Virtual sensing of subsoil strain response in monopile-based offshore wind turbines via Gaussian process latent force models

TL;DR

This paper presents an in-situ validation of a Gaussian process latent force model for virtual sensing of subsoil strain in offshore wind turbines, demonstrating its effectiveness in estimating unmeasured strain responses.

Contribution

It provides one of the first real-world validations of the GPLFM technique for strain estimation in offshore wind turbines, combining physics-based and data-driven models.

Findings

GPLFM accurately estimates subsoil strain responses.

The method outperforms traditional virtual sensing techniques.

Efficient Bayesian inference is achieved via Kalman filtering.

Abstract

Virtual sensing techniques have gained traction in applications to the structural health monitoring of monopile-based offshore wind turbines, as the strain response below the mudline, which is a primary indicator of fatigue damage accumulation, is impractical to measure directly with physical instrumentation. The Gaussian process latent force model (GPFLM) is a generalized Bayesian virtual sensing technique which combines a physics-driven model of the structure with a data-driven model of latent variables of the system to extrapolate unmeasured strain states. In the GPLFM, modeling of unknown sources of excitation as a Gaussian process (GP) serves to facilitate strain estimation by providing a complete stochastic characterization of the covariance relationship between input forces and states, using properties of the GP covariance kernel as well as correlation information supplied by the mechanical model. It is shown that posterior inference of the latent inputs and states is performed by Gaussian process regression of measured accelerations, computed efficiently using Kalman filtering and Rauch-Tung-Striebel smoothing in an augmented state-space model. While the GPLFM has been previously demonstrated in numerical studies to improve upon other virtual sensing techniques in terms of accuracy, robustness, and numerical stability, this work provides one of the first cases of in-situ validation of the GPLFM. The predicted strain response by the GPLFM is compared to subsoil strain data collected from an operating offshore wind turbine in the Westermeerwind Park in the Netherlands.