Gaussian Process Latent Force Models for Virtual Sensing in a Monopile-Based Offshore Wind Turbine

Joanna Zou; Alice Cicirello; Alexandros Iliopoulos; Eliz Mari Lourens

doi:10.1007/978-3-031-07254-3_29

Gaussian Process Latent Force Models for Virtual Sensing in a Monopile-Based Offshore Wind Turbine

Joanna Zou^*, Alice Cicirello, Alexandros Iliopoulos, Eliz Mari Lourens

^*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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Abstract

Fatigue assessment in offshore wind turbine support structures requires the monitoring of strains below the mudline, where the highest bending moments occur. However, direct measurement of these strains is generally impractical. This paper presents the validation of a virtual sensing technique based on the Gaussian process latent force model for dynamic strain monitoring. The dataset, taken from an operating near-shore turbine in the Westermeerwind Park in the Netherlands, provides a unique opportunity for validation of strain estimates at locations below the mudline using strain gauges embedded within the monopile foundation.

Original language	English
Title of host publication	European Workshop on Structural Health Monitoring, EWSHM 2022, Volume 1
Editors	Piervincenzo Rizzo, Alberto Milazzo
Publisher	Springer
Pages	290-298
Number of pages	9
ISBN (Print)	978-303107253-6
DOIs	https://doi.org/10.1007/978-3-031-07254-3_29
Publication status	Published - 2022
Event	10th European Workshop on Structural Health Monitoring, EWSHM 2022 - Palermo, Italy Duration: 4 Jul 2022 → 7 Jul 2022

Publication series

Name	Lecture Notes in Civil Engineering
Volume	253 LNCE
ISSN (Print)	2366-2557
ISSN (Electronic)	2366-2565

Conference

Conference	10th European Workshop on Structural Health Monitoring, EWSHM 2022
Country/Territory	Italy
City	Palermo
Period	4/07/22 → 7/07/22

Keywords

Bayesian inference
Gaussian process
Offshore wind turbines
Virtual sensing

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10.1007/978-3-031-07254-3_29

EWSHM_2022_Paper_Final_ACAccepted author manuscript, 1.02 MB

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Zou, J., Cicirello, A., Iliopoulos, A., & Lourens, E. M. (2022). Gaussian Process Latent Force Models for Virtual Sensing in a Monopile-Based Offshore Wind Turbine. In P. Rizzo, & A. Milazzo (Eds.), European Workshop on Structural Health Monitoring, EWSHM 2022, Volume 1 (pp. 290-298). (Lecture Notes in Civil Engineering; Vol. 253 LNCE). Springer. https://doi.org/10.1007/978-3-031-07254-3_29

@inproceedings{9244f01eca70459583c87e6cfd6800d3,

title = "Gaussian Process Latent Force Models for Virtual Sensing in a Monopile-Based Offshore Wind Turbine",

abstract = "Fatigue assessment in offshore wind turbine support structures requires the monitoring of strains below the mudline, where the highest bending moments occur. However, direct measurement of these strains is generally impractical. This paper presents the validation of a virtual sensing technique based on the Gaussian process latent force model for dynamic strain monitoring. The dataset, taken from an operating near-shore turbine in the Westermeerwind Park in the Netherlands, provides a unique opportunity for validation of strain estimates at locations below the mudline using strain gauges embedded within the monopile foundation.",

keywords = "Bayesian inference, Gaussian process, Offshore wind turbines, Virtual sensing",

author = "Joanna Zou and Alice Cicirello and Alexandros Iliopoulos and Lourens, {Eliz Mari}",

year = "2022",

doi = "10.1007/978-3-031-07254-3_29",

language = "English",

isbn = "978-303107253-6",

series = "Lecture Notes in Civil Engineering",

publisher = "Springer",

pages = "290--298",

editor = "Piervincenzo Rizzo and Alberto Milazzo",

booktitle = "European Workshop on Structural Health Monitoring, EWSHM 2022, Volume 1",

note = "10th European Workshop on Structural Health Monitoring, EWSHM 2022 ; Conference date: 04-07-2022 Through 07-07-2022",

}

Zou, J, Cicirello, A, Iliopoulos, A & Lourens, EM 2022, Gaussian Process Latent Force Models for Virtual Sensing in a Monopile-Based Offshore Wind Turbine. in P Rizzo & A Milazzo (eds), European Workshop on Structural Health Monitoring, EWSHM 2022, Volume 1. Lecture Notes in Civil Engineering, vol. 253 LNCE, Springer, pp. 290-298, 10th European Workshop on Structural Health Monitoring, EWSHM 2022, Palermo, Italy, 4/07/22. https://doi.org/10.1007/978-3-031-07254-3_29

Gaussian Process Latent Force Models for Virtual Sensing in a Monopile-Based Offshore Wind Turbine. / Zou, Joanna; Cicirello, Alice; Iliopoulos, Alexandros et al.
European Workshop on Structural Health Monitoring, EWSHM 2022, Volume 1. ed. / Piervincenzo Rizzo; Alberto Milazzo. Springer, 2022. p. 290-298 (Lecture Notes in Civil Engineering; Vol. 253 LNCE).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Gaussian Process Latent Force Models for Virtual Sensing in a Monopile-Based Offshore Wind Turbine

AU - Zou, Joanna

AU - Cicirello, Alice

AU - Iliopoulos, Alexandros

AU - Lourens, Eliz Mari

PY - 2022

Y1 - 2022

N2 - Fatigue assessment in offshore wind turbine support structures requires the monitoring of strains below the mudline, where the highest bending moments occur. However, direct measurement of these strains is generally impractical. This paper presents the validation of a virtual sensing technique based on the Gaussian process latent force model for dynamic strain monitoring. The dataset, taken from an operating near-shore turbine in the Westermeerwind Park in the Netherlands, provides a unique opportunity for validation of strain estimates at locations below the mudline using strain gauges embedded within the monopile foundation.

AB - Fatigue assessment in offshore wind turbine support structures requires the monitoring of strains below the mudline, where the highest bending moments occur. However, direct measurement of these strains is generally impractical. This paper presents the validation of a virtual sensing technique based on the Gaussian process latent force model for dynamic strain monitoring. The dataset, taken from an operating near-shore turbine in the Westermeerwind Park in the Netherlands, provides a unique opportunity for validation of strain estimates at locations below the mudline using strain gauges embedded within the monopile foundation.

KW - Bayesian inference

KW - Gaussian process

KW - Offshore wind turbines

KW - Virtual sensing

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U2 - 10.1007/978-3-031-07254-3_29

DO - 10.1007/978-3-031-07254-3_29

M3 - Conference contribution

AN - SCOPUS:85134303763

SN - 978-303107253-6

T3 - Lecture Notes in Civil Engineering

SP - 290

EP - 298

BT - European Workshop on Structural Health Monitoring, EWSHM 2022, Volume 1

A2 - Rizzo, Piervincenzo

A2 - Milazzo, Alberto

PB - Springer

T2 - 10th European Workshop on Structural Health Monitoring, EWSHM 2022

Y2 - 4 July 2022 through 7 July 2022

ER -

Gaussian Process Latent Force Models for Virtual Sensing in a Monopile-Based Offshore Wind Turbine

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