Data-driven virtual sensing and dynamic strain estimation for fatigue analysis of offshore wind turbine using principal component analysis

Marius Tarpø*, Sandro Amador, Evangelos Katsanos, Mattias Skog, Johan Gjødvad, Rune Brincker

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Virtual sensing enables estimation of stress in unmeasured locations of a system using a system model, physical sensors and a process model. The system model holds the relationship between the physical sensors and the desired stress response. A process model processes both the physical sensors and the system model to synthesise virtual sensors that ‘measure’ the desired stress response. Thus, virtual sensing enables mapping between the physical sensors (input) and the desired stress response (output). The system model is a mathematical model of the system based on knowledge or data of the system. Here, the data-driven system model is constructed directly on data analyses for the specific system. In this paper, supervised learning and data-driven system models are applied to strain estimation of an offshore wind turbine in the dynamic range through a novel use of principal component analysis (PCA); 40 min of training data is used to establish the data-driven system model that can estimate the dynamic strain response with high precision for 2 months, while the estimated fatigue damage averaged out to −1.76% of the measured strain response.

Original languageEnglish
JournalWind Energy
Number of pages12
ISSN1095-4244
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Data-driven model
  • Principal component analysis
  • Stress estimation
  • Structural health monitoring
  • Supervised learning
  • Virtual sensing

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