Risk-based Operation and Maintenance of Offshore Wind Turbines Sørensen, John Dalsgaard

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1 Aalborg Unverstet Rsk-based Operaton and Mantenance of Offshore Wnd Turbnes Sørensen, John Dalsgaard Publshed n: Proceedngs IFIP WG7.5 Conference on Relablty and Optmzaton of Structural Systems Publcaton date: 2009 Document Verson Publsher's PDF, also known as Verson of record Lnk to publcaton from Aalborg Unversty Ctaton for publshed verson (APA): Sørensen, J. D. (2009). Rsk-based Operaton and Mantenance of Offshore Wnd Turbnes. In Proceedngs IFIP WG7.5 Conference on Relablty and Optmzaton of Structural Systems The IFIP Internatonal Conference on Research and Practcal Issues of Enterprse Informaton Systems. General rghts Copyrght and moral rghts for the publcatons made accessble n the publc portal are retaned by the authors and/or other copyrght owners and t s a condton of accessng publcatons that users recognse and abde by the legal requrements assocated wth these rghts.? Users may download and prnt one copy of any publcaton from the publc portal for the purpose of prvate study or research.? You may not further dstrbute the materal or use t for any proft-makng actvty or commercal gan? You may freely dstrbute the URL dentfyng the publcaton n the publc portal? Take down polcy If you beleve that ths document breaches copyrght please contact us at vbn@aub.aau.dk provdng detals, and we wll remove access to the work mmedately and nvestgate your clam. Downloaded from vbn.aau.dk on: aprl 28, 208

2 WG7.5 RELIABILITY AND OPTIMIZATION OF STRUCTURAL SYSTEMS Toluca, Mexco, August RISK-BASED OPERATION AND MAINTENANCE OF OFFSHORE WIND TURBINES John Dalsgaard Sørensen Aalborg Unversty & Rsø DTU, Denmark ABSTRACT For offshore wnd turbnes costs to operaton and mantenance are substantal. Ths paper descrbes a rsk-based lfecycle approach for optmal plannng of operaton and mantenance. The approach s based on pre-posteror Bayesan decson theory. Deteroraton mechansms such as fatgu corroson, wear and eroson are assocated wth sgnfcant uncertanty. Observatons of the degree of damage can ncrease the relablty of predctons, especally n connecton wth condton-based mantenance. The approach can be used for gearboxes, generators, cracks, corroson, etc. The paper also descrbes how probablstc ndcators can be used to quantfy ndrect nformaton about the damage state for crtcal components, e.g. gear-boxes. Keywords: wnd turbnes, operaton & mantenanc rsk. INTRODUCTION For offshore wnd turbnes costs to operaton and mantenance are substantal, and can be expected to ncrease when wnd farms are placed at deeper water depths and n more harsh envronments. Ths paper descrbes a rsk-based lfecycle approach for optmal plannng and desgn of offshore wnd turbnes. For ol & gas offshore nstallatons costeffectve procedures for rsk-based nspecton plannng have been developed durng the last 0-5 years and are used at several locatons world wd see e.g. (Moan 2005), (Faber et al. 2000) and (Sørensen et al. 200). These procedures are based on pre-posteror Bayesan decson theory. Ths paper descrbes how these procedures can be appled for offshore wnd farms, especally: how rsk-based methods can be used to optmal plannng of future nspectons / montorng / servce (tme / type) decsons on mantenance/repar on bass of (unknown) observatons from future nspectons / montorng takng nto account uncertanty and costs. Deteroraton mechansms such as fatgu corroson, wear and eroson are assocated wth sgnfcant uncertanty. Observatons of the degree of damage can ncrease the relablty of predctons. Mantenance actvtes can be dvded n correctve and preventve (tmetabled or condtone mantenance. Condton-based mantenance usng observatons from e.g. condton montorng should optmally be based on pre-posteror Bayesan decson theory. Ths approach can be used for operaton and mantenance plannng related to falure & error types such as: Gearbox, Generator, Rotor blades, Blade ptch mechansm, Yaw mechansm, Man shaft, Tower / support structure (jacket): cracks, corroson, Another mportant aspect for decsons related operaton and mantenance s the tme scale whch can be short (mnutes), medum (days) for e.g. decdng f an offshore mantenance / repar acton should be ntated dependng on the weather forecast or long (months / years) for e.g. preventve mantenance and nspecton / montorng plannng for gear boxes. An mportant aspect s collecton and probablstc modellng of nformaton. Informaton can come from Condton Montorng System (CMS), drect nspectons or ndrect ndcators. Indcators contan ndrect nformaton on falure rates and statstcal models can be formulated and updated based on Bayesan statstcs. The use of ndcators s llustrated for typcal wnd turbne components.

