Technology and Reliability Improvements in GE’s 1.5MW Wind Turbine Fleet
Wind Turbine Reliability Workshop Sandia National Labs Albuquerque, NM
James R. Maughan General Manager Product Service and Warranty GE Wind Energy
September 17, 2007 GE and GE Wind GE’s Infrastructure Businesses
Energy Rail Financial Water Aircraft Oil & Gas - Thermal Services Engines - Nuclear - Renewables Solar Hydrogen
Wind Biomass
James Maughan, GE Wind Wind Turbine Reliability Workshop p. 2 GE Energy’s Start in Wind Power
• Formed May 2002…Purchased from Enron • Foresee significant long term, global demand for Wind Turbines • Continue growth with… Technology - integrate with GE to improve on starting point…reliability, capacity factor, cost Supply Chain – more turbines, with predictability Permitting & Transmission – more turbine locations Policy - support Long-term commitment • Eco magination
“Green is Green”…For Everyone
James Maughan, GE Wind Wind Turbine Reliability Workshop p. 3 GE Wind Growth and Investment
Accelerating global deployment GE Wind since 2002 :
$600MM+ technology investment … (units/yr) 2,500+ • Unit production: 400 to 2500+ • Key suppliers: up 3x • 2007 Revenue: $4.5B+ • Fleet size: 2000 to 8000 ~400 • Continued within 2-3 years … ‘02 ‘07 • Expect policy issues to stabilize • Managing supply chain … integrated with global suppliers • Continue to grow supply chain • Expand capacity • Operational differentiation … driving dependable fulfillment • Becoming GE’s largest Energy product business • Advancing technology … efficiency, reliability, diagnostics
It All Depends on Reliability
James Maughan, GE Wind Wind Turbine Reliability Workshop p. 4 Why Reliable Operation is Everything
Customer View • Price + Capacity Factor + Availability + Operating Costs = Customer Rate of Return • Reliability issues directly cut production • Reliability issues increase operating costs • Reliability issues are aggravating
Reliability and Availability • Reliability…component focused - Failure rates, MTBF, MTBT, starting reliability, etc • Availability…operator focused - Fraction of time system is able to operate as intended - Includes impact of failure on operation - Failure rates, faults, outage length, planned repair, maintenance, etc - Ties to lost production, lost revenue
The Fuel is Free. The Turbine Needs To Run. James Maughan, GE Wind Wind Turbine Reliability Workshop p. 5 1.5sle Availability Summary - Customer Fleet View
100% 90% 80% 70% 60% 50% 2005 40% 30% Percent of Fleet Percent 2007 20% 10% 0% 0 0.2 0.4 0.6 0.8 1 Availability Engineering Availability • Includes downtime for any reason (except grid outages) • Duration from coming off-line to return to service • Faults, repairs, maintenance, upgrades, etc • Lost production calculated from wind speeds during downtime
James Maughan, GE Wind Wind Turbine Reliability Workshop p. 6 Availability Analysis By Fault Count and Duration
2000 # Events 2500 1500 Downtime 2000
1000 1500 1000
Fault Count Fault 500 500 Fault Downtime, Fault hrs 0 0 0-1 h 1-4 h 4-24 h 1-4 d > 4 d
System Faults Component Failures
•More frequent but shorter events •Less frequent but longer events from from system, thermal, integration, component failures and operation issues - impact is lost production and - impact is primarily lost cost of component production
James Maughan, GE Wind Wind Turbine Reliability Workshop p. 7 Fleet Relative Cost of Component Failure
Failure Data - Weibull Predictions ββββ 6.0 6.0 6.0 6.0 3.0 3.0 3.0 3.0 2.0 2.0 2.0 2.0 1.6 1.6 1.6 1.6 1.4 1.4 1.4 1.4 1.2 1.2 1.2 1.2 1.0 1.0 1.0 1.0 0.9 0.9 0.9 0.9 0.8 0.8 0.8 0.8 0.7 0.7 0.7 0.7 0.6 0.6 0.6 0.6 0.5 0.5 0.5 0.5 99.00
