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MONITORING THE CONSUMED FATIGUE LIFE OF WINDTURBINES ON MONOPILE FOUNDATIONS
Wout WeijtjensVUB / OWI-lab
Alexandros IliopoulosVUB / OWI-lab
Jan HelsenVUB / OWI-lab
Christof DevriendtVUB / OWI-lab
Abstract :
Fatigue life is often a design driver for offshorewind turbines. A better understanding on the mech-anisms behind fatigue can improve design and thusreduce the initial cost of future substructures. Thiscontribution aims to improve the understanding offatigue life progression through monitoring. Mea-surements on an existing structure allow linking thefatigue life progression to different operational andenvironmental conditions, which can then be re-turned as a design input. In addition a continuouslymonitored fatigue life can serve an important role indeciding over wind farm inspections, repowering orlifetime extension.This paper introduces the OWI-lab fatigue monitor-ing strategy and uses data from three instrumentedturbines outside the Belgian coast to illustrate theimpact environmental and operational conditionshave on the consumption of fatigue life. The ulti-mate goal is to use the observed relations to builda toolbox which will allow to perform farm-widefatigue assessment.
1 Introduction
Fatigue life is a design driver for offshore windturbines and a key concern of wind farm operators.Most monitoring campaigns that are currentlyperformed are often related to fatigue life. A goodexample is scour monitoring. Yet the major concernis not the scour itself, but rather the effect scourhas on the turbine dynamics (e.g. the max bendingmoments) and ultimately the designed (fatigue)lifetime [1]. But even when scour is detected onestill needs to analyse to what extent the fatigue lifeis affected.An alternative approach is to directly monitorfatigue life consumption, e.g. by measuring theloads experienced by the turbine. This allows totrack the consumption over time of fatigue life, butalso allows to put actual results against designassumptions. In collaboration with designers andregulatory bodies this can result in improved designcodes in the near feature. The results from afatigue monitoring campaign can also serve asa support tool for cost-effective inspection and
Figure 1: Location of the Belwind and Northwindwind farms
end-of-life decision for the wind farm owner. Bybetter understanding how fatigue is progressingfor the different turbines within the farm, the owneris better aware of turbines that might run up totheir expected lifetime and turbine that can beconsidered for lifetime extension.This paper is part of an ongoing research collab-oration between the Vrije Universiteit Brussels,OWI-lab and Parkwind, the owner of the Belwindand Northwind offshore wind farms outside the Bel-gian coast, Fig. 1. The main goal of this researchcollaboration was to develop and field-test a broadmonitoring strategy for offshore wind turbines thatcan support wind farm owners and developers inthe near future. To develop a monitoring strategyfor offshore wind turbines on monopile foundationsone turbine at Belwind, a farm consisting of 55Vestas V90 turbines on monopiles, was equippedwith a multi physics sensor array in 2012. Thissensor array ranges from LVDT’s and force trans-ducers to monitor the condition of the groutedconnection, accelerometers for dynamic analysisand corrosion sensors in the monopile. At the morerecent Northwind wind farm, 72 Vestas V112 onmonopiles, two turbines were equipped with anoptimized setup. The three equipped turbines arecurrently continuously monitored.The data from these monitored turbines, in com-bination with state of the art data-processing, hasresulted in a large database of over 3 years of
(a) (b)
Figure 2: (a) Belwind V90 turbine (b) NorthwindV112 Turbine with indication of accelerometers (yel-low) and optical fiber strain gauges locations (white)
relevant dynamic properties and amongst othersan in-depth analysis of the damping properties ofoffshore turbines [2,3].
2 Methodology
In the following section the measurement setup forfatigue monitoring will be explained and the pro-posed monitoring strategy will be introduced.
