelectric sensing as structural health monitoring techniques. This article provides a comparison of the sensing results of these different structural health monitoring approaches to detect the defects and track the resultant damage from the initial fatigue cycle to final failure.

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This accounts for 1.25% of all U.S. electricity generated and enough to power 7 million homes. As wind energy becomes a key player in power generation and in the economy, so does the performance and reliability of wind turbines. To improve both performance and reliability, smart rotor blades are being developed that collocate reference measurements, aerodynamic actuation, and control on the rotor blade. Towards the development of a smart blade, SNL has fabricated a sensored rotor blade with embedded distributed accelerometer measurements to be used with operational loading methods to estimate the rotor blade deflection and dynamic excitation. These estimates would serve as observers for future smart rotor blade control systems. An accurate model of the rotor blade was needed for the development of the operational monitoring methods. An experimental modal analysis of the SNL sensored rotor blade (a modified CX-100 rotor blade) with embedded DC accelerometers was performed when hung with free boundary conditions and when mounted to a Micon 65/13 wind turbine. The modal analysis results and results from a static pull test were used to update an existing distributed parameter CX-100 rotor analytical blade model. This model was updated using percentage error estimates from cost functions of the weighted residuals. The model distributed stiffness parameters were simultaneously updated using the static and dynamic experimental results. The model updating methods decreased all of the chosen error metrics and will be used in future work to update the edge-wise model of the rotor blade and the full turbine model.