Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering Conference
Shanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

Probabilistic Virtual Load Monitoring of Offshore Wind Substructures:
A Supervised Learning Approach
Nandar Hlaing, Pablo G. Morato, Philippe Rigo
Naval and Offshore Engineering, ArGEnCo, University of Liege, Liege, Belgium

environmental loads and structural dynamics induced by turbine loads.
Strain gauges are often installed at fatigue critical locations to collectstructural response data that can be effectively utilized to reduce
uncertainties associated with fatigue deterioration.

ABSTRACTIn this work, a virtual load monitoring framework is proposed forderiving a mapping from either high or low frequency (1-Hz /10-minutetime averaged) SCADA data to load signals, while preserving the highfrequency dynamic components of the latter. Specifically, the proposed
virtual load monitoring scheme relies on a data-driven model thatreceives features retrieved from SCADA data and yields the probability
distribution of the structural response. The constituent neural networks
are trained via supervised learning based on the labelled data retrieved
while strain sensors are still functional, since at that operational stage,
both SCADA and structural response can be collected concurrently.
Once the strain sensors are not functional, the trained deep neuralnetwork is deployed, providing structural response predictions from onsite SCADA data. The proposed virtual monitoring approach is tested ona monopile-supported offshore wind turbine and cross-validated in termsof the predicted stress range distribution of a structural connection
located at the mudline. The results show good agreement between
structural response predictions and measurements, thus demonstrating
the efficacy and utility of the tested scheme.

An approach to reduce the epistemic uncertainty associated with thelong-term load distribution relying on strain monitoring data has been
introduced in (Morato P. G., 2021), within the proposed decision-making
framework for optimally managing offshore wind farms. In general, thelong-term stress range exhibited by offshore wind structural components
is typically described by a two-parameter Weibull distribution (DNV,2016; DNV, 2019). Due to the physical and model uncertainties
associated with load/ load effect estimations, a high coefficient ofvariation of the Weibull scale parameter is often assumed. The
uncertainty might also further increase during the structure’s operational
life as a result of the time-varying behavior of wind turbines, influenced
by variations in soil stiffness, scouring, etc. The stress range data
retrieved from strain monitoring can be used to update the prior belief
and to reduce the uncertainty of the Weibull scale parameter. In thiscontext, the uncertainty of strain monitoring can be characterized as theprobability distribution over the states of the ‘observed’ Weibull scaleparameter given the ‘true’ Weibull scale parameter. The measurement
quality of strain sensors combined with imperfect Weibull distribution
fitting are, therefore, inherently considered in the uncertainty of strain
monitoring. Bayesian inference is then conducted to update the scaleparameter through Weibull distribution fitting of stress ranges for acertain period of strain monitoring (e.g., 1 year).

KEY WORDS:

Load monitoring; Supervised learning; SCADA;
Neural networks; Fatigue; Offshore wind turbines.

INTRODUCTION
Manual or robotic inspections have conventionally been planned andconducted in order to collect useful information for the evaluation of theperformance and structural degradation of offshore wind turbines(OWTs). In the context of increasing wind farm installations in deeper
waters and farther from shore, monitoring solutions can provide timely
information on wind turbine performance and/or structural health. Alongwith the advancement of sensor technologies, various monitoring
instruments are currently installed on offshore wind turbines, e.g.,
SCADA system, accelerometers, and strain sensors. Monitoring fatigue
deterioration has become an important objective for assessing the healthof OWT support structures since they are exposed to the interaction ofIn several reported methods for the estimation of remaining useful
lifetime, the fatigue damage computed from strain monitoring dataserves as the basis to assess the remaining lifetime of the analyzed
offshore wind turbines. Short-term 10-minute damages are, for instance,computed from strain measurements using Miner's rule and binned into
different environmental conditions using SCADA and metocean data.
Estimating the occurrence probability of several environmental
conditions either from design documents or long-term monitoring ofSCADA data, the computed short-term 10-min damages are extrapolated
to lifetime damage. By comparing with the design values or target

3137