2.1 Measurement setupIn early 2014 optical strain gauges, a.k.a. fiberBragg gratings [4], were added to the Belwindsetup. Two strain gauges at the middle tower leveland four sensors at the tower-Transition Piece (TP)interface connection, Fig. 2.(a). The array of foursensors at the tower-TP interface is equally dis-tributed over the entire circumference of the tower.This setup allows, after calibration, to determinethe instantaneous bending moment at the interfacelevel as well as the direction of the bending moment.
At Northwind two turbines are equipped with asimilar setup as at Belwind, Fig. 2.(b). Particularfor Northwind is the addition of nine optical straingauges at the TP-Monopile interface. The opticalstrain gauges are installed in three rosettes, whichare equally spaced over the circumference of theTP-wall. This setup allows to identify both the towerbending moment as well as the torsional moment atthe monopile interface level. Each rosette is accom-panied by an additional optical temperature sensorto compensate for temperature effects.
To assess the results it is necessary to have aninsight into the operational and environmental con-ditions at the site. To allow this a subset of the tur-bines’ SCADA is made available by the wind farmowner. In addition meteorological data is available
(a) (b)
Figure 3: Optical strain gauges as installed at (a)Belwind and (b) Northwind prior to applying the pro-tective sealing. Northwind features a rosette setupat the TP-MP interface which allows to determinetower bending as well as tower torsion. The fourthsensor in the rosette serves to compensate the tem-perature variation of the optical fiber strain gauge.
from a meteorological station on top of the BelwindOHVS and two wave radars installed at Belwind.
2.2 Monitoring the consumed fatiguelife
The concept of the proposed fatigue monitoringstrategy is summarized in Fig. 4. The key idea isto develop a strategy that requires a limited num-ber of instrumented turbines spread across the windfarm and to extrapolate the results from these tur-bines to the entire wind farm. An approach that alsohas been suggested in [5]. However, applying suchan extrapolation to monitor the foundation fatiguelife requires a proper understanding of the relationbetween operational and environmental conditionsand the consumption of fatigue life. In particular thedifferences in turbine dynamics and availability ofeach individual turbine will directly influence its fa-tigue life. A well known example is the potential in-teraction of waves with the turbines’ first resonancefrequency, which potentially reduces the turbine’suseful life. To avoid this interaction is often the mo-tivation for scour monitoring or scour protection.In this contribution we will focus on the cycle count-ing histograms, a.k.a. fatigue spectra, of thetower bending moments as a tool to understandthe mechanisms behind the consumption of fatiguelife. These cycle counts can in the future be trans-lated into damage indices and ultimately consumedlifetime using industry accepted methods such asMiner’s rule and the appropriate S/N curves. In ad-dition stress concentration factors need to be takeninto account to incorporate the effect of structuralfeatures such as welds and bolts.
2.3 Interpreting long term results
To assume a worst case scenario with respect tofatigue life we will count the cycles for the bending-
Figure 4: Overview of the fatigue monitoring strategy as proposed by OWI-lab. In this contribution we willfocus on how environmental and operational conditions will ultimately affect fatigue life.
Wind Speed (m/s)5 10 15 20 25
Tow
er B
endi
ng M
omen
t
Figure 5: Tower bending at the Tower-MP interfacevs. the wind speed at one of the Northwind turbines.
Table 1: Used case definitions for the V112
ParkedParked idlingOperating : Run-upOperating : Max. RPMOperating : Max. Power
moment equivalent stress, Fig. 5.All available ten-minute records of these equiva-
lent stresses are processed using rain flow count-ing. This returns a histogram that summarizes thenumber of cycles for a given amplitude. We caninterpret these results by putting them against therelevant S/N curve.However, to better understand the effect of differ-ent operational conditions the datasets are dividedinto different operational cases. The cycle countinghistograms can then be built for each case individu-ally. In the next section this analysis will be demon-strated.
