Towards a Fleetwide Data-Driven Lifetime Assessment Methodology of Offshore Wind Support Structures Based on SCADA and SHM Data

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Abstract

In recent years there has been an increased interest of the offshore wind industry to use structural health monitoring (SHM) data in the assessment of consumed lifetime and lifetime extension for an entire wind farm. In order for operators, certifying bodies, insurance entities and government agencies to agree on a lifetime extension, a commonly accepted lifetime assessment strategy with proven results is required. This paper aims to provide such an answer through a data-driven lifetime assessment approach using SHM and SCADA data. The research involves training neural network (NN) models using SCADA and SHM data to estimate the fore-aft damage equivalent moment (DEM) at the tower interface level on a 10-min basis for implementation in a data-driven lifetime assessment. The NN are trained and validated based on one instrumented turbine (the fleetleader) and cross-validated based on another instrumented turbine. A DEM representative for the lifetime of the asset is calculated based on the 10-min DEM’s. An analysis of the NN models’ performance (error of 10-min DEM estimation in relation to DEM derived from SHM data) and accuracy (lifetime DEM error) is undertaken. The DEM representative for the lifetime of the assets is benchmarked with the as-designed DEM to assess the lifetime.
Original languageEnglish
Title of host publicationEuropean Workshop on Structural Health Monitoring
Subtitle of host publicationEWSHM 2022
EditorsPiervincenzo Rizzo, Alberto Milazzo
PublisherSpringer International Publishing
Pages123–132
Volume253
DOIs
Publication statusPublished - 2023
EventEuropean workshop on Structural Health Monitoring (2022) - Palermo, Italy
Duration: 4 Jul 20227 Jul 2022

Publication series

NameLecture Notes in Civil Engineering
PublisherSpringer International Publishing
Volume253

Conference

ConferenceEuropean workshop on Structural Health Monitoring (2022)
Abbreviated titleEWSHM 2022
CountryItaly
CityPalermo
Period4/07/227/07/22

Keywords

  • Offshore wind turbine support structures
  • Lifetime assessment
  • Neural networks

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