SlideShare a Scribd company logo
Comparing Remote Sensing
Systems to Met Towers
What You Can Learn From Vaisala’s Recent Study
Webinar, October 22, 2015
Lee Alnes Dr. Mark Stoelinga
#WindWebinar
q  This webinar will be available afterwards at
www.windpowerengineering.com & email
q  Q&A at the end of the presentation
q  Hashtag for this webinar: #WindWebinar
Before We Start
#WindWebinar
Michelle Froese
Senior Editor - Moderator
Windpower Engineering
& Development
Lee Alnes
Key Account Manager
Vaisala
Dr. Mark Stoelinga
Senior Scientist
Vaisala
Comparing Remote Sensing Systems to Met Towers
Meet the Speakers…
Page © Vaisala | www.vaisala.com/energy
Why Remote Sensing? Higher height data
10/27/154
Page © Vaisala | www.vaisala.com/energy
Why Remote Sensing? Additional advantages
10/27/155
•  Easy to install
•  No permitting
delays
•  Easy to move,
relocate, and
service
Page © Vaisala | www.vaisala.com/energy
Applications of Higher Height Data
10/27/156
Page © Vaisala | www.vaisala.com/energy
Accuracy
10/27/157
The words accuracy and uncertainty are sometimes interchanged…
Accuracy of measurement [means] ‘the closeness of the agreement between the result
of a measurement and a true value of the measurand’… Accuracy is a qualitative
concept.’ It can be high or low for example but strictly it should not be used quantitatively.
In practice, though, it is often used quantitatively…this unofficial definition breaks down
because it inherently assumes that a true value can be defined, known and realized
perfectly.
… Uncertainty of measurement acknowledges that no measurements can be perfect and is
defined as a '… parameter, associated with the result of a measurement, that
characterizes the dispersion of values that could reasonably be attributed to the
measurand.’
National Physical Laboratory (UK): http://www.npl.co.uk/reference/faqs/is-there-a-difference-between-'accuracy'-and-'uncertainty'-%28faq-length%29
Page © Vaisala | www.vaisala.com/energy
Uncertainty
10/27/158
Page © Vaisala | www.vaisala.com/energy
“P values” (P90, P50, . . .)
10/27/159
§ Probability of
exceedance
§ P50 – median value
§ P90 – 90% chance of
achieving at least this
value
P99 P90 P75 P50
Page © Vaisala | www.vaisala.com/energy
Correlation:
R and R2
R: correlation coefficient
R2: correlation squared,
a.k.a. coefficient of
determination.
It represents the fraction
of variance in the
y-variable “explained” by
the linear fit to the
x-variable.
10/27/1510
Remotesensorwindspeed(m-1s)
Tower wind speed (m-1s)
Page © Vaisala | www.vaisala.com/energy
Validation Studies
§ Who does them
§  Wind developers and operators
§  Remote sensing manufacturer
§  Research organizations
§ What they measure
§  Wind speed, direction, etc. à comparisons
§  Data recovery, up-time, power consumption, …
§ Not to be confused with
calibration
10/27/1511
Page © Vaisala | www.vaisala.com/energy
Ongoing Validation Studies
§ Triton® Wind Profiler has been independently evaluated and
verified for accuracy by:
§ Studies in progress – 2015
10/27/1512
China
Hydroelectric
Corporation
Page © Vaisala | www.vaisala.com/energy
Our Main Question
§ How does Triton uncertainty
compare to a met tower
system, in actual
commercial use?
10/27/1513
Page © Vaisala | www.vaisala.com/energy
Vaisala’s Unique Position
10/27/1514
MANY DEPLOYMENTS HAD
COLLOCATED TOWERS
MANY LEADING CUSTOMERS
VOLUNTEERED DATA FOR
A GLOBAL STUDY
IN-HOUSE WIND RESOURCE
ASSESSMENT EXPERTISE
2500+
TRITON LOCATIONS
IN 30+
COUNTRIES
Page © Vaisala | www.vaisala.com/energy
The Data Set
§  30 Triton / Tower data sets included in the study:
§  Triton data came from 24 different units manufactured between 2008 and 2014