90.00
50.00
10.00
5.00
1.00 Unreliability, Unreliability, Unreliability, F(t) F(t) 0.50
0.10
0.05
0.01 100.00 1000.00 10000.00 100000.00 Cycles
•Lost Production and replacement cost data •Failure data and predictions for continuous product improvement
James Maughan, GE Wind Wind Turbine Reliability Workshop p. 8 Typical Failure Root Cause Analysis Process Failure Analysis Analytics
Annual Annual Annual energy Annual Probability energy Annual Probability energy Annual Probability production number of per w ind production num ber of per w ind production num ber of per w ind per w ind revolutions bin per w ind revolutions bin per w ind revolutions bin bin (kWh) bin (kWh) bin (kWh)
Avg windspeed 7.5 7.5 7.5 8.5 8.5 8.5 10 10 10 (m /s) >>>
8.463 8.463 8.463 9.591 9.591 9.59 11.284 11.284 11.284 1 0 0 0.03 0 0 0.02 0 0 0.016 2 0 0 0.05 0 0 0.04 0 0 0.030 3 0 437,410 0.07 0 350,384 0.06 0 260,294 0.044 4 33,552 529,028 0.09 27,462 433,008 0.07 20,835 328,517 0.055 5 112,715 604,015 0.10 94,850 508,278 0.08 73,935 396,202 0.064 6 221,454 712,198 0.10 192,774 619,961 0.09 155,321 499,511 0.071 7 358,705 807,086 0.10 325,002 731,254 0.09 272,299 612,672 0.075 8 511,763 839,611 0.09 485,599 796,685 0.09 425,625 698,290 0.076 9 655,944 775,207 0.08 655,858 775,105 0.08 605,008 715,009 0.075 10 714,918 668,306 0.07 757,894 708,480 0.07 740,242 691,979 0.072 11 675,418 548,684 0.06 763,851 620,523 0.06 794,694 645,579 0.067 12 565,133 434,475 0.04 686,033 527,424 0.05 764,846 588,016 0.061 13 445,726 332,263 0.03 584,380 435,622 0.05 702,385 523,587 0.054 14 332,432 245,653 0.03 473,629 349,991 0.04 617,419 456,246 0.047 15 238,760 175,728 0.02 371,946 273,753 0.03 529,049 389,380 0.040 16 165,366 121,709 0.01 283,414 208,593 0.02 442,511 325,688 0.034 17 110,951 81,660 0.01 210,494 154,923 0.02 362,944 267,126 0.028 RCA Fishbone, Identification, Validation 18 72,146 53,099 0.01 152,449 112,203 0.01 292,036 214,938 0.022
Design WTG System Root Cause Contributor Eliminated Material Analysis
Mechanical Wear Above rated pitch motor of Brushes Mechanical Wear of gold currents-(Temperature/Wear) coating on slip ring. Bearing Friction Protection devices CB for slip ring 400 V, 50 A Old Style to new style transition in design
Slip Ring
Inadequate Brush Replacement Humidity Temperature. Slip Ring Lubrication procedures Assembly error Slip Slip ring cleaning Ring Assembly procedures. BAA Faults
Lightning Strikes
Maintenance Other
James Maughan, GE Wind Wind Turbine Reliability Workshop p. 9 Availability Analysis By Fault Count and Duration
2000 # Events 2500 1500 Downtime 2000
1000 1500 1000
Fault Count Fault 500 500 Fault Downtime, Fault hrs 0 0 0-1 h 1-4 h 4-24 h 1-4 d > 4 d
System Faults Component Failures
•More frequent but shorter events •Less frequent but longer events from from system, thermal, integration, component failures and operation issues - impact is lost production and - impact is primarily lost cost of component production
James Maughan, GE Wind Wind Turbine Reliability Workshop p. 10 Availability Analysis by Return to Service Process Step
3000
2500
2000
1500
1000 Unavailability(hours) Unavailability(hours) 50
0 Planned In Repair Repair In Maintenance Planned Repair Pending Repair Fault Handling / Fault Handling Mandatory Wait Mandatory
Return to Service Process Steps 1. Identification, remote diagnostics, waiting period (if required) 2. Assemble needed teams, tools, and parts 3. Repair time
James Maughan, GE Wind Wind Turbine Reliability Workshop p. 11 Typical Fault And Availability Data
Unavailability Lost Production 300 180
250 150
200 120
150 90
