Intelligent Malfunction Prognostics

1. Intelligent Malfunction Prognostics From equipment condition monitoring to optimal asset management EWEA Annual Conference, Brussels, Belgium, March 14-17, 2011

2. There are many CMS for WT on the market, differing in their functional scope, WT component focus, learning capabilities and life cycle stage Intelligent Malfunction Prognostics Solution Profile Scope Monitoring (Predictive) Diagnostics Prognostics Focus Pitch Drive Gearbox Generator Converter Yaw     Learning Manual Automated, unit-level Automated, fleet-wide Stage R&D Installation Validation 10s 100s 1000s

3. Intelligent Malfunction Prognostics Intelligent malfunction prognostics can be provided through reliability reports, supporting critical decisions on maintenance scope and schedule Reliability Report View condition diagnostics a View malfunction diagnostics b View malfunction prognostics c a Aggregate prognostics d f Cross-check maintenance plan e Extend condition data sources f Extend malfunction modes g b c Extend prognostic horizon h g d e h

4. Intelligent Malfunction Prognostics Reliability reports aggregate to a fleet level, with navigation functions, consolidating diagnostic insight and prognostic foresight for several units Fleet View Unit vs. Fleet View Unit View • Monitor power output and technicalcondition of each unit in the fleet • Review diagnostic insights for units in marginal or critical condition • Use prognostic foresight to optimize fleet maintenance process • Identify and avoid unnecessary preventive measures and costs • Anticipate malfunctions beforefailure, damage, foregone output • Realize a commercially optimal fleet maintenance schedule

5. Intelligent Malfunction Prognostics Crucial condition data is captured through vibration and lubricant sensors, and directly uploaded into the WT controller via standard protocols Hardware Package Wind Turbine L1 V1 V4 V5 V6 • Inline twin laser particle counter • Latest-generation technology • Integrated humidity sensor • Stainless steel block V8 V2 L1 R2 V7 R1 T4 T5 T6 T8 T7 V3 T2 T3 E1 T1 T,R,E V,L V8 V1 Nacelle … Rotor hub Blade • Very low frequency accelerometer • High sensitivity & accuracy • Latest-generation technology • Armored integral cable Pitch Gearbox Generator Slow rotating shaft Fast rotating shaft Rotor bearing V,L Bearing Tower Yaw drive • Versatile Profibus terminal • Easy plug-in installation • Straightforward configuration • Meeting OEM standards Foundation SCADA controller Sensor controller Ethernet Switch WT Server

6. Intelligent Malfunction Prognostics Reliability reports are updated with new condition and process data in periodical intervals, and delivered to the operators on-line via reliability portal Data Transfer Fleet Server • Report Review • Asset Mgt. Decisions • Spare Part Mgt. • Capacity Forecasts WP Operator Wind Park • Report Review • Maintenance & Service Scheduling WP Service Providers WT Server Router, Firewall Internet 1 4 7 ISDN, ADSL, or similar • Ascertain state-of-the-art prognostic solution 2 5 8 WP Insurer WP Server 3 6 Etc. Download batches of condition and process data (V,L,T,R,E) for all WT in regular intervals Upload WP Reliability Reportsin corresponding intervals A LAN / Ethernet or similar WT Manufacturer • Hosting Network Mgt and PLC Controller • Consolidation of SCADA data from all WT • Consolidation of additional sensor data • Forwarding of consolidated data batches to Cassantec Cassantec Server • Gather malfunction and failure statistics • Inform suppliers of components affected • Improve quality of WT components affected Further Wind Park B • Data Mgt & Archiving • Condition Monitoring • Malfunction Diagnostics • Failure Prognostics • Intelligent Reporting Further Wind Park Etc. WP = Wind Park, WT = Wind Turbine

7. Intelligent Malfunction Prognostics We have calibrated and validated our reliability reporting solution with off-line and on-line data from several wind farms predominantly in the U.S. Field Validation of Solution Illustrative Example Wind Farm: Buffalo Ridge near Alta, IA, U.S.A. WF Capacity: 150 x 750 kW = 112.5 MW WT Models: Zond Z-46 (now GE) Sampling period: 2006 – 2010 (on- & off-line) Sampling intervals: continuous to 6 months Malfunction modes: e.g. Gearbox LS wheel wear Causes: e.g. Micro pitting, contributed by water ingress Impact: e.g. Bearing life reduces by factor 3 Learnings: ► Upgrade sensor hardware ►Monitor condition dynamics ►Exploit fleet intelligence Map source: www.google.comLogo source: www.altaiowa.com