2.4 Effect of operational conditionsThe case definitions used for the V112 in this contri-bution are shown in Table 1. Fig. 6.(a) shows a cy-cle counting histogram of one of the Northwind tur-bines. This plot shows the cycle counting histogramfor the most recurring operational cases. Obvi-ously, operational cases of power production occurfar more often and will ultimately contribute the mostto the consumed fatigue life. This can also be seenin Fig. 6.(a) in which the number of cycles duringoperation far exceed those associated with parked
conditions. However, to better understand the me-chanics driving fatigue these histograms can alsobe normalized w.r.t. the time spent in each opera-tional case. This allows to build a plot in which eachhistogram is the histogram of a turbine that wouldspend an entire year in each operational case. Sucha normalized result is shown in Fig. 6.(b) for the sec-ond Northwind turbine.
Fig. 6 reveals interesting behavior in the mecha-
Number of cycles
Str
ess
Ran
ge
(a) Non normalized Northwind D06
Number of cycles
Str
ess
Ran
ge
First mode
(b) Normalized Northwind H05
Figure 6: Cycle plot histograms, a.k.a fatigue spec-tra, for the most occurring operational cases. Col-ors correspond do the different operational casesas defined in Table 1. In red a S/N curve is addedas example. (a) The total contribution to damagestrongly depends on the time spent in each oper-ational case. (b) Normalizing the cycle histogramsw.r.t. the time spent in each operational case re-veals the different mechanics of fatigue for differentoperational cases.
nisms of fatigue. In both plots a lobe in all opera-tional cases is visible near the bottom of the graph.For the normalized graph, Fig. 6.(b), the (normal-ized) number of cycles is approximately equal for alloperational cases. The driver behind these lobes isthe first foundation mode of the turbine. As the firstmode has a relatively fixed frequency the number of
Time (s)0 100 200 300 400 500 600
Ben
ding
mom
ent
Time (s)50 60
Ben
ding
mom
ent
Time (s)90 100 110
Ben
ding
mom
ent
Time (s)0 100 200 300 400 500 600
Ben
ding
mom
ent
Figure 7: Example datasets of the instantaneousbending moment for both a (top) power producingand (bottom) a parked dataset. On the left a zoomon the time records reveal the oscillations related tothe first order mode.
cycles is determined by this frequency rather thanthe operational condition. Nonetheless, the stressrange of this lobe is larger during parked conditionsthan during power producing cases. In particular forthe Parked (•) case and Run-up (•) this differencebecomes apparent. As the mean wind speeds ofthese two operational cases during the consideredperiod are comparable, respectively 7.29±2.62 m/sand 7.34±1.36 m/s, the difference in stress rangecannot be attributed to different wind conditions. Apossible explanation can be found in [2]. The damp-ing of the first tower mode is far less during parkedconditions (<2%) in comparison to the dampingduring rotating conditions (>5%). Less dampingwill ultimately result in larger cycles under the same(wind) excitation.However, this does not imply that for these turbinesfatigue is driven by the first mode in parked condi-tions. Fig. 6.(b) also reveals that power producingcases have an additional lobe above the contribu-tion of the first mode. This lobe represent stressranges that exceed those during parked conditions,but represent far less cycles. Nonetheless in a dam-age assessment these lobes will contribute signifi-cantly to the fatigue life, as they are closer to theSN curve. These large low frequent cycles are at-tributed to low frequent variations in the thrust load-ing of turbine that occur during normal operation ofthe turbine. Interestingly, these variations would notpop-up in ten minute averages of the SCADA andthus motivate measuring the actual bending mo-ments.Fig. 7 shows the instantaneous bending momentsfor both an operational and a parked dataset. Thelarge low frequent variations in the bending momentare readily recognizable during power productionand do not appear in the parked dataset.
As expected from the cycle histograms both
datasets do show the oscillations linked to the firsttower mode. However, for parked conditions theseoscillations are, on average, larger in amplitude andare the dominant cause of cycles in the bending mo-ment.Up to now the analysis was limited to the effect ofoperational conditions on fatigue life. Another keyparameter that can affect fatigue are the environ-mental conditions at the site.
