## 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 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.

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.

Original language | English |
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Number of pages | 11 |

Publication status | Published - 2 Jun 2022 |

Event | TORQUE 2022: The Science of Making Torque from Wind (TORQUE 2022) - TU Delft, Delft, Netherlands Duration: 1 Jun 2022 → 3 Jun 2022 |

### Conference

Conference | TORQUE 2022 |
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Country/Territory | Netherlands |

City | Delft |

Period | 1/06/22 → 3/06/22 |