8. $$$ $$ $ Intelligent Malfunction Prognostics We achieve malfunction and failure prognostics over an explicit time horizon exceeding the best “predictive diagnostic” approaches on the market so far Prognostic Horizon Value addedby reliability report EquipmentProcurement& Replacement Potentialfuture capability MaintenanceCycleScheduling Our current capability Work OrderScheduling Competitor capabilities UnscheduledOutageCoordination RoutineMonitoring 0 Prognostic horizon[Days after last update] 100 0 1 10 1.000

9. Intelligent Malfunction Prognostics Benefits of reliability reports have been confirmed by operators beyond wind power – these benefits increase over time through machine learning Prognostic Accuracy April 2007 August 2008 March 2009 June 2010 July 2010 Nov. CouplingAlignmentMech. seal Cartridge sealsMech. seals Cartridge sealsMech. seals Cartridge seals CouplingAlignment OK • In retrospect, 99% of predictable malfunctions were accurately predicted, with a horizon of up to 5 years (!) • Operator knowledge was exceeded by 20%, with several surprises (e.g. cartridge sealing) • Diagnostics und prognostics are enhanced over time through machine learning

10. Intelligent Malfunction Prognostics Value bands must be continuously „learned“ from the empirical condition data: Even with constant equipment utilization, value bands may shift over time! Machine Learning  „Normal“ value bands shift and evolve Example for learning value bands Static value bands not useful  Collective learning process for equipment of same type (flagging before adjusting)

11. Intelligent Malfunction Prognostics This learning process is initialized at different parameter value levels – gearbox oil has fluctuating initial levels of cleanliness, mostly within tolerance intervals Learning Process Initialization  Gearbox oil is rarely “clean” to begin with: units start up with varying levels of initial contami-nation Example for flexibleinitialization 21 18 16 Learning process initialization for equipment of same type

12. Intelligent Malfunction Prognostics In summary, we are targeting new features allowing commercially optimal fleet maintenance schedules, cutting costs of failure, damage and lost power output Technical & Commercial Target Benefits • State-of-the-art sensor hardware • High-end specialized sensors for wind power applications • Integration of latest technologies (e.g. twin laser particle counters) • Full utilization (and no duplication) of existing data and infrastructure (SCADA) • Intelligent diagnostics • Comprehensive expertise on model-specific malfunction and failure sources and risk • Automated learning from ongoing monitoring of the entire fleet • Reference data from other WT, fleets, applications • Advanced prognostics • Extended prognostic horizon through computational stochastic model • Full utilization of recorded and archived condition and process data histories per WT • Prognostic accuracy exceeding capabilities of any competing product on the market • Cost-effective advice on optimal fleet asset management • Reduction of risk and costs for WT malfunction, failure, damage and foregone power output • Reduction of risk and costs of unnecessary preventive measures and foregone power output • Realization of a commercially optimal condition-based fleet maintenance schedule State-of-the-art Enhanced New Enhanced

13. Intelligent Malfunction Prognostics For further information, please review our brochure on-line, and contact us by e-mail or telephone Further Information • To obtain more information, please download our brochure at www.cassantec.com/wind.pdf • Or send an e-mail to info@cassantec.com Cassantec team behind this presentation Frank KirschnickZurich, Switzerland Heinz GiovanelliMunich & Zurich Gary EllisCleveland, Ohio Shuang YuanZurich, Switzerland Mart GrasmederCleveland, Ohio Katerina StamouZurich, Switzerland Mila VodovozovaZurich, Switzerland

14. Intelligent Malfunction Prognostics Appendix

15. Intelligent Malfunction Prognostics Cassantec is an independent provider of integrated, automatedprognostic services for critical power plant equipment with a unique, protected technology Profile of Cassantec Ltd. Meaning: Cassantec = Cassandra Technologies Position: Independent provider of integrated, automated equipment condition diagnostics and malfunction prognostics Technology:Novel combination of best practice techniques from Operations Research, Artificial Intelligence and Data Mining Comprehensive condition data reference base (since 1993): 500k data sets of 20 equipment types, 2000 models Offering: Online Condition Monitoring Systems and Reliability Reports on a subscription basis for equipment operators worldwide References: Chemical and Power industries (U.S.A. and Europe) including nuclear and fossil-fired power plants and wind farms Promoters: Power corporations, private investors, Swiss government (CTI) Industry Partner: Leading independent U.S. lubricant lab (Insight Services) Academic Partner:EPFL, ETHZ, Stanford University Cassandra prophet of critical future events in the Greek mythology

16. Intelligent Malfunction Prognostics Our prognostic services have been successfully applied to a wide range of power equipment, with operators in different regions and industry segments Cassantec References (Excerpt) Wind Fossil Nuclear Chemical Steel