Using Bayesian updating for monopile offshore wind turbines monitoring

Abstract

Digital twins (DTs) are thought to be promising tools for offshore wind turbines (OWTs) in real-time updating, optimized design, intelligent operation, and maintenance. When creating DT analysis tools, it is critical to update model parameters to create high-fidelity, high-accuracy virtual models. In this paper, a Bayesian updating framework based on Transitional Markov Chain Monte Carlo (TMCMC) is proposed to construct the desired virtual model of monopile OWTs to address this difficult problem. The likelihood function for Bayesian inference was created using measurements from physical entity monitoring as well as natural frequencies and damping ratios from the monopile OWT finite element (FE) model. Then, through a series of relatively simple intermediate likelihood functions, it gradually converges to the target function. During this time, the artificial neural network (ANN) surrogate model calculates the intermediate likelihood function of each sample, and a parallel computing strategy is used to enhance the computational efficiency. The results show that the posterior distribution of the uncertain parameters can be successfully estimated from the Markov chain using this method to update the finite element model. In addition, the proposed Bayesian updating method is resistant to measurement noise.