Sandia National Laboratories is a multi-program laboratory managed by Sandia Corporation for the US Department of Energy’s National Nuclear Security Administration. The document summarizes a panel discussion on establishing successful blade maintenance programs. It discusses the DOE Wind Program mission to increase wind energy deployment through technology development. It also outlines Sandia's objectives in serving the nation's needs through rotor systems innovation, innovative wind plant technologies and operations experimentation, quantification and reduction of uncertainties in wind plant modeling, and research on wind power performance, reliability and safety.

1. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin
Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000. SAND NO. 2011-XXXXP
Panel: Establishing a Successful Blade Maintenance Prog.
• Josh Crayton, Director of Business Development, Rope Partner
• Jeffrey Hammit, Principal Technical Specialist, NextEra Energy
• Olen Richardson, President, Blade Consultant Services
• Gary Kanaby, Commercial Manager, WindCom
• Chair: Carsten Westergaard, Senior Advisor (Cont), Wind & water, Sandia National Laboratories
(track 354440 and dept. side)

2. DOE Wind Program Mission
§ We seek to increase the deployment of wind energy systems
to offset carbon in the atmosphere and increase domestic
energy product by technology development guided by
Department of Energy Wind Vision towards 35% wind
penetration in 2050:
Ø Wind Power Resources and Site Characterization
Ø Wind Plant Technology Advancement
Ø Supply Chain, Manufacturing and Logistics
Ø Wind Power Performance, Reliability, and Safety
Ø Wind Electricity Delivery and Integration
Ø Wind Siting and Permitting
Ø Collaboration, Education, and Outreach
Ø Workforce Development
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3. Wind Energy Technologies Objective
§ Serve the nations needs by providing world leading expertise:
1. Rotor Systems Innovation
2. Innovative Experimentation in Wind Plant Technologies and
Operations
3. Quantification and Reduction of Uncertainties in Wind Plant
Modeling and Experimentation
4. Wind Power Performance, Reliability, and Safety
§ We accomplish our open-innovation goals through a nation-
leading engineering team, leveraging the broader capabilities
of the largest US engineering laboratory and utilizing the most
advanced and well documented wind plant research facility in
the world
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4. Why Innovative Rotors?
§ Single largest driver of LCOE reduction
§ Longer blades both reduce LCOE and
allow for increased deployment
§ Larger blades require breakthroughs in:
§ Modular/on-Site Construction
§ Load control
§ Low-cost high strength fiber
§ Manufacturing quality
§ Improvements to design standards are
needed for low wind-speed rotors
§ Taller towers enable longer blades
§ New opportunities may be found in
blade innovation combined with wind
plant controls
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5. Unplanned Reliability
Events Estimates
5
Preliminary crude numbers misc. collected data from
workgroups,discussion and presentations
Lifetime cost per
turbine
(not including
scheduled
maintenance)
Annual failure rate
of repairable items
Fraction of fleet
which will
experience major
replacement in
lifetime (20 yrs)
Blade $ 150,400 16% 14%
Gear + bearing $ 189,200 6% 42%
Generator $ 112,200 3% 25%
Other $ 44,120 39%
Forced outage /
resets $ 20,645
Total over 20 yrs $ 516,565*
Here of replacement $ 330,600
Unscheduled cost $ 5.1 per MWh
Unplanned reliability cost, 2 MW @ 98% availability

6. An insurance company’s perspective
§ Observations:
§ Cost is known
§ High level root cause is
known, sometimes at a
lower level
§ Limited technical data on
fundamental mechanisms
§ Environment plays an
important role
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Source: GCube,
September 2014

7. Environmentally induced
Reliability Events
7
The data on environmental is not quantified atthis stage
and are fleet average.I.e. icing is a regional effect
Failure induced by Component Annual failure rate of
repairable items
(number is relative to all
component repairs)
Fraction of fleet which will
experience major
replacement in lifetime
(20 years)
Lightning
35 days/year
Blade 3% 4%
Ice Blade ? ?
Erosion Blade High Almost none
Extreme wind w/wo
vibration
Blade ? 6%
Corrosion and surface
degradation
Blade ? ?
Misc. Blade ? 4%

8. Requirements To Determine
The Benefit Of Pro-active Vs Reactive ?
§ Measure and benchmark
§ How do we accurately created technical reliability data for blades ?
§ Is it meaningful to correlate technical and economical benchmarks ?
§ Can we use historical data to predict the future ?
§ How do we quantify the actual operating envelope and include:
§ Environmental conditions ?
§ Operational conditions ?
§ Data-mining SCADA data for both performance and reliability ?
§ Do we need to have new sensors in the blade ?
§ Can these data be used to control and/or extended the life time of a wind farm
(remaining useful life) ?
§ Field actions
§ Do we need better field inspections and follow-up methods ?
§ Effects of structural improvements or repairs ?
§ Data collection ?
§ Verification of the starting point
§ Can field data be used to improve future design ?
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9. Example of discrete series of events which
will bias an average unreasonably
§ Specific turbine in MW class has a
peak of blade challenge(s) in 2011.
Level normalized in 2013 and
disappeared in 2014
§ Relative young turbines, presumably
an issue of proprietary nature
§ Blade may only be inspected every 2nd
or 3rd year
§ Sub-conclusion:
§ Including discrete (infancy) events
will not support conclusion of
future performance
§ Inspection methods and
frequency will bias inter-annual
results
9
Graph source: Coffey (2014)