Unavailability (hrs) Unavailability 100 60 Lost Production, MWhr Production, Lost
50 30
0 0 Yaw runaway Yaw Yaw runaway Yaw Planned repair Planned Planned repair Planned Motor protection Motor Motor protection Motor Program start PLC start Program Weather downtime Weather Program start PLC start Program Weather downtime Weather Pitch thyristor 1 fault 1 thyristor Pitch Gearbox oil pressure oil Gearbox Main switch released switch Main Pitch thyristor 1 fault 1 thyristor Pitch Gearbox oil pressure oil Gearbox Main switch released switch Main Planned Maintenance Planned Planned Maintenance Planned Blade angle asymmetry angle Blade Rotor CCU fault current fault CCU Rotor Rotor CCU fault voltage fault CCU Rotor Rotor CCU fault current fault CCU Rotor Blade angle asymmetry angle Blade Battery charging voltage charging Battery Rotor CCU fault voltage fault CCU Rotor Battery charging voltage charging Battery Cabinet Over temperature Over Cabinet Cabinet Over temperature Over Cabinet Rotor CCU collective faults collective CCU Rotor Rotor CCU collective faults collective CCU Rotor
•Lost production guides technical effort, field solutions, component redesign, system improvement
James Maughan, GE Wind Wind Turbine Reliability Workshop p. 12 Wind Turbine Blade Angle Asymmetry
Motor Gear box
Drive gear
Bearing
Electronic Control
Background • Electronic pitch control system sets blade angle during high load wind control • Under some conditions on some units, one blade lags behind, unit shuts down
James Maughan, GE Wind Wind Turbine Reliability Workshop p. 13 Anatomy of a Blade Angle Asymmetry Fault
20 Blade Angle 1 - Actual 18 Blade Angle 2 - Actual 16 Blade Angle 3 - Actual 4. Blades return to Blade Angle Setpoint feathered position on 14 grid or battery power. Unit restarts. 12 3. Unit shuts down 2. Blade 3 begins due to potential 10 1. Blades all to lag behind asymmetric loads following 8 setpoint Blade Angle, Blade deg 6
4
2
0 200 250 300 350 400 450 500 Time [sec]
James Maughan, GE Wind Wind Turbine Reliability Workshop p. 14 BAA - Root Cause Analysis Root Cause Analysis Bearing Torque Data Summary • Variability in torque requirements not seen in prototype data 20 Bearing Torque Characteristics • ~4% of bearings outliers Revised • Cause of variation not definitively Torque identified
y Capability Torque 10 Capability System Solution equenc r
F • Torque applied to bearing increased with use of higher ratio Outliers gearbox 0 • Loads, lifing, response time all
RMS Torque, Nm validated
Possible Corrective Action • Uprate pitch gearbox • Avg 87% reduction in impact validated
Pitch System Gearbox
James Maughan, GE Wind Wind Turbine Reliability Workshop p. 15 Cabinet Over Temperature Faults
Background • Energy dissipated in pitch motors and hub increases cabinet temperatures Top Box • Temperature protection shuts down Hub Center Cabinet unit if necessary… • … in some cases below spec of 40C outside ambient
Impact 30 Hub Axis 25 Cabinet (3) Hub Battery 20 Cabinets (3) 15 10 5
Lost Production,Lost MWhrs 0 FW01 FW10 FW19 FW28 FW37 FW46
James Maughan, GE Wind Wind Turbine Reliability Workshop p. 16 Hub Ventilation Schematic Root Cause Analysis • Vendor cabinets tightly sealed Flexible hose • Hub is stagnant and Center unventilated Cabinet • Internal hub temperature exhaust can peak higher than Ambient ambient Air
Possible Corrective Action • Create consistent air flow Screen and deflector through hub with vent • Screen and deflectors to limit water, snow, debris, etc
James Maughan, GE Wind Wind Turbine Reliability Workshop p. 17 Typical Hub Temperature Data With Ventilation
50
Tamb 45 Thub Wind Tcenter Center Cabinet 40
