Copyright notice General presentation Siemens Wind Power Reliability Assessment and Improvement through ARM Modeling Poul Skjærbæk, Thomas Mousten, Henrik Stiesdal, September 2009

Page 2 Date Author Copyright © Siemens AG 2009 Energy Sector Offshore Challenges Lead to Questions Offshore conditions when correcting defects are worse… Equipment size, availability, cost and mobilization time Magnitude of weather impact Efficiency of man-hours … which leads to obvious questions: As an owner, what will be my lifecycle costs? As a financer or insurer, what are my risks? As a manufacturer, what will I spend during the warranty period?

Page 3 Date Author Copyright © Siemens AG 2009 Energy Sector An ARM Model Can Provide Some of the Answers An ARM model is a framework for a quantified analysis of failure probabilities and consequences Availability Reliability Maintainability The failure probabilities are described by Weibull distributions A Weibull distribution is a statistical distribution describing the likelihood of a specific event occurring within a certain time frame A well-known use of Weibull distributions is for the description of naturally occurring wind speed distributions It also turns out that failures of technical equipment will often follow a Weibull distribution

Page 4 Date Author Copyright © Siemens AG 2009 Energy Sector The Classical “Bathtub” Reliability Curve Each of the three basic curves can be described with a Weibull distribution Time Failure Rate Infant Mortality Random Failure Wear-out Bathtub Curve

Page 5 Date Author Copyright © Siemens AG 2009 Energy Sector The Modified Bathtub Curve With Four Failure Types

Page 6 Date Author Copyright © Siemens AG 2009 Energy Sector ARM Model Basics The ARM model reviews probabilities and consequences on a component level The turbine is split into about 10 main components plus a sweep-up “Others” for minor components. For some main components it is relevant to consider different types of failures with different consequences. For example, the gearbox should be modelled with at least two entries, one for defects that can be corrected in the turbine, and another for defects that require removal of the gearbox – they will have vastly different vessel cost consequences It is Siemens’ experience that sufficient resolution is obtained by review of failure types

Page 7 Date Author Copyright © Siemens AG 2009 Energy Sector For each failure type the ARM Model has the same steps 1.Determination of the Weibull distribution data – shape parameter β and characteristic life η The shape parameter β depends on the failure type. The characteristic life η is the point in time when 1 – 1/e = 63% of components have failed 2.Determination of the failure probability The probability that a failure type will occur By definition random failure and Wear-out affect all components The real difficulty is a realistic estimate of the probability of Infant mortality and Premature serial failure. 3.Determination of the failure consequences Component cost (new / refurbished) Proportion of components that can be refurbished Average crew size and number of working days required on site Technician rate and day rate of any crane / vessel needed Typical mobilization time for crane / vessel Long-term average weather window

Page 8 Date Author Copyright © Siemens AG 2009 Energy Sector Making the ARM Model operational Calculation for all components combined in one Excel sheet Using Step 1-3 data actual calculation is straightforward A component may have more than one set of data If more than one component of the same type results are simply multiplied with the number used per turbine Key results: NPV of cost Downtime / availability Spare parts needed Resources needed

Page 9 Date Author Copyright © Siemens AG 2009 Energy Sector The Snake in the Paradise – Data Quality No model is better than its input data It is notoriously difficult to make predictions – particularly about the future… (Niels Bohr) Estimates of failure probabilities in the wind industry are by definition forward estimates The critical data depend on the failure type: For infant mortality / premature serial failure: η For random failure and wear-out: P(f) Best estimates derived from Well-consolidated operational records Objective assessment using FMEA analysis Common sense

Page 10 Date Author Copyright © Siemens AG 2009 Energy Sector A Real Life Example – Generic Project with 3.6 Distribution of NPV over Component Types Blade Blade_min Pitch bear. Main bear. Gearbox Gearbox min Generator Conv.mod. Yaw ring Yaw gear Others Turbine trsf.

Page 11 Date Author Copyright © Siemens AG 2009 Energy Sector A Real Life Example – Generic Project with 3.6

Page 12 Date Author Copyright © Siemens AG 2009 Energy Sector A Real Life Example – Nysted Availability Time after Take Over (y) Availability (%) Actual Predicted 30 pr. bev. gnsn. (Actual)

Page 13 Date Author Copyright © Siemens AG 2009 Energy Sector Use of ARM Model Results Owner’s use Basis for revenue and income calculations Basis for qualified discussion of operational risks with financers and insurance Basis for long-term asset management Manufacturer’s use Basis for continuous design improvement programs, because the cost- benefit ratio is easily quantified Basis for warranty-period risk assessment Basis for qualified pricing of LTPs (Long Term Packages)

Page 14 Date Author Copyright © Siemens AG 2009 Energy Sector Conclusion – And a Word of Caution An ARM Model can provide lots of answers… Best estimates of costs, downtime, equipment and spare parts needed, etc. Best basis for dialogue with owners, financers, insurance… …But one has to respect the fundamentals! The ARM Model is a probabilistic model Probabilistic models work for large numbers The ARM Model does not provide accurate predictions on turbine level, often not even on project level A good ARM Model provides good predictions on large project level and population level