10. Lightning: Regional Risk Variation
§ Technical report IEC/TR 61400-24:2002
§ Based on data from 2800 “small turbines”
in Northern EU (<15 days) and inner
Germany (<35 days of thunderstorms)
§ Annual failure rate 0.4 to 1.4%
§ US has 5 to 100 days of thunderstorms,
0.3% to 5%
§ Midwest has ~55 day with an annual
failure rate up to 3%
§ Sub-conclusion:
§ Fleet average without considering regional exposure is fairly meaning less
§ A well document standard for normalization combined with a national
fleet average could improve our understanding
§ Many US owners, has more accumulate experience than what the IEC
standard is based upon
10
NOAA.gov, Cannata (2014), Coffery
(2014), Nissam (2013), LM Wind power

11. Lightning: Technology bias and Size bias
§ Lightning risk, according to IEC61400-24 and
ported from building code, suggest risk is
proportional to height squared, including
landscape topology
§ Experience shows much higher height
dependency ~R4
§ On the flip side, improved LPS systems are
reported to have as much as an X10
improvement
§ Sub-conclusion: Historical fleet average will
not predict the future without significant
considerations to technology and size
correction
11
Cannata (2014), Coffery (2014), Green
(2014), LM Wind power
Proportional to ~R4

12. Observations and Tagging
§ Gear and generators have received a
lot of attention
§ Blades are receiving more attention.
More frequent inspections are
increasing awareness
§ Lack of common naming and tagging
makes direct comparison of
technical data difficult
§ New CREW objectives is to develop a
common platform through an
auditing process and aggregate
these into a high level benchmark
with more data
12

13. A controlled
connection between
rotor and rotor wake
impact every aspect
of the plant
Rotor systems innovation
§ The wind plant rotors controls and drives every aspect of a successful
wind plant operation. Advanced rotor concepts offer great potential for
improving reliable wind plant performance while reducing energy costs
13
§ Innovative rotors flown at the National Rotor Testbed hosted at DOE/
Sandia SWiFT wind plant offers elaborate verification and validation a real
and controlled wind plant environment, documented to reduce
experimental uncertainties and thereby the reliability of the results

14. Innovative Experimentation in Wind
Plant Technologies and Operations
§ Ensuring site suitability, reproducibility, resilience and
efficiency of wind plants to produce usable electric power
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Variability
Tower vibration
§ Wind plant controllers
§ Cyber security and micro grid
integration
§ Development and testing of
new flow diagnostics with
commercial partners
§ Data analytics (SCADA) wind
plant performance and
reliability analysis
§ Environmental impact, such as
radar interference mitigation

15. Quantification and Reduction of Uncertainties in
Wind Plant Modeling and Experimentation
§ High-fidelity modeling of wind plants for reduction
in COE
§ Accurate prediction of wind plant power production
§ Understanding of complex aero-structural load scenarios
§ Leveraging of nuclear weapons simulation
technology
§ Sandia’s Nalu computational fluid dynamics code has been
chosen as a DOE exascale application code for exploitation of
next-generation supercomputing hardware.
§ Nalu is an open source code born from the Fuego code, used
originally for abnormal fire environments for stockpile
systems.
§ Validation
§ Validation experiments to be performed at SWiFT and other
facilities
§ Quantified uncertainty bounds on simulation predictions -
needed for decisions impacting wind plant design and
financing
15
A Nalu simulation of stalled flow past a wind
turbine blade section, performed in the DOE
Trinity supercomputer.

16. Wind Power Performance,
Reliability, and Safety
§ Sandia leads efforts in wind turbine reliability research, specifically
focusing on:
§ Nondestructive inspection
§ Effects of blade defects
§ Composite materials research
§ Structural health monitoring
§ Structure dynamics
§ Wind loading
§ Full system hazard and root cause analysis
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17. Innovative wind plant technology
platform for the future industry partnerships
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Short term program goals:
§ Reduce turbine-turbine inter-
action and wind plant under-
performance
§ Develop advanced wind
turbine rotors
§ Public, open-source in order to
produce high accurate results
supporting development of
advanced simulations
Facilities:
§ Three variable-speed variable-pitch modified wind turbines with
full power conversion and extensive sensor suite
§ Two heavily instrumented in-flow meteorological towers
§ Massive LIDAR instrumentation (in the pipeline)
§ Site-wide time-synchronized data collection
§ Home to the National Rotor Testbed

18. Thank you!
This work is supported by the US DOE EERE Wind and Water Power Program.