This document summarizes the analysis of operational data from wind turbine pilot projects. Key findings include: - Data quality and completeness improved over time but remains an ongoing concern. - Availability is reported using the IEC standard to promote consistent metrics. - Power curve and event analysis can identify performance issues but are limited by data quality. - Preliminary event analysis using Weibull techniques showed potential to provide useful insights about common failure modes.

1. Jim Thomas – VP Product Development
Strategic Power Systems, Inc.
Data and its Influence
on Reliability Programs
Sandia 2013 Wind Plant Reliability Workshop
August 14, 2013

2. Topics
 Who we are
 SNL/SPS Pilot
 Pilot Issues and Lessons Learned
 Analysis of Data
 Availability Reporting for Wind
 Power Curve Analysis
 Event Analysis with Weibull Techniques
 Wrap Up
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3. Who we are
 Gas Turbines, Combined Cycles & Fossil Steam
 ORAP: Operational Reliability Analysis Program
 25+ year track record working with OEMs: GE, Mitsubishi, Rolls
Royce, ALSTOM … and many large and small End Users
 RAM Analysis, Benchmarking, Maintenance Forecasting, Parts
Tracking
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ORAP
Participants
 Focus on Thermal
 Extending to Renewables

4. SNL/SPS Pilot
 Wind unit data capture and analysis
 Where we’ve been:
 Five Year Effort with SNL & DOE
 Eight pilots with industry partners: 4 technologies - ~ 700 units
 Focus: Data collection and transformation into time, capacity and events
 Today
 ORAPWind™ Performance Dashboard – 24x7 analysis online
• RAM and Performance data analysis
• Data Completeness and Quality monitoring/metrics
• One minute statistical data
• ORAP Transformed data - time, capacity, availability & events
• Fault/Event analysis
• IEC/IEEE Availability reporting
• Industry benchmarks and NERC GADS reporting
 Beginning to do data analysis
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5. Pilot Issues and Lessons Learned
 Remote monitoring of wind facilities is a challenge:
 Data Gaps from Power Outages, Communications Losses ….
 Static Data: Data values “freeze” for periods of time due to communications issues and uneven
implementation of OPC servers
 What we did:
 Improved robustness of ORAP® Link™ data collection software by building in:
• Automatic restart on service failures
• Automatic monitoring of software and server performance
• Automatic email alerts of issues
 Added Integrity Check of each raw data value for Reasonableness and Static
• Values that fail are not included in Analysis
 Report Data Quality and Completeness to increase awareness of data issues and timeliness of
response to them
 Results: Data Quality/Completeness improved
 ~ 65% in 2011
 ~ 85% in 2012
 ~ 90% in 2013 (expected)
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However, Data Quality is an ongoing concern.

