Jim Thomas – VP Product DevelopmentStrategic Power Systems, Inc.

Data and its Influence on Reliability Programs

Sandia 2013 Wind Plant Reliability Workshop

August 14, 2013

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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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

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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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.

Data Quality – Reporting

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% Information Available by Month

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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Analysis of Data – Availability Reporting Reference

Developed cross reference between IEC, IEEE and proposed NERC GADS standards

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Analysis of Data – Report Availability to IEC Standard

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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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Analysis of Data – Power Curve Deviation

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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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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

affected1 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 eventsavg # events

per unit

% of units

affected1 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%

91028: 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%

Analysis of Data – Event Analysis – Duration Review

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All Sites

Events Duration Ranking <10min 10-20min 20-30min30min-

1hr 1hr-1.5hrt 1.5-3hrs >3hr Total Events1144: Blade Angle Asymmetry 4127 351 163 244 487 158 119 56491141: Rotor CCU Collective Faults 3473 358 269 295 165 252 330 51421106: Rotor CCU Fault Current 1321 148 112 184 105 135 199 22041134: Battery Charging Voltage Not OK 455 117 229 257 927 99 106 21901177: Tower Vibration 1760 52 20 40 13 26 60 19711113: Line CCU Fault Voltage 1437 67 36 41 24 50 89 17441077: Gearbox Oil Overtemperature 210 206 274 746 29 51 7 15231142: Line CCU Collective Faults 986 96 48 80 43 90 62 14051122: Collective Fault Pitch Controller 681 214 95 145 42 26 28 12311125: Pitch Overrun 90° 841 76 28 42 25 56 33 11011119: Timeout Pitch Controller 530 81 46 68 44 57 67 893

Company A

Events <10min 10-20min 20-30min30min-

1hr 1hr-1.5hrt 1.5-3hrs >3hr Total Events1122: Collective Fault Pitch Controller 625 210 91 142 38 23 24 11531141: Rotor CCU Collective Faults 437 64 28 28 15 16 36 6241276: Pitch Thyristor 3 Fault 241 71 39 61 26 27 58 5231275: Pitch Thyristor 2 Fault 202 56 36 50 18 28 63 4531027: Secondary Braking Time Too High 121 66 23 31 19 25 67 3521053: Wind Vane Defect 220 15 4 12 10 14 77 3521274: Pitch Thyristor 1 Fault 141 42 25 21 12 13 19 2731121: Axis 1 Fault Pitch Controller 131 38 17 34 7 8 3 2381119: Timeout Pitch Controller 159 20 15 17 2 5 13 2311145: Pitch Control Deviation Axis 1 126 11 29 17 4 5 8 2001214: Battery Voltage Not OK Axis 3 3 13 37 26 29 29 41 178

Analysis of Data – Event Analysis – Downtime Rankings - Sites

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Company A - Site 1Events <10min 10-20min 20-30min 30min-1hr 1hr-1.5hrt 1.5-3hrs >3hr Total Events1291: Undertemperature Cabinet 104 1 1 1 2 1 0 1101027: Secondary Braking Time Too High 27 15 5 9 5 17 22 1001141: Rotor CCU Collective Faults 58 14 7 5 3 2 5 941060: Yaw Limit Switch Activated 46 15 3 8 1 3 9 851275: Pitch Thyristor 2 Fault 28 8 5 5 0 7 11 641274: Pitch Thyristor 1 Fault 19 5 12 6 2 4 6 541119: Timeout Pitch Controller 42 3 2 4 1 0 0 521276: Pitch Thyristor 3 Fault 21 10 2 8 1 3 6 511028: No Speed Reduction With Secondary Braking 13 5 4 4 1 2 18 471144: Blade Angle Asymmetry 28 2 1 6 1 2 3 431045: Hydraulic Pump Time Too High 27 3 1 2 0 7 3 43

Company A - Site 2Events <10min 10-20min 20-30min 30min-1hr 1hr-1.5hrt 1.5-3hrs >3hr Total Events1122: Collective Fault Pitch Controller 614 210 90 140 37 23 23 01141: Rotor CCU Collective Faults 379 50 21 23 12 14 31 11371276: Pitch Thyristor 3 Fault 220 61 37 53 25 24 52 5301275: Pitch Thyristor 2 Fault 174 48 31 45 18 21 52 4721053: Wind Vane Defect 199 8 3 9 9 9 73 3891027: Secondary Braking Time Too High 94 51 18 22 14 8 45 3101121: Axis 1 Fault Pitch Controller 131 38 17 34 7 8 3 2521274: Pitch Thyristor 1 Fault 122 37 13 15 10 9 13 2381145: Pitch Control Deviation Axis 1 126 10 29 16 4 5 7 2191119: Timeout Pitch Controller 117 17 13 13 1 5 13 197

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

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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