2.5 Effect of environmental conditionsand environmental events
To asses the effect of environmental conditions onthe fatigue life we can transform the cycle countsinto an instantaneous damage rate using Miner’srule and a representative S/N curve. The instanta-neous damage rates can then be analyzed againstthe available environmental parameters from theturbine SCADA or the wave radars.An interesting observation is made when putting theinstantaneous damage rate, during run-up, againstthe wind direction. Examples are shown in Fig. 8for both a turbine in the middle of the windfarm anda turbine at the outer edge of the farm. Fig. 8.(a,b)
Yaw Angle (°)0 90 180 270 360
Dam
age
rate
(a) Turbine in the middle of Belwind
Yaw Angle (°)0 90 180 270 360
Dam
age
rate
(b) Turbine at the edge of Northwind
Turbulence Intensity (%)5 10 15 20 25 30 35
Dam
age
rate
(c) Damage rate vs. turbulence intensity
Figure 8: (a-b) Plotting the damage rates againstthe wind direction reveals that for several wind direc-tions the damage rate has increased. (a) Wind di-rections in which a turbine is behind another turbineare marked with a dashed black line and show anincreased damage rate. (b) As this turbine is at theouter edge of the wind farm the damage rate dropsbeyond 220 deg. as the turbine receives clean, non-turbulent air. (c) Turbulence intensity correlates withthe increased damage rates
show that for given wind directions a distinctive in-crease in the instantaneous damage rate occurs.For the turbine in the center of the wind farm,Fig. 8.(a) each increased damage rate correspondsto a wind direction in which the turbine is in the tur-bulent air of another turbine in the farm. For theturbine at the edge of the park a similar observationcan be made. Beyond 220 deg. the turbine is facingoutside the farm and thus receives non-turbulent air.Exactly in these wind directions the damage rateshave significantly dropped.The observation that an increased damage rate canbe attributed to the turbulent air is key in this anal-ysis. Consequently any fatigue assessment modelshould properly include the turbulence intensity asa parameter. This also implies that the dominantwind direction at a wind farm is a driving parameterof fatigue life. In essence a turbine that is receiv-ing turbulent air in the dominant wind direction willfatigue faster than one that is not subjected to tur-bulent air. Note however, that at this point we areonly assessing fatigue rates and a definitive conclu-sion can only be made when a reliable assessmentof the remaining useful life is made.
3 Future work
The most important future work is to translate thecycle counting histograms into a reliable estimationof remaining useful life.
3.1 Reliable remaining life assessment
In order to obtain a reliable assessment of remain-ing life the analyst needs to take into account thelessons learned from this analysis. A remaininguseful life needs to take the different operationalconditions into consideration as well as their ex-pected occurrence in the future.In addition any calculation of the remaining use-ful life needs to consider the stochastic nature ofthe S/N curve and the predicted site conditionsand define uncertainty bounds accordingly. More-over, it will need to take into account the effectof (stochastic) short-term events, like wave-slams,boat-landings and (emergency) stops. For instancein Fig. 9 recent results, from our participation in theWiFi project [6], are shown for the BBC01 turbine inwhich two wave impacts occur. The effect of theseslamming waves on fatigue life needs to be inves-tigated and when necessary added to a fatigue as-sessment.
3.2 Virtual Sensing
All prior analysis assumes that the installed straingauges are attached to the fatigue hotspots, whichis not always feasible.For monopile foundation the fatigue hotspot is oftensituated below the sea-level at the mudline of the
Time
Str
ain
Wave impacts
Figure 9: Strains measured during two confirmedwave impacts. A reliable remaining lifetime assess-ment will need to take into account the occurrenceof these environmental events.
foundation. In practice this implies the installationof strain gauges on the monopile prior to the piledriving. It also becomes difficult to maintain thesesubmerged sensors throughout the operational lifeof the turbine.To resolve the limitations of direct measurementof hotspot stress, virtual sensing techniques arelooked into. Virtual sensing allows to use ac-celerometers installed in the tower to predict thestrains at any virtual sensor locations, e.g. the mud-line or at the node of a jacket foundation, usingthe turbine’s Finite Element Model. Accelerometersin the tower are far more reliable than submergedstrain gauges and are easier to maintain. More-over, the installation of the necessary accelerom-eters can be done after completion of the OWT.Currently, much research is invested [7, 8] to de-velop these techniques. But the step towards amethod that is applicable over the full operationalrange of the turbine still needs to be taken. Fu-ture work at OWI-lab will therefore validate thesetechniques using the long-term data that is availablefrom the ongoing monitoring campaign.
