Article (Scientific journals)
Farm-wide virtual load monitoring for offshore wind structures via Bayesian neural networks
Nandar, Hlaing; Morato, Pablo G.; Santos, Francisco de Nolasco et al.
2023In Structural Health Monitoring
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Keywords :
Offshore wind farm; Structural health monitoring; Virtual load monitoring; Bayesian neural networks; Uncertainty quantification; Structural fatigue
Abstract :
[en] Offshore wind structures are exposed to a harsh marine environment and are subject to deterioration mechanisms throughout their operational lifetime. Even if the deterioration evolution of structural elements can be estimated through physics-based deterioration models, the uncertainties involved in the process hurdle the selection of lifecycle management decisions, e.g., lifetime extension. In this scenario, the collection of relevant information through an efficient monitoring system enables the reduction of uncertainties, ultimately driving more optimal lifecycle decisions. However, a full monitoring instrumentation implemented on all wind turbines in a farm may become unfeasible due to practical and economical constraints. Besides, certain load monitoring systems often become defective after a few years of marine environment exposure. Addressing the aforementioned concerns, a farm-wide virtual load monitoring scheme directed by a fleet-leader wind turbine offers an attractive solution. Fetched with data retrieved from a fully instrumented wind turbine, a model can be first trained and then deployed, yielding load predictions for non-fully monitored wind turbines, from which only standard data are available, e.g., supervisory control and data acquisition. During the deployment stage, the pretrained virtual monitoring model may, however, receive previously unseen monitoring data, leading to inaccurate load predictions. In this article, we propose a virtual load monitoring framework formulated via Bayesian neural networks (BNNs) and provide relevant implementation details needed for the construction, training, and deployment of BNN data-based virtual monitoring models. As opposed to their deterministic counterparts, BNNs intrinsically announce the uncertainties associated with generated load predictions and allow to detect inaccurate load estimations generated for non-fully monitored wind turbines. The proposed virtual load monitoring is thoroughly tested through an experimental campaign in an operational offshore wind farm and the results demonstrate the effectiveness of BNN models for “fleet-leader”-based farm-wide virtual monitoring.
Disciplines :
Civil engineering
Author, co-author :
Nandar, Hlaing  ;  Université de Liège - ULiège > Urban and Environmental Engineering
Morato, Pablo G. ;  Department of Wind and Energy Systems, Technical University of Denmark, Roskilde, Denmark
Santos, Francisco de Nolasco;  OWI-Lab, Vrije Universiteit Brussel, Brussels, Belgium
Weijtjens, Wout;  OWI-Lab, Vrije Universiteit Brussel, Brussels, Belgium
Devriendt, Christof;  OWI-Lab, Vrije Universiteit Brussel, Brussels, Belgium
Rigo, Philippe  ;  Université de Liège - ULiège > Département ArGEnCo > ANAST (Systèmes de transport et constructions navales)
Language :
English
Title :
Farm-wide virtual load monitoring for offshore wind structures via Bayesian neural networks
Publication date :
24 August 2023
Journal title :
Structural Health Monitoring
ISSN :
1475-9217
eISSN :
1741-3168
Publisher :
SAGE Publications
Peer reviewed :
Peer Reviewed verified by ORBi
Funders :
SPF Economie - Service Public Fédéral Économie, PME, Classes moyennes et Énergie [BE]
Funding text :
Belgian Energy Transition Fund
Available on ORBi :
since 25 August 2023

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