Distributed Virtual Sensing via Bayesian Filtering for Wind Energy Structures
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2022-07-04
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Master Thesis
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Abstract
Nowadays, monitoring systems are used in wind turbine structures as an early warning system to detect structural defects and track the accumulated fatigue damage. This already available data could be further used to sense the climatic conditions such as wave loads or wind pressure in otherwise inaccessible locations like the ocean. The goal of this thesis is to simultaneously estimate spatially distributed loads and full-field vibration responses by applying Bayesian filtering with physics-based models, defining the structural dynamics, and data-driven Gaussian processes. The input varying in time and space is described by a Gaussian process regression model also known as kriging, where the distributed input signals are wave loads. Wind force at the top of the wind turbine tower is added to consider an operational wind turbine and two different types of loading. This spatio-temporal model is then combined with the Kalman filter to deal with the input-state estimation problem. Two approaches are examined: the dual Kalman filter and generalized least squares estimator. Before training the Gaussian process regression model, a suitable kernel and basis functions are selected. The algorithm is first applied to the case of available input measurements and afterwards also to unknown input measurements, which are derived from acceleration measurements. If input measurements are available, the generalized least squares estimator is the optimal predictor for both input and state estimation. Furthermore, fewer covariance matrices need to be tuned compared with the dual Kalman filter. On the contrary, if there are no input measurements, the dual Kalman filter performs better in the identification of the system’s inputs for the full-order model and even if the wind force is considered. However, in the latter case, the large noise from the acceleration measurement for the wind force needs to be smoothed out by an additional filter, the Savitzky-Golay filter. For the modally reduced-order model, the input-state estimation is very poor. To validate this approach, the algorithm should be applied to real-world or experimental data.
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Examiner : Tatsis, Konstantinos
Examiner : Smyth, Andrew W.
Examiner: Chatzi, Eleni
Examiner : Dertimanis, Vasileios K.
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ETH Zurich
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Subject
Bayesian filtering; wind energy; Kalman filter; Virtual sensing
Organisational unit
03890 - Chatzi, Eleni / Chatzi, Eleni