03/18/2024
By Cinamon Blair
Mechanical & Industrial Engineering Lecture with Professor Babak Moaveni on System Identification of Offshore Wind Turbines Using Vibration and SCADA Measurements:
Date: March 29, 2024
Time: 1 p.m.
Location: Dandeneau Hall 220
For information: Alessandro_Sabato@uml.edu
Professor Moaveni highlights our efforts in system identification of three operational offshore wind turbines. The turbines are located in the first two offshore wind farms in the US, namely the Block Island Wind Farm (BIWF), and the Coastal Virginia Offshore Wind (CVOW), and one in the North Sea. I will give an overview of optimal sensor placement approach for instrumentation design and the installation of the sensors. I will then present our efforts for physics-based, data-driven, and hybrid (physics-base plus data-driven) system identification of these offshore turbines. Physic-based identification is performed through Bayesian model updating and Hierarchical Bayesian model updating where uncertain parameters of mechanics-based (finite element) models are estimated using measured data. Data-driven modeling is performed through sparse Gaussian Processes to infer the strain and moment response of a monopile OWT at its tower-base (as measured by strain gauges) using environmental and operational conditions from SCADA as model inputs. And the hybrid approach combines a Bayesian inference approach with a neural network. The presentation highlights advantages and shortcomings of the different approaches that we have used for system identification and structural health monitoring of these OWTs.
Babak Moaveni is a Professor of Civil and Environmental Engineering at Tufts University. His research interests include vibration-based systems and damage identification of structural systems as well as verification and validation of computational models. He is currently active in research projects on instrumentation and monitoring of offshore wind turbines.