Framework for risk-based O&M planning fffh idtbi for

Framework for risk-based O&M planning f ff h i d t bi

J h D l d S

for offshore wind turbines

John Dalsgaard SørensenAalborg University, Denmark

• Introduction• ReliabilityReliability• Operation & Maintenance• Bayesian NetworksBayesian Networks• Examples • Summary-ConclusionsSummary Conclusions

1

Introduction

Goal: minimize the total expected life-cycle costs → minimize COE

Initial costs: dependent on reliability levelInitial costs: dependent on reliability levelO&M costs: dependent on O&M strategy,

availability and reliabilityy yFailure costs: dependent on reliability

2

IntroductionExperience: Risk-Based Inspection Planning forFatigue in Offshore installationsFatigue in Offshore installations

3

Reliability modeling of wind turbinesy g

Analysis of failure probabilities based on different types of information:

- Observed failure rates –Classical reliability theory

Mechanical / electricalcomponentsy y

- Probabilistic models for failure probabilitiesfailure probabilities –Structural Reliability Theory:Limit state modeling & St t l tLimit state modeling & FORM / SORM / simulation

Structural components

4

Reliability modeling of wind turbines

Failure Rates and Downtimes (examples)

5 Source: ISET: 2006

Reliability modeling of wind turbinesy g

Structural members– Structural failure modes in

• Tower, main frame, blades, foundation, , ,– Limit state equation for failure modes to be formulated– Parameters modeled by stochastic variablesy

– Reliability estimated using Structural Reliability Methods

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Reliability modelingy g

• Physical uncertainty Aleatory uncertaintyy y y y– Strength parameters: Yield strength of steel– Annual maximum wind speedp– Turbulence intensity

• Measurement uncertainty Epistemic uncertaintyMeasurement uncertainty Epistemic uncertainty– Wind measurement– Strain gaugeStrain gauge

• Statistical uncertainty Epistemic uncertainty– Limited number of dataLimited number of data

• Model uncertainty Epistemic uncertainty– Mathematical model as an approximation of failure mode

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– Mathematical model as an approximation of failure mode

Reliability - System aspectsy y p

• Series / parallel system?• Damage tolerant design• Robustness

Robustness (system reliability) can be increased by• increased redundancyincreased redundancy

– mechanical load sharing– statistical parallel system effectsstatistical parallel system effects

• increased ductility • protecting the wind turbine to (unforeseen) incidents andprotecting the wind turbine to (unforeseen) incidents and

defects• good quality control in all phases

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good quality control in all phases

Reliability level• Building codes: e.g. Eurocode EN1990:2002:

– annual PF = 10-6annual PF 10

• Fixed steel offshore structures: e.g. ISO 19902:2004– manned: annual PF ~ 3 10-5manned: annual PF 3 10– unmanned: annual PF ~ 5 10-4

• IEC 61400-1+3: wind turbines– annual PF ~ 10-4 - 10-3

• Observation of failure rates for wind turbines– Failure of blades: approx. 10-4 - 10-3 per yearFailure of blades: approx. 10 10 per year – Wind turbine collapse: approx. 10-5 - 10-4 per year

9

Operation & Maintenancep

How can risk-based methods be used to optimal planning of

• future inspections / monitoring (time / type)• decisions on maintenance/repair on basis of (unknown)decisions on maintenance/repair on basis of (unknown)

observations from future inspections / monitoring

taking into account uncertainty and costs?

10

Operation & Maintenancep

• High costs for operation and maintenance for offshore wind farmsHi h f il ?– Higher failure rates?

– Access: boat, helicopter, …– Weather windowsWeather windows– Loss of production– Mobilization

• Deterioration processes are always present • High uncertainty

→ Maintenance could optimally be planned by using risk-based methods

11

Operation & Maintenance

• Corrosion

p

• Corrosion

• Erosion

i• Fatigue

• Wear

• Etc.