§  A total of 100 correlation pairs (heights) were used
–  Shortest tower measurement height was 34m
–  Tallest tower measurement height was 120m
–  Average tower to Triton distance was 134m
–  Most Triton / tower elevation differences were less than 2m, and all were within 6m
§  18 of the data sets used Tritons with the original speaker array
§  12 of the data sets used Tritons with an improved speaker array that was released
in 2013
§  All Tritons were deployed as they were shipped from the factory with no special
modifications or equipment
10/27/1515
Page © Vaisala | www.vaisala.com/energy
Geographic Distribution
§ Well distributed
geographically
§ Exact locations not
shown to preserve
customer
confidentiality
10/27/1516
Page © Vaisala | www.vaisala.com/energy
Key Take-aways
§ Certainty: Triton has the same uncertainty on mean wind speed as a
well designed met tower—about 1% RMSE.
§  Not just in one experiment, but over 10’s of 1000’s of data-hours
§  Measuring with Triton cuts met tower shear extrapolation error in half
§ Repeatability: You can expect continued, repeatable performance
from one Triton to another
§ Longevity: Study included data collected over 6 years – with no
degradation due to age of unit
10/27/1517
Page © Vaisala | www.vaisala.com/energy
But what is the “Truth”?
10/27/1518
“TRUE” WIND SPEED
?
TOWER UNCERTAINTY SOURCES:
CALIBRATION | TURBULENCE &
OFF-HORIZONTAL FLOW | SENSOR
DEGRADATION | TOWER FLOW
DISTORTION | TO NAME A FEW!
Page © Vaisala | www.vaisala.com/energy
Conclusion: Wind speed
“When the mean wind speed differences at all 100 qualifying
anemometer measurement heights within the 30 Triton/met tower
pairs are aggregated, the average percent difference is +0.09%,
and the percent root mean-square of the differences is 1.27%.
This is consistent with an estimated uncertainty of the Triton of
approximately 1%, if the met tower measurement uncertainty is
assumed to be independent and approximately 1% as well, a
reasonable assumption for a large set of met towers maintained by
many different Triton users.”
10/27/1519
Page © Vaisala | www.vaisala.com/energy
Data Recovery
“All Tritons (both original units and those with the upgraded speaker
array, or “TPU” units) exhibit high data recovery (>=90%) up to 80m.
Data recovery for the newer TPU units is considerably improved
compared to that of original units at higher heights:
•  17% higher at 100 m,
•  47% higher at 140 m, and
•  106% higher at 180 m.”
10/27/1520
Page © Vaisala | www.vaisala.com/energy
Reducing Shear Extrapolation
Uncertainty
When mean winds directly measured by Triton are compared with
estimates sheared up from lower met tower heights, the Triton-
measured mean wind speeds exhibit uncertainties less than
half that of estimates sheared up from met towers.
10/27/1521
Page © Vaisala | www.vaisala.com/energy
Summary: Main Points
§ Remote sensing is in widespread use and
will continue to grow
§ Accuracy, uncertainty, calibration,
validation, correlation are frequently
misunderstood or misused
§ Vaisala Triton has been shown to have
equivalent uncertainty to met towers – all
over the world
10/27/1522
#WindWebinar
Michelle Froese
Senior Editor - Moderator
Windpower Engineering
& Development
@Windpower_Eng
Lee Alnes
Key Account Manager
Vaisala
lee.alnes@vaisala.com
Dr. Mark Stoelinga
Senior Scientist
Vaisala
mark.stoelinga@vaisala.com
Comparing Remote Sensing Systems to Met Towers
Questions?
#WindWebinar
Thank You
q  This webinar will be available at
www.windpowerengineering.com & email
q  Tweet with hashtag #WindWebinar
q  Connect with Windpower Engineering & Development
q  Discuss this on EngineeringExchange.com

