Framework for risk-based O&M planning for offshore wind dturbines

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1 Framwork for risk-bas O&M planning for offshor win turbins John Dalsgaar Sørnsn Aalborg Univrsity Dnmark Introuction Rliability Opration & Maintnanc Baysian Ntworks Exampls Summary-Conclusions 1

2 Introuction Goal: minimi th total xpct lif-cycl costs minimi COE Initial costs: pnnt on rliability lvl O&M costs: pnnt on O&M stratgy availability an rliability Failur costs: pnnt on rliability 2

3 Introuction Exprinc: Risk-Bas Inspction Planning for Fatigu in Offshor installations 3

4 Rliability moling of win turbins Analysis of failur probabilitis bas on iffrnt typs of information: - Obsrv failur rats Classical rliability thory Mchanical / lctrical componnts - Probabilistic mols for failur probabilitis Structural Rliability Thory: Limit stat moling & FORM / SORM / simulation St t l t Structural componnts 4

5 Rliability moling of win turbins Failur Rats an Downtims xampls 5 Sourc: ISET: 2006

6 Rliability moling of win turbins Structural mmbrs Structural failur mos in Towr main fram blas founation Limit stat quation for failur mos to b formulat Paramtrs mol by stochastic variabls Rliability stimat using Structural Rliability Mthos 6

7 Rliability moling Physical uncrtainty Alatory uncrtainty Strngth paramtrs: Yil strngth of stl Annual maximum win sp Turbulnc intnsity Masurmnt uncrtainty Epistmic uncrtainty Win masurmnt Strain gaug Statistical uncrtainty Epistmic uncrtainty Limit numbr of ata Mol uncrtainty Epistmic uncrtainty Mathmatical mol as an approximation of failur mo 7

8 Rliability -Systm aspcts Sris / paralll systm? Damag tolrant sign Robustnss Robustnss systm rliability can b incras by incras runancy mchanical loa sharing statistical paralll systm ffcts incras uctility protcting th win turbin to unforsn incints an fcts goo quality control in all phass 8

9 Rliability lvl Builing cos:.g. Euroco EN1990:2002: annual P = -6 F 10 Fix stl offshor structurs:.g. ISO 19902:2004 mann: annual P ~ F unmann: annual P F ~ IEC : win turbins annual P F ~ Obsrvation of failur rats for win turbins Failur of blas: approx pr yar Win turbin collaps: approx pr yar 9

10 Opration & Maintnanc How can risk-bas mthos b us to optimal planning of futur inspctions / monitoring tim / typ cisions on maintnanc/rpair on basis of unknown obsrvations from futur inspctions / monitoring taking into account uncrtainty an costs? 10

11 Opration & Maintnanc High costs for opration an maintnanc for offshor win farms Highr failur rats? Accss: boat hlicoptr Wathr winows Loss of prouction Mobiliation Dtrioration procsss ar always prsnt High uncrtainty Maintnanc coul optimally b plann by using risk-bas mthos 11

12 Opration & Maintnanc Corrosion Erosion Fatigu War Etc. Dtrioration amag accumulation: Dtrioration procsss ar connct with significant uncrtainty Obsrvations of th actual trioration / conition by monitoring or inspctions can b introuc in th mols an significantly improv th prcision of forcasts 12

13 Opration & Maintnanc Corrctiv unplann: xchang / rpair of fail componnts Prvntiv plann: Timtabl: inspctions / srvic aftr prfin schm Conition: monitor conition of systm an ci nxt on inspction bas on gr of trioration bas on pr-postrior Baysian cision mol 13

14 Opration & Maintnanc Thortical basis lif-cycl approach: Baysian cision thory pr-postrior formulation Optimal cision: Minimum total xpct costs in liftim 14

15 Opration & Maintnanc Minimum i rliability cos authoritis... Cost risk Optimal stratgy Maintnanc & rpair cost Expct failur cost Incrasing maintnanc fforts Dcrasing risk xpct failur cost Maintnanc ffort 15

16 Opration & Maintnanc u i i F REP IN N i C C C C B 1... s.t. W max l i I Expct bnfits: L F t T t P t P max i T N i i F i r T P B B Expct inspction costs: i T N i i F i IN IN r T P C C Expct rpair costs: E pct fail r costs: i T i N i R i R REP r P C C T P P t C C L 1 16 Expct failur costs: t FAT COL t t F F F r P P t C C 1 1

17 Opration & Maintnanc Failur / rror typs: Garbox Gnrator Rotor blas Bla pitch mchanism Yaw mchanism Main shaft Towr / support structur jackt: cracks corrosion 17

18 Opration & Maintnanc Tim scal for cisions: Short: minuts Opration: x: Stop win turbins if pric too low - Inclu uncrtainty on win forcasts an pric vlopmnt Mium: ays Maintnanc: x: Start maintnanc / rpair opration on offshor win turbin Inclu uncrtainty on wathr winows Long: months / yars Prvntiv maintnanc: Inspction- an monitoring planning Garboxs gnrators fatigu cracks 18

19 Opration & Maintnanc Information collction: Conition Monitoring Systm CMS SCADA ata Inspctions irct information on fct / amag rat Exampl: masurmnt of crack si in fatigu Inicators inirct information on fct / amag rat Exampl: garbox mtallic particl monitoring 19

20 20 Opration & Maintnanc

21 Exampl garbox Exampls of inspction mthos an inspction rsults: Visual inspction though inspction covrs inication of xtnt of war Oil analysis tim intrval sampl takn inicating xtnt of war Magnt tim intrval rprsntativ sampl takn inicating xtnt of war matrial Invstigation of oil filtrs tim intrval rprsntativ sampl is takn inicating xtnt of war matrial Particl counting onlin continuously rprsntativ sampls ar takn inicating xtnt of war matrial Conition monitoring continuously vibration rspons is monitor an us to inicat mchanical changs Inirct information inicators 21

22 Exampl garbox Inspction Obsrvations Dcision i on maintnanc bas on obsrvation plan - inicators x: rpair now / wait to nxt inspction on amag 22

23 Opration & Maintnanc Application of Baysian Ntworks M U M U1 M U2 F C1 F C2 F 1 F 2 A 1 A 2 D 0 D 1 D 2 Ins 1 Ins 2 R 1 R 2 R C1 R C2 23

24 Summary - Conclusions Risk-bas mthos can b us to optimal planning of futur inspctions / monitoring tim / typ cisions on maintnanc/rpair on basis of unknown obsrvations from futur inspctions / monitoring taking into account uncrtainty an costs Risk-bas opration & maintnanc thortical basis: pr-postrior cision thory Optimal cisions: maximi total xpct bnfits-costs Exampls: Inspction planning for fatigu cracks corrosion O&M for garbox xpos to trioration 24

25 Framwork for risk-bas O&M planning for offshor win turbins Thank You For Your Attntion John Dalsgaar Sørnsn Profssor Aalborg Univrsity Dnmark 25

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