3 2. OPTIMAL PLANNING OF MAINTENENCE Fgure 2. shows a decson tree related to the lfe cycle of an engneerng structure such as a wnd turbne or wnd farm. The decsons taken by the decson maker (desgner / owner / ) and observatons of uncertan parameters (unknown at the tme of the decson) are: At the desgn stage a decson on the optmal desgn parameters z = ( z,..., z N ) s made whch n prncple should maxmze the total expected benefts mnus costs durng the whole lfetme such that safety requrements are fulflled at any tme. In practce requrements from standards and actual costs of materals are used to determne the optmal desgn. Durng the lfetme contnuous montorng of the wnd turbnes and nspectons of crtcal components / detals are performed. These are ndcated n the box repeated nspecton/mantenance n fgure 2.. Each box conssts of: o a decson on tmes and types of nspecton / montorng for the rest of the lfetme o observatons from nspecton / montorng o decson on eventual mantenance / repar based on the nspecton / montorng results Realsaton of uncertan parameters such as wnd and wave clmat strengths, degradaton, model uncertantes wll take place durng the lfetme. It s noted that these uncertantes can be dvded n aleatory and epstemc uncertantes. Aleatory uncertanty s nherent varaton assocated wth the physcal system or the envronment t can be characterzed as rreducble uncertanty or random uncertanty. Epstemc uncertanty s uncertanty due to lack of knowledge of the system or the envronment t can be characterzed as subjectve uncertanty, reducble uncertanty. The total cost s the sum of all costs n the remanng part of the lfetme after the decson tme. Fgure 2. Decson tree for optmal mantenance plannng. The approach can be used for operaton and mantenance plannng related to dfferent falure & error types n Gearbox, Generator, Rotor blades, Blade ptch mechansm, Yaw mechansm, Man shaft, Tower / support structure (fatgue cracks, corroson), Further, decsons related to operaton and mantenance are related to dfferent tme scales: short (mnutes) for decson related to e.g. parkng the wnd turbn medum (days) for e.g. decsons on when to start offshore mantenance / repar actons dependng on e.g. weather forecasts, or long (months / years) for e.g. preventve mantenance and nspecton / montorng plannng for gear boxes. An mportant step n rsk-based nspecton & mantenance plannng s collecton of data / nformaton and probablstc modellng of ths nformaton. Informaton can come from Condton Montorng Systems (CMS), nspectons or ndcators. Indcators that contan ndrect nformaton on e.g. falure rates can be formulated and updated based on Bayesan statstcs, see (Faber & Sørensen 2002). 2

