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Offshore wind structures 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. 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 might 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 trained and then deployed, thus yielding load
predictions of non-fully monitored wind turbines, from which only standard data
remains available. In this paper, we propose a virtual load monitoring
framework formulated via Bayesian neural networks (BNNs) and we 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.
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