6. Data Quality – Reporting
6
% Information Available by Month

7. Analysis of Data – Availability Reporting
 Availability standard to support consistent metrics across sites and fleet
 Thermal generation uses IEEE 762/ISO 3977 standard – Not granular enough
 NERC GADS reporting will become mandatory
 Awareness of need/solution across the industry
 Adopted IEC TS 61400-26-1: Time Based Availability for Wind Turbine
Generating Systems
 Specifically developed for wind turbines
 Direct US representation on standards committee
 Worked with the NERC GADS Reporting Instructions working group to
promote the IEC standard as a basis for NERC GADS reporting
 Contributed to the AWEA O&M Best Practices Working Group on Data
Reporting KPI’s
 Present IEC based Availability Reporting to promote the standard to support
fleet reporting
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8. Analysis of Data – Availability Reporting Reference
 Developed cross reference between IEC, IEEE and proposed NERC GADS standards
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9. Analysis of Data – Report Availability to IEC Standard
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10. Analysis of Data – Power Curve
 Corrected Power Curve calculations hampered by quality of Met Tower
Data – sensor issues
 Provide both corrected and uncorrected Power Curves
 Provide consolidated look at site Met Tower data to easily visualize issues.
Goal is to create a focus on correcting issues
 Difficult to compare power curve performance of individual turbines
due to the number of turbines
 Provide consolidated view of relative power curve performance of all units
 Power Curve Deviation Chart
• 4 Configurable Points on Power Curve
• Compare average weekly performance at each point to baseline numbers
• Plot poorest performing number for each unit for simplicity
• Look for outliers
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11. Analysis of Data – Power Curve Deviation
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12. Analysis of Data – Event Analysis
 Many units – many events
 Base analysis on thermal experience and evaluate Weibull techniques
 What is Weibull Analysis?
 Why use Weibull?
 We weren’t looking at final results, but trying to determine if the
method could provide information of interest … and it did
 8 ORAPWind Pilot Partners Sites
 4 Technologies
 Focused on most common technology in Pilot set
• GE 1.5: 5 sites - ~ 500 Units - Several variants
 We have:
 Faults
 Downtime durations
 Frequency
 Selected one Partner with multiple sites to evaluate
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13. Analysis of Data – Event Analysis – Counts
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All Sites (479)
Events
# of
events
# of units
affected
avg # events
per unit
% of
units
affected
1 1144: Blade Angle Asymmetry 5649 152 37.16 31.73%
2 1141: Rotor CCU Collective Faults 5142 291 17.67 60.75%
3 1106: Rotor CCU Fault Current 2204 107 20.60 22.34%
4 1134: Battery Charging Voltage Not OK 2190 169 12.96 35.28%
5 1177: Tower Vibration 1971 137 14.39 28.60%
6 1113: Line CCU Fault Voltage 1744 56 31.14 11.69%
7 1077: Gearbox Oil Overtemperature 1523 42 36.26 8.77%
8 1142: Line CCU Collective Faults 1405 48 29.27 10.02%
9 1122: Collective Fault Pitch Controller 1231 158 7.79 32.99%
10 1125: Pitch Overrun 90° 1101 147 7.49 30.69%
16 1119: Timeout Pitch Controller 893 114 7.83 23.80%
Company A (~200 Units)
Events
# of
events
# of units
affected
avg #
events
per unit
% of units
affected
1 1122: Collective Fault Pitch Controller 1153 138 8.36 68.66%
2 1141: Rotor CCU Collective Faults 624 74 8.43 36.82%
3 1276: Pitch Thyristor 3 Fault 523 85 6.15 42.29%
4 1275: Pitch Thyristor 2 Fault 453 88 5.15 43.78%
5 1027: Secondary Braking Time Too High 352 165 2.13 82.09%
5 1053: Wind Vane Defect 352 92 3.83 45.77%
6 1274: Pitch Thyristor 1 Fault 273 103 2.65 51.24%
7 1121: Axis 1 Fault Pitch Controller 238 100 2.38 49.75%
8 1119: Timeout Pitch Controller 231 70 3.30 34.83%
9 1145: Pitch Control Deviation Axis 1 200 36 5.56 17.91%
10 1214: Battery Voltage Not OK Axis 3 178 65 2.74 32.34%
Site 1
Events # of events
avg # events
per unit
% of
units
affected
1 1291: Undertemperature Cabinet 110 15.71 10.45%
2 1027: Secondary Braking Time Too High 100 2.13 70.15%
3 1141: Rotor CCU Collective Faults 94 5.22 26.87%
4 1060: Yaw Limit Switch Activated 85 5.31 23.88%
5 1275: Pitch Thyristor 2 Fault 64 3.05 31.34%
6 1274: Pitch Thyristor 1 Fault 54 2.16 37.31%
7 1119: Timeout Pitch Controller 52 4.33 17.91%
8 1276: Pitch Thyristor 3 Fault 51 2.43 31.34%
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1028: No Speed Reduction With Secondary
Braking 47 1.52 46.27%
10 1144: Blade Angle Asymmetry 43 2.15 29.85%
10 1045: Hydraulic Pump Time Too High 43 7.17 8.96%
Site 2
Events # of events
avg #
events per
unit
% of units
affected
1 1122: Collective Fault Pitch Controller 1137 8.49 100.00%
2 1141: Rotor CCU Collective Faults 530 9.46 41.79%
3 1276: Pitch Thyristor 3 Fault 472 7.38 47.76%
4 1275: Pitch Thyristor 2 Fault 389 5.81 50.00%
5 1053: Wind Vane Defect 310 3.52 65.67%
6 1027: Secondary Braking Time Too High 252 2.14 88.06%
7 1121: Axis 1 Fault Pitch Controller 238 2.38 74.63%
8 1274: Pitch Thyristor 1 Fault 219 2.81 58.21%
9 1145: Pitch Control Deviation Axis 1 197 5.97 24.63%
10 1119: Timeout Pitch Controller 179 3.09 43.28%