More Related Content

PDF
Increase AEP by 2% With Improved Wind Measurement
PPTX
Power Performance Measurements: Cheaper Faster Better
PDF
WindBridge Advantage: Easy Anemometer Replacement for Any Turbine, Any Time
PDF
Cost Benefit Analysis and Argument in Favor of Replacement of Mechanical Turb...
PDF
Proactive Turbine Damage Reduction
PDF
Root Cause Analysis: Understand Why Electronic Parts Fail In Your Wind Turbine
PDF
New Methods for Accurately Detecting Lightning Strikes to Turbines
PDF
How to Reduce OpEx With Engineered Parts
Increase AEP by 2% With Improved Wind Measurement
Power Performance Measurements: Cheaper Faster Better
WindBridge Advantage: Easy Anemometer Replacement for Any Turbine, Any Time
Cost Benefit Analysis and Argument in Favor of Replacement of Mechanical Turb...
Proactive Turbine Damage Reduction
Root Cause Analysis: Understand Why Electronic Parts Fail In Your Wind Turbine
New Methods for Accurately Detecting Lightning Strikes to Turbines
How to Reduce OpEx With Engineered Parts

What's hot (20)

PDF
Blade Maintenance: Observations from the Field and Practical Solutions
PDF
Transient Wind Events and Their Effect on Drivetrain Loads
PDF
Leading Edge Erosion: Protection, Costs, and Benefits
PDF
New ideas for trimming O&M costs
PPT
Air Wtg Blade Inspection And Repair ( 2012 2015, A Division Of Canwindpower...
PDF
Lightning detection - strategies for monitoring & integrating into a blade ma...
PDF
Sandia 2014 Wind Turbine Blade Workshop- Newman
PDF
Aaron Bar - The Dynamic Wind Energy Market & the Impact on Blade Technology
PPTX
2014 Sandia Wind Turbine Blade Workshop- Griffith
PDF
2014 Wind Turbine Blade Workshop- Haag
PDF
Ground Based Inspection and Monitoring of Wind Turbine Blades
PDF
Sandia 2014 Wind Turbine Blade Workshop- Barr
PDF
2014 Wind Turbine Blade Workshop- Johansen
PDF
Optimizing Quality Assurance Inspections to Improve the Probability of Damage...
PDF
Sandia 2014 Wind Turbine Blade Workshop- Malkin
PDF
Sandia 2014 Wind Turbine Blade Workshop- Standish
PDF
Ben Rice - Blade RCA Processes and Inspection Methods at Younger Sites
PDF
David Maniaci - Leading Edge Erosion Measurement and Modeling Campaigns
PDF
John Schroeder - the Impact of Atmospheric Stability on Wind Structure and Tu...
Blade Maintenance: Observations from the Field and Practical Solutions
Transient Wind Events and Their Effect on Drivetrain Loads
Leading Edge Erosion: Protection, Costs, and Benefits
New ideas for trimming O&M costs
Air Wtg Blade Inspection And Repair ( 2012 2015, A Division Of Canwindpower...
Lightning detection - strategies for monitoring & integrating into a blade ma...
Sandia 2014 Wind Turbine Blade Workshop- Newman
Aaron Bar - The Dynamic Wind Energy Market & the Impact on Blade Technology
2014 Sandia Wind Turbine Blade Workshop- Griffith
2014 Wind Turbine Blade Workshop- Haag
Ground Based Inspection and Monitoring of Wind Turbine Blades
Sandia 2014 Wind Turbine Blade Workshop- Barr
2014 Wind Turbine Blade Workshop- Johansen
Optimizing Quality Assurance Inspections to Improve the Probability of Damage...
Sandia 2014 Wind Turbine Blade Workshop- Malkin
Sandia 2014 Wind Turbine Blade Workshop- Standish
Ben Rice - Blade RCA Processes and Inspection Methods at Younger Sites
David Maniaci - Leading Edge Erosion Measurement and Modeling Campaigns
John Schroeder - the Impact of Atmospheric Stability on Wind Structure and Tu...
Ad

Similar to Comparing Remote Sensing Systems to Met Towers (9)

PPT
Wind Energy Technology & Application of Remote Sensing
PDF
Campbell iain
PPTX
05 - IEC 61400-15 Update, Jason Fields (NREL).pptx
PDF
WindResourceAndSiteAssessment.pdf
PPTX
S4 oman wind energy the technology 2016
PDF
Derrick alan
PDF
Using remote sensing for accelerated wind development
PPTX
Advances in Wind Assessment Technology: Industry Pursuit of Higher Resource M...
PDF
Wind resource assessment on a complex terrain: Andhra Lake project - India
Wind Energy Technology & Application of Remote Sensing
Campbell iain
05 - IEC 61400-15 Update, Jason Fields (NREL).pptx
WindResourceAndSiteAssessment.pdf
S4 oman wind energy the technology 2016
Derrick alan
Using remote sensing for accelerated wind development
Advances in Wind Assessment Technology: Industry Pursuit of Higher Resource M...
Wind resource assessment on a complex terrain: Andhra Lake project - India
Ad

More from Windpower Engineering & Development (15)