3.3 Farm-wide fatigue assessmentThe final step is linking consumed life time to the op-erational and environmental parameters using datamining techniques. This will allow to determine thepark-wide consumed life time with only a limitednumber of instrumented turbines, Fig. 4. However,such method will still require a good understandingof the mechanics of fatigue progression.For instance this paper already showed the pres-
ence of the first mode in the cycle counting. Differ-ent turbines will have different resonance frequen-cies and ultimately age differently. To perform afarm wide fatigue assessment a model that incor-porates the dynamic properties of each individualturbine will be necessary.
4 Conclusion
This paper introduced a fatigue monitoring strategyfor offshore foundation based on measurements atthree turbines in the Belgian North Sea. By combin-ing results from strain gauges and operational and
Figure 10: The ultimate goal is to develop a toolboxto perform a farm-wide fatigue assessment basedon measurements at a limited number of instru-mented turbines
environmental parameters, we have shed a light onthe different dynamics of fatigue life progression inoperational turbines.It showed that a reliable remaining useful life as-sessment will require to incorporate the different fa-tigue behavior under the different operational casesof the wind turbine. Moreover environmental events,like wave-impacts , or the influence of turbulent airneed to be taken into consideration.The current research continues and will aim todevelop a reliable farm-wide fatigue assessmentbased on a limited number of instrumented sensors.
5 *Acknowledgements
This research has been performed in the frame-work of the Offshore Wind Infrastructure Project(http://www.owi-lab.be) and the O&O Parkwind -project.The authors also acknowledge the financialsupport by the Fund for Scientific Research Flan-ders (FWO) and the Agency for innovation by Sci-ence and Technology (IWT) . The authors gratefullythank the people of Parkwind and our colleaguesin OWI-lab for their continuous support within thisproject.
References
[1] M. Zaaijer and J. Van der Tempel, “Scour pro-tection: a necessity or a waste of money,” in Pro-ceedings of the 43 IEA Topixal Expert Meeting,pp. 43–51, 2004.
[2] W. Weijtjens, R. Shirzadeh, G. De Sitter, andC. Devriendt, “Classifying resonant frequenciesand damping values of an offshore wind turbineon a monopile foundation,” in Proceedings ofEWEA, Barcelona, 2014.
[3] R. Shirzadeh, W. Weijtjens, G. De Sitter, andC. Devriendt, “An experimental and computa-tional investigation on the dynamics of an off-shore wind turbine in parked conditions,” in Pro-ceedings of EWEA, Barcelona, 2014.
[4] M. Kreuzer, “Strain measurement with fiberbragg grating sensors,” HBM, Darmstadt,S2338-1.0 e, 2006.
[5] H. Braam, T. Obdam, and T. Verbruggen, “Lowcost load monitoring for offshore wind farms,” inProceedings of ISMA, Leuven, pp. 4287–4300,2012.
[6] “Wifi : Wave impacts on fixed turbines.”
[7] A. Iliopoulos, W. Weijtjens, D. Van Hemelri-jck, and C. Devriendt, “Life time assessmentof offshore foundations using a virtual sensorapproach,” in Proceedings of EWEA Offshore,Copenhagen, 2015.
[8] P. Van der Male and E. Lourens, “Estimationof accumulated fatigue damage in lattice sup-port structures from operational vibrations,” inProceedings of EWEA Offshore, Copenhagen,2015.