14. Analysis of Data – Event Analysis – Duration Review
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All Sites
Events Duration Ranking <10min 10-20min 20-30min
30min-
1hr 1hr-1.5hrt 1.5-3hrs >3hr Total Events
1144: Blade Angle Asymmetry 4127 351 163 244 487 158 119 5649
1141: Rotor CCU Collective Faults 3473 358 269 295 165 252 330 5142
1106: Rotor CCU Fault Current 1321 148 112 184 105 135 199 2204
1134: Battery Charging Voltage Not OK 455 117 229 257 927 99 106 2190
1177: Tower Vibration 1760 52 20 40 13 26 60 1971
1113: Line CCU Fault Voltage 1437 67 36 41 24 50 89 1744
1077: Gearbox Oil Overtemperature 210 206 274 746 29 51 7 1523
1142: Line CCU Collective Faults 986 96 48 80 43 90 62 1405
1122: Collective Fault Pitch Controller 681 214 95 145 42 26 28 1231
1125: Pitch Overrun 90° 841 76 28 42 25 56 33 1101
1119: Timeout Pitch Controller 530 81 46 68 44 57 67 893
Company A
Events <10min 10-20min 20-30min
30min-
1hr 1hr-1.5hrt 1.5-3hrs >3hr Total Events
1122: Collective Fault Pitch Controller 625 210 91 142 38 23 24 1153
1141: Rotor CCU Collective Faults 437 64 28 28 15 16 36 624
1276: Pitch Thyristor 3 Fault 241 71 39 61 26 27 58 523
1275: Pitch Thyristor 2 Fault 202 56 36 50 18 28 63 453
1027: Secondary Braking Time Too High 121 66 23 31 19 25 67 352
1053: Wind Vane Defect 220 15 4 12 10 14 77 352
1274: Pitch Thyristor 1 Fault 141 42 25 21 12 13 19 273
1121: Axis 1 Fault Pitch Controller 131 38 17 34 7 8 3 238
1119: Timeout Pitch Controller 159 20 15 17 2 5 13 231
1145: Pitch Control Deviation Axis 1 126 11 29 17 4 5 8 200
1214: Battery Voltage Not OK Axis 3 3 13 37 26 29 29 41 178

15. Analysis of Data – Event Analysis – Downtime Rankings - Sites
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Company A - Site 1
Events <10min 10-20min 20-30min 30min-1hr 1hr-1.5hrt 1.5-3hrs >3hr Total Events
1291: Undertemperature Cabinet 104 1 1 1 2 1 0 110
1027: Secondary Braking Time Too High 27 15 5 9 5 17 22 100
1141: Rotor CCU Collective Faults 58 14 7 5 3 2 5 94
1060: Yaw Limit Switch Activated 46 15 3 8 1 3 9 85
1275: Pitch Thyristor 2 Fault 28 8 5 5 0 7 11 64
1274: Pitch Thyristor 1 Fault 19 5 12 6 2 4 6 54
1119: Timeout Pitch Controller 42 3 2 4 1 0 0 52
1276: Pitch Thyristor 3 Fault 21 10 2 8 1 3 6 51
1028: No Speed Reduction With Secondary
Braking 13 5 4 4 1 2 18 47
1144: Blade Angle Asymmetry 28 2 1 6 1 2 3 43
1045: Hydraulic Pump Time Too High 27 3 1 2 0 7 3 43
Company A - Site 2
Events <10min 10-20min 20-30min 30min-1hr 1hr-1.5hrt 1.5-3hrs >3hr Total Events
1122: Collective Fault Pitch Controller 614 210 90 140 37 23 23 0
1141: Rotor CCU Collective Faults 379 50 21 23 12 14 31 1137
1276: Pitch Thyristor 3 Fault 220 61 37 53 25 24 52 530
1275: Pitch Thyristor 2 Fault 174 48 31 45 18 21 52 472
1053: Wind Vane Defect 199 8 3 9 9 9 73 389
1027: Secondary Braking Time Too High 94 51 18 22 14 8 45 310
1121: Axis 1 Fault Pitch Controller 131 38 17 34 7 8 3 252
1274: Pitch Thyristor 1 Fault 122 37 13 15 10 9 13 238
1145: Pitch Control Deviation Axis 1 126 10 29 16 4 5 7 219
1119: Timeout Pitch Controller 117 17 13 13 1 5 13 197

16. Analysis of Data – Event Analysis – Weibull Distribution
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> 20 min downtime, >1 hr. between faults
Timeout Pitch Controller – All Sites Timeout Pitch Controller – All Sites
• Alpha = 122
• Beta = .38
• Alpha = 17
• Beta = .28
• Alpha = 26
• Beta = .24
• Alpha = 206
• Beta = .42
> 20 min downtime, >1 hr. between faults
Timeout Pitch Controller – Site 2 Timeout Pitch Controller – Site 2

17. Wrap Up
 Five year focused effort. Data collection live ~3 years
 Many challenges and lessons learned
 Data Quality is the largest challenge
 Data Quality much improved over the 3 years
 Begun to analyze data with interesting results
 IEC Availability specification is a viable approach to standardize analysis
 Power Curve presents challenges due to met tower data quality and
number of units
• Met tower data presentation to create understanding of issues
• Power Curve Deviation presents analysis of many units on one page
 Event Analysis presents many challenges that we are just beginning to
tackle
• Commonality of faults across multiple sites and site ages
• Weibull distribution analysis provides some interesting correlations that we
will explore in the future
 Questions?
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