PDF
Asset Management: Multiple Perspectives and Common Goals
PDF
Effective Ways to Prevent Wind Blade Erosion
PDF
Reducing Composite Defects in Blade Manufacturing
PDF
New Ideas, Methods and Materials for Improving Blade Repairs
PDF
Blade Assets – Extend Life and Reduce Risks with Proactive Maintenance
PDF
Keeping turbines and workers safe with lightning diverters and electric meters
PDF
Day-to-day condition monitoring for a large fleet of wind turbines
PDF
An Engineer’s Guide to OSHA’s New Recommendations for Arc Flash Studies
PDF
The Value of SCADA Infrastructure Virtualization on Wind Farms
PDF
Basics of Power Inverters
PPTX
Wind project site evaluations and their impact on development and longer-term...
PPTX
New Ideas for Repairing Gearboxes and Generators
PPTX
Wind Drivetrain Bearing Reliability
PPTX
Planning for Substation Maintenance and Reliability
PPTX
Compliance And Safety In Confined Spaces For The Windpower Industry
Asset Management: Multiple Perspectives and Common Goals
Effective Ways to Prevent Wind Blade Erosion
Reducing Composite Defects in Blade Manufacturing
New Ideas, Methods and Materials for Improving Blade Repairs
Blade Assets – Extend Life and Reduce Risks with Proactive Maintenance
Keeping turbines and workers safe with lightning diverters and electric meters
Day-to-day condition monitoring for a large fleet of wind turbines
An Engineer’s Guide to OSHA’s New Recommendations for Arc Flash Studies
The Value of SCADA Infrastructure Virtualization on Wind Farms
Basics of Power Inverters
Wind project site evaluations and their impact on development and longer-term...
New Ideas for Repairing Gearboxes and Generators
Wind Drivetrain Bearing Reliability
Planning for Substation Maintenance and Reliability
Compliance And Safety In Confined Spaces For The Windpower Industry

Recently uploaded (20)

PPTX
additive manufacturing of ss316l using mig welding
PDF
Mitigating Risks through Effective Management for Enhancing Organizational Pe...
PPTX
Fundamentals of Mechanical Engineering.pptx
PPTX
Internet of Things (IOT) - A guide to understanding
PDF
Enhancing Cyber Defense Against Zero-Day Attacks using Ensemble Neural Networks
PDF
R24 SURVEYING LAB MANUAL for civil enggi
PPT
introduction to datamining and warehousing
PPTX
FINAL REVIEW FOR COPD DIANOSIS FOR PULMONARY DISEASE.pptx
PDF
The CXO Playbook 2025 – Future-Ready Strategies for C-Suite Leaders Cerebrai...
PDF
Human-AI Collaboration: Balancing Agentic AI and Autonomy in Hybrid Systems
PPTX
Foundation to blockchain - A guide to Blockchain Tech
PPTX
Fundamentals of safety and accident prevention -final (1).pptx
PDF
keyrequirementskkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk
PPTX
6ME3A-Unit-II-Sensors and Actuators_Handouts.pptx
PDF
III.4.1.2_The_Space_Environment.p pdffdf
PDF
Level 2 – IBM Data and AI Fundamentals (1)_v1.1.PDF
PDF
Artificial Superintelligence (ASI) Alliance Vision Paper.pdf
PPTX
Engineering Ethics, Safety and Environment [Autosaved] (1).pptx
PPTX
Sustainable Sites - Green Building Construction
DOCX
ASol_English-Language-Literature-Set-1-27-02-2023-converted.docx
additive manufacturing of ss316l using mig welding
Mitigating Risks through Effective Management for Enhancing Organizational Pe...
Fundamentals of Mechanical Engineering.pptx
Internet of Things (IOT) - A guide to understanding
Enhancing Cyber Defense Against Zero-Day Attacks using Ensemble Neural Networks
R24 SURVEYING LAB MANUAL for civil enggi
introduction to datamining and warehousing
FINAL REVIEW FOR COPD DIANOSIS FOR PULMONARY DISEASE.pptx
The CXO Playbook 2025 – Future-Ready Strategies for C-Suite Leaders Cerebrai...
Human-AI Collaboration: Balancing Agentic AI and Autonomy in Hybrid Systems
Foundation to blockchain - A guide to Blockchain Tech
Fundamentals of safety and accident prevention -final (1).pptx
keyrequirementskkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk
6ME3A-Unit-II-Sensors and Actuators_Handouts.pptx
III.4.1.2_The_Space_Environment.p pdffdf
Level 2 – IBM Data and AI Fundamentals (1)_v1.1.PDF
Artificial Superintelligence (ASI) Alliance Vision Paper.pdf
Engineering Ethics, Safety and Environment [Autosaved] (1).pptx
Sustainable Sites - Green Building Construction
ASol_English-Language-Literature-Set-1-27-02-2023-converted.docx

Comparing Remote Sensing Systems to Met Towers

  • 1. Comparing Remote Sensing Systems to Met Towers What You Can Learn From Vaisala’s Recent Study Webinar, October 22, 2015 Lee Alnes Dr. Mark Stoelinga
  • 2. #WindWebinar q  This webinar will be available afterwards at www.windpowerengineering.com & email q  Q&A at the end of the presentation q  Hashtag for this webinar: #WindWebinar Before We Start
  • 3. #WindWebinar Michelle Froese Senior Editor - Moderator Windpower Engineering & Development Lee Alnes Key Account Manager Vaisala Dr. Mark Stoelinga Senior Scientist Vaisala Comparing Remote Sensing Systems to Met Towers Meet the Speakers…