4 Fgure 2.2 Damage model wth nspecton / repar at tme T. Deteroraton mechansms such as fatgu corroson, wear and eroson are assocated wth sgnfcant uncertanty. Observatons of the degree of damage D(t) can ncrease the relablty of predctons usng Bayesan statstcal technques as llustrated n fgure 2.2. Generally an nspecton at tme T and assocated mantenance/repar wll decrease the uncertanty and the expected mean damage level at tme T 2 wll be smaller snce most realzatons wth large damage level at tme T can be expected to be mantaned / repared. The performance of wnd turbnes s subject to a number of uncertantes. These nclude operatonal condtons, materal characterstcs and envronmental exposure. The uncertantes are due to nherent physcal randomness and uncertantes assocated wth the models used to assess the performance of the systems. If, furthermor the statstcal bass for the assessment of the uncertantes s lmted then also statstcal uncertantes may be mportant. When nspecton / mantenance plannng for wnd turbnes s consdered, t s mportant to take all these uncertantes nto consderaton, as they wll strongly nfluence the future performance of the systems. It s also mportant to realze that the degree of control of the engneerng systems acheved by the nspectons s strongly nfluenced by the relablty of the nspectons,.e. ther ablty to detect and sze degradaton. The relablty of nspectons themselves may be subject to sgnfcant uncertanty and ths must be taken nto account n the plannng of nspectons. The decson problem of dentfyng the cost optmal nspecton plan may be solved wthn the framework of pre-posteror analyss from the classcal decson theory see e.g. (Raffa and Schlafer 96) and (Benjamn and Cornell 970). Here a short summary s gven, see e.g. (Sørensen et al. 99), (Madsen & Sørensen 9900), (Faber et al. 2000) and (Sørensen et al. 200). A smlar formulaton has wth large success been appled for plannng of nspecton and repar of offshore ol & gas nstallatons. In the general case the parameters defnng a nspecton plan are the possble repar / mantenance actons whch are modeled by the decson rule d, the number of nspectons N n the servce lfe T L, the tme ntervals between nspectons and possble repar/mantenance t = ( t, t 2,..., t N ) and the nspecton qualtes q = ( q,,..., q2 qn ). These nspecton/mantenance parameters are wrtten as e = ( N, t, q). The outcome of nspectons (typcally a damage level, e.g. a crack sz the extent of corroson or wear) s modeled by a random varable S snce t s unknown at the tme of decson makng. A decson rule d (S) s then appled to the outcome of the nspecton to decde whether or not repar / mantenance should be performed. The dfferent uncertan parameters (stochastc varables) modelng the state of nature such as load varables and materal characterstcs are collected n X = X, X,..., X ). ( 2 n 3

5 If the total expected costs are dvded nto fabrcaton, nspecton, repar, mantenanc strengthenng and max falure costs and a constrant related to a maxmum annual (or accumulate falure probablty Δ P F s added then the optmzaton problem can be wrtten max z, d s.t. W( z, = B( z, C ( z, C l z z z t u ΔP ( t, z, ΔP max F I IN, =,..., N, t =,2,..., T ( z, C L REP ( z, C ( z, F (2.) W ( z, e, s the total expected benefts mnus costs n the servce lfe tme T L, B s the expected benefts, C I s the ntal costs, C IN s the expected nspecton costs, C REP s the expected costs of repar and C F s the expected falure costs. The annual probablty of falure n year t s Δ P F, t. The N nspectons are assumed performed at tmes 0 T T2... T N TL. The total captalzed benefts are wrtten N B( z, = B ( PF ( T )) (2.2) T = ( + r) where the th term represents the captalzed benefts n year gven that falure has not occurred earler, B s the benefts n year, P F ( T ) s the probablty of falure n the tme nterval [ 0, T ] and r s the real rate of nterest. The total captalzed expected nspecton costs are C IN N ( e, = CIN, ( q) ( PF ( T )) (2.3) T = ( + r) where the th term represents the captalzed nspecton costs at the th nspecton when falure has not occurred earler, C IN, ( q ) s the nspecton cost of the th nspecton. The total captalzed expected repar costs are C REP N ( e, = CR, PR T = ( + r) (2.4) where C R, s the cost of a repar at the th nspecton and th nspecton when falure has not occurred earler. The total captalzed expected costs due to falure are estmated from P R s the probablty that a repar s performed after the TL CF ( e, = CF ( t) ΔPF, t P (2.5) COL FAT t t= ( + r) where C F (t) s the cost of falure at the tme t. P s the condtonal probablty of collapse of the wnd turbne COL FAT gven fatgue falure of the consdered component and models the mportance / consequence of fatgue falure. The probabltes of falure at year t and the probablty of repar can be determned as descrbed n e.g. Madsen et al. (990). The above model s n prncple related to a sngle wnd turbne and a sngle component. For wnd turbne placed n a wnd farm wth many crtcal components the same basc formulaton can be used, but the ntal costs, nspecton, repar and falure costs should be formulated as a basc cost plus margnal costs for each extra wnd turbne n the park. 4

