Abstract
Fatigue has become a major consideration factor in modern
offshore wind farms as optimized design codes and a lack of lifetime
reserve have made continuous fatigue life monitoring become an
operational concern. In this contribution we discuss a data-driven
methodology for farm-wide tower-transition piece fatigue load
estimation. We specifically tackle the employment of this methodology in
a real-world farm-wide setting and the implications of continuous
monitoring. With reliable nacelle-installed accelerometer data at all
locations, along with the customary ten-minute SCADA statistics and
three strain gauge-instrumented ’fleet-leaders’ we discuss the value of
two distinct approaches: use of fleet-leader or population-based data
for training a physics-guided neural network model with a built-in
conservative bias, with the latter taking precedence. In the context of
continuous monitoring, we touch on the importance of data imputation,
working under the assumption that if data is missing, then its fatigue
loads should be modelled as under idling. With this knowledge at hand,
we analyzed the errors of the trained model over a period of nine
months, with monthly accumulated errors always kept below ±5%
. A particular focus was given to performance under high loads,
where higher errors were found. The cause for this error was identified
as being inherent to the use of ten-minute statistics, but mitigation
strategies have been identified. Finally, the farm-wide results are
presented on fatigue load estimation, which allowed to identify
outliers, whose behaviour we correlated with the operational conditions.
Finally, the continuous data-driven, population-based approach here
presented can serve as a springboard for further lifetime-based
decision-making.
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