35
30 Hub 25
20
Temp, C, orSpeed,C, m/s Temp, Ambient 15 Wind
10 Speed
5
0 4-Mar-07 5-Mar-07 6-Mar-07 7-Mar-07 8-Mar-07 9-Mar-07 10-Mar-07 11-Mar-07 12-Mar-07
- Hub temperature within a few degrees of outside ambient temperature James Maughan, GE Wind Wind Turbine Reliability Workshop p. 18 Typical Fault And Availability Data
Unavailability Lost Production 300 180
250 150
200 120
150 90
Unavailability (hrs) Unavailability 100 60 Lost Production, MWhr Production, Lost
50 30 Field Solutions Developed 0 0 Yaw runaway Yaw Yaw runaway Yaw Planned repair Planned Planned repair Planned Motor protection Motor Motor protection Motor Program start PLC start Program Weather downtime Weather Program start PLC start Program Weather downtime Weather Pitch thyristor 1 fault 1 thyristor Pitch Gearbox oil pressure oil Gearbox Main switch released switch Main Pitch thyristor 1 fault 1 thyristor Pitch Gearbox oil pressure oil Gearbox Main switch released switch Main Planned Maintenance Planned Planned Maintenance Planned Blade angle asymmetry angle Blade Rotor CCU fault current fault CCU Rotor Rotor CCU fault voltage fault CCU Rotor Rotor CCU fault current fault CCU Rotor Blade angle asymmetry angle Blade Battery charging voltage charging Battery Rotor CCU fault voltage fault CCU Rotor Battery charging voltage charging Battery Cabinet Over temperature Over Cabinet Cabinet Over temperature Over Cabinet Rotor CCU collective faults collective CCU Rotor Rotor CCU collective faults collective CCU Rotor New System Designs
James Maughan, GE Wind Wind Turbine Reliability Workshop p. 19 GE Pitch Control System
Legacy System
GE Redesign
System Redesign Number Cumulative Trips of • Higher torque and temperature capability
• Improved batteries and 0 chargers 0 24000 48000 72000 96000 120000 120000 Time, hrs • PWM conversion, reduced slip ring current Validation • Digital control, fewer • Fault counts at validation site components • Half old system, half new • Ethernet-based diagnostics James Maughan, GE Wind Wind Turbine Reliability Workshop p. 20 1.5 MW DFIG IGBT-based Power Converter
Control DC link AC to DC Grid Disturbance Continued operation Cards AC from Generator
Grid Voltage 0.200 at Converter sec
Reactive Current Power Conditioning Power
60 Hz to Grid
Liquid DC to 60 Hz AC Cooling
Power Converter Low Voltage Ride Through • Replaces multiple vendors • Based on GE’s motor drive • Digital monitoring and and generator exciter control technology technology • Remains connected during • Enhanced diagnostics, grid disturbance capability, reliability • Uses reactive power flow to • Operates under authority of ride through disturbance legacy main control James Maughan, GE Wind Wind Turbine Reliability Workshop p. 21 Electrical System Simplification and Integration
System Redesign • Integrates converter, main controller, and power distribution into a single cabinet • Ethernet diagnostics and control across converter, pitch, control, generator, all electrical • Modernized software, blockware, and toolkit, - identical to Mark VIe control in gas turbines • Discrete components in top box replaced with circuit boards • Prototypes in installation now
James Maughan, GE Wind Wind Turbine Reliability Workshop p. 22 Summary - Improving GE’s Fleet Reliability
100% 90% 80% 70% 60% 50% 40% 30% PercentFleet of 20% 10% 0% 0 0.2 0.4 0.6 0.8 1 Availability
Summary • Focus on total reliability - component failure rates and system faults • Increase production and revenue • Reduce operating costs • Grow the industry It All Depends on Reliability
James Maughan, GE Wind Wind Turbine Reliability Workshop p. 23