6 Fgure 2.3 Bath-tub model for lfetme falure rate. For many components also wnd turbne components - subject to degradaton / damage accumulaton the model n fgure 2.3 can be used to llustrate the development of the falure rate durng the lfetme. Intally a hgh falure rate can be expected due to fabrcaton / burn-n defects. Next, a perod wth a normal constant falure / defect rate wll take place. Correctve mantenance s performed n ths perod. At the end of the lfetme of the component the falure / defect rate can be expected to ncrease. If the falure rate ncreases strongly (tme to falure s known ) then preventve mantenance should be performed. If the falure rate s moderately ncreasng deteroraton / damage can be observed before falur and condton control / condton & rsk based mantenance should be performed and planned usng the prncples descrbed above for rsk-based nspecton & mantenance/repar plannng. The rsk-based methods descrbed abobe can thus be used to optmal plannng of decsons on future nspectons / condton montorng (tme and type), and mantenance / repar actons based on (unknown) observatons from future nspectons / montorng takng nto account uncertanty and costs. The next secton llustrates how ths can be used for typcal components n wnd turbnes. 3. EXAMPLES Fgure 3. Repeated nspecton/mantenance. 5

7 Table 3. Indcators for nspecton of gear-boxes. Inspecton type Descrpton Vsual nspecton Vsual though nspecton covers: ndcaton of extent of wear nspecton Ol analyss wth tme ntervals a sample s taken ndcatng extent of wear Magnet wth tme ntervals a representatve sample s taken ndcatng extent of wear materal Investgaton of ol flters wth tme ntervals a representatve sample s taken ndcatng extent of wear materal Partcle countng onlne contnuously representatve samples are taken ndcatng extent of wear materal Condton montorng contnuously the vbraton response s montored and used to ndcate mechancal changes The models descrbed n secton 2 can e.g. be used for gearboxes n wnd turbnes when plannng nspecton and mantenance / repar. Only the process of plannng repeated nspecton / servce and mantenance / repar s consdered, see fgure 3.. Examples of nspecton methods and nspecton results for gearboxes are shown n table 3.. All the assessment methods shown gve ndrect nformaton on the damage/deteroraton state of the gearbox, snce the damage/deteroraton state s not measured drectly. The ndcators wll have dfferent relabltes wth respect to nformaton about the real damage state and they wll have dfferent costs. These aspects can be modelled usng the rsk-based nspecton & mantenance plannng approach descrbed n secton 2. In order to use the preposteror Bayesan decson models optmal, rsk-based nspecton & mantenance plannng t s further necessary to formulate decson rules for mantenance / repar actons gven nspecton/montorng results,.e. t s beforehand decded whch future mantenance / repar to perform when future nspecton results become avalable. The models to be establshed are: A mantenance model for the decson parameters descrbng the future mantenanc servce and nspecton plan and methods, see below A determnstc model for damage / deteroraton accumulaton as functon of tme: D (t), see below A stochastc model for uncertan parameters n the damage accumulaton model such that a probablstc model for the damage accumulaton can be obtaned,.e. the probablty of certan damage levels can be calculated A stochastc model for the uncertanty / relablty of each nspecton type A decson model d (S) for repar / mantenance (acton) gven future result of nspecton / condton montorng. The decson model can also nclude decsons dependng on uncertan weather forecast A model for costs related to nspectons, mantenanc repar and possble falure (ncludng loss of ncome), see below It s noted that nformaton from contnuous qualty control and montorng systems can be used to establsh the stochastc models. The model can easly be extended wth nformaton from other nspecton methods, and nspecton / montorng from other (correlate components (gear-boxes). A typcal model for damage accumulaton for varous mechancal/electrcal components can be wrtten: ( Δ ) D ( t) = ΔD (3.) t where the damage accumulaton ncrements over tme ntervals Δ t depend on the operatonal mod e.g. Δt d2 f V = 0 (standstll) ΔD ( Δt) = (3.2) Δt( d0 + d V ) f V > 0 6