Deterioration – damage accumulation:• Deterioration processes are connected with significant uncertainty• Observations of the actual deterioration / condition by monitoring or

inspections can be introduced in the models and significantly improve the precision of forecasts

12

p


• Corrective (unplanned):

p

– exchange / repair of failed components

• Preventive (planned):– Timetabled: inspections / service after predefined scheme – Conditioned: monitor condition of system and decide next on

inspection based on degree of deterioration→ based on pre-posterior Bayesian decision model

13


Theoretical basis – life-cycle approach: Bayesian decision theory – pre-posterior formulation

Optimal decision: Minimum total expected costs in lifetime

14

Optimal decision: Minimum total expected costs in lifetime


Mi i li bilit ( d th iti )Minimum reliability (codes, authorities ...), r

isk

Maintenance & repair costOptimal

strategy

Cos

t,

Expected failure cost

Increasing maintenance efforts

Maintenance effort

15

Decreasing risk (expected failure cost)


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iT

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Expected inspection costs: iT

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iiFiININ r

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Expected repair costs:

E pected fail re costs:

ii T

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16

Expected failure costs: tFATCOLt

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Failure / error types:• Gearbox• Generator• Rotor blades• Rotor blades• Blade pitch mechanism• Yaw mechanism• Main shaft• …• Tower / support structure (jacket): cracks, corrosion, …

17


Time scale for decisions:• Short: minutes

– Operation: ex: Stop wind turbines if price too low - Include uncertainty on wind forecasts and price development

• Medium: days– Maintenance: ex: Start maintenance / repair operation on

offshore wind turbine – Include uncertainty on weather windowswindows

• Long: months / yearsP ti i t– Preventive maintenance:

• Inspection- and monitoring planningG b t f ti k

18

– Gearboxes, generators, fatigue cracks, …


Information collection:• Condition Monitoring System (CMS)• SCADA data

• Inspections (direct information on defect / damage rate)p ( g )– Example: measurement of crack size in fatigue

• Indicators (indirect information on defect / damage rate)– Example: gearbox metallic particle monitoringExample: gearbox metallic particle monitoring

19


20

Example – gearbox

Examples of inspection methods and inspection results:• Visual inspection though inspection covers → indication of extent of wear• Oil analysis (time interval) → sample taken indicating extent of wear

M ( i i l) i l k i di i f• Magnet (time interval) → representative sample taken indicating extent of wear material

• Investigation of oil filters (time interval) → representative sample is taken g ( ) p pindicating extent of wear material

• Particle counting (online) → continuously representative samples are taken indicating extent of wear materialindicating extent of wear material

• Condition monitoring (continuously) → vibration response is monitored and used to indicate mechanical changes

→ Indirect information (indicators)

21

Example – gearbox

I ti Ob ti ( ) D i i i t b d b tiInspection Observation(s) Decision on maintenance based on observation plan - indicators ex: repair now / wait to next inspection

on damage

22


Application of Bayesian Networks

FC1 FC2

MU MU1 MU2

FC1 FC2

F1 F2

A1 A2

D0 D1

Ins

D2

Ins

1 2

Ins1

R1

Ins2

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RC1 RC2

23

Summary - Conclusions

• Risk-based methods can be used to optimal planning of f i i / i i ( i / )

y

– future inspections / monitoring (time / type)– decisions on maintenance/repair on basis of (unknown) observations

from future inspections / monitoringtaking into account uncertainty and costs

• Risk based operation & maintenance• Risk-based operation & maintenance– theoretical basis: pre-posterior decision theory

• Optimal decisions: maximize total expected benefits-costs

• Examples:Examples: – Inspection planning for fatigue cracks, corrosion,…– O&M for gearbox exposed to deterioration

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

Thank You For Your AttentionThank You For Your Attention

John Dalsgaard SørensenProfessorAalborg University, [email protected]

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Framework for risk-based O&M planning fffh idtbi for