Fatigue damage calculation of offshore wind turbines’ long-term data considering the low-frequency fatigue dynamics

Abstract

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 real-life 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.