Session: 09-01-10: Offshore Wind Energy - Data science and Digital twins

Paper Number: 102743

102743 - Bayesian Network Modelling of Aero-Mechanical Performance of Wind Turbine 

Aero-mechanical performance of a wind turbine such as power output, environmental loadings, structural deformation and stress rapidly change during operation due to the variable nature of environment, rotation of the rotor or other active/ passive controls. Being able to obtain these performance indicators and/ or infer abnormalities in real time would be very helpful for operation management or maintenance scheduling for a wind turbine or farm. In this paper, we demonstrate a Bayesian Network (BN) model that returns these indicators in terms of range of values and likelihoods of occurrence from the inputs of operating conditions, i.e. inflow wind speed, rotating speed, tilt and pitch angles. The BN could also be used to derive operating conditions (i.e. rotation speed or pitch angle of blade) that maximize the possibility of achieving a desired power output while subject to certain turbine structural conditions.

The BN model is built from a large database of high-fidelity Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA) simulations of a 5MW NREL wind turbine. The abnormality is assumed to be in the form of abnormal deformation and/ or stress that are numerically generated by introducing corresponding faults in the blade structure. The database generation, construction and demonstration of the BN model will be presented in the paper.

Presenting Author: My Ha Dao Institute of High Performance Computing

Presenting Author Biography: My Ha Dao is a Scientist at the Institute of High Performance Computing (IHPC), A*STAR. He is also a Principal Investigator on several projects with industry partners, developing computational fluid dynamics models for simulations of wave, current and wave-current interaction in real oceans and artificial deep-water basins. His research interests extend to physics-based, data driven and artificial intelligence models that complement high fidelity computational fluid dynamics in aerospace, marine and offshore, and additive manufacturing applications.

Authors:

My Ha Dao Institute of High Performance Computing
Quang Tuyen Le Institute of High Performance Computing
Xiang Zhao Institute of High Performance Computing
Chin Chun Ooi Institute of High Performance Computing
Trung Pham Duong Luu Singapore University of Technology and Design
Nagarajan Raghavan Singapore University of Technology and Design