8 V s the mean wnd speed and d 0, d, d2 are constants modellng the damage accumulaton. The model n Eqn. 3.2 mples that the damage accumulaton depends on the operatonal mode (standstll or producton of electrcty) and also on the mean wnd speed. The constants d 0, d, d2 can be modelled by stochastc varables whch are updated contnuously when new nformaton / measurements of actual damages become avalable. For varous mechancal/electrcal components a typcal mantenance/servce strategy s as follows: Tme-tabled mantenance: servce s performed wth regular tme ntervals of 6 (or 2) months where the components are nspected / mantaned and n many cases exchanged. Servce s only made when access to the wnd turbnes can be made by cheap boat transport Correctve mantenance: faled components (mplyng stop of the wnd turbne) are repared as soon as possbl.e. by boat f low mean wnd speed (low cost) or by helcopter f hgh mean wnd speed (hgh cost) Ths mantenance strategy can be extended by usng rsk- and condton-based mantenance plannng: At repeated tme ntervals (e.g. month 3, 6, 9, 2, ) a decson s made where the alternatves are: o servce now, or after 3, 6, 9, 2 months usng nformaton from e.g. measured mean wnd speed to estmate the damage accumulaton. The optmal decson s the one whch mnmze the total expected costs n the rest of the lfetme of the wnd turbne. The decsons can be extended to nclude methods for repar/mantenance. The cost model should nclude: Cost of nspecton / servce Cost of repar / mantenance Transport: (weather dependent) Boat / helcopter Watng tme Spare parts etc. Loss of revenue n case of falure Dscountng rate r 4. CONCLUSION A rsk-based lfe-cycle approach for optmal plannng of operaton and mantenance s descrbed. The approach s theoretcally based on pre-posteror Bayesan decson theory and can be used when deteroraton mechansms such as fatgu corroson, wear and eroson are present and can be observed by nspecton and/or montorng before falure of the component consdered. The rsk based approach can ratonally take nto account the uncertanty related to the deteroraton and the future costs related to nspecton/montorng, mantenanc repar and falure (loss of ncome). Observatons of the degree of deteroraton damage can ncrease the relablty of predctons, especally n connecton wth condton-based mantenance usng Bayesan updatng. The approach can be used for gearboxes, generators, cracks, corroson, etc. Further, t s descrbed how probablstc ndcators can be used to quantfy ndrect nformaton about the damage state for crtcal components. The approach s llustrated for applcaton to gear-boxes n stuatons where deteroraton can be observed before falure. 5. ACKNOWLEDGEMENTS The work presented n ths paper s part of the projects Probablstc desgn of wnd turbnes, grant no and Bottlenecks, grant no supported by the Dansh Research Agency. The fnancal support s greatly apprecated. 6. REFERENCES Moan, T., Relablty-based management of nspecton, mantenance and repar of offshore structures. Structure and Infrastructure Engneerng, (), pp

9 Faber, M.H., Engelund, S., Sørensen, J.D. and A. Bloch, Smplfed and generc rsk based nspecton plannng. Proc. OMAE 2000, S&R paper 643. Sørensen, J.D. & M.H. Faber, 200. Generc nspecton plannng for steel structures, Proc. ICOSSAR 0, USA. Faber, M.H. & J.D. Sørensen, Indcators for nspecton and mantenance plannng of concrete structures. Structural Safety, 24, pp Raffa, H. & Schlafer, R., 96. Appled Statstcal Decson Theory. Harward Unversty Press, Cambrdge Unversty Press, Mass. Benjamn, J.R. and Cornell, C.A., 970. Probablty, statstcs and decson for cvl engneers, McGraw-Hll, NY. Sørensen, J.D., Faber, M.H., Rackwtz, R. and Thoft-Chrstensen, P.T., 99. Modelng n optmal nspecton and repar, Proc. OMAE9, Stavanger, Norway, pp Madsen, H.O. and J.D. Sørensen 990. Probablty-Based Optmzaton of Fatgue Desgn Inspecton and Mantenance. Proc. Int. Symp. on Offshore Structures, Glasgow. 8