Fatigue damage calculation of offshore wind turbines' longterm data considering the low-frequency fatigue dynamics

The mutual and simultaneous action of external variable forces on (offshore) wind
support structures causes fatigue. Fatigue analysis in this context relies on 10-minute-long strain
signals. Cycle-counting allows capturing the fatigue cycles nested within these signals but
inevitably leaves some open loops called half-cycles or residuals. Some other loops are not even
cycle-counted because the algorithm cannot catch low-frequency (LF) cycles spanning more
than 10 minutes, as caused, e.g., by wind speed variations. Notwithstanding, LF cycles are also
the most damaging since they always contain the highest range in the variable amplitude signal.
Therefore, counting multiple 10-minute signals and merging the resulting histograms into one
inevitably has some non-conservative effects, for it leaves the LF cycles uncounted. In this work,
we avoid signal concatenation to recover the LF effect. To do so, we use the residuals sequence
from the 10-minute signals as it embeds the LF information. As method validation, we compare
the impact of LF fatigue dynamics recovery on the linearly accumulated damage using a reallife dataset measured at a Belgian offshore wind turbine. By defining a factor that incorporates
the LF effect, we observe that after 300 days of observation, the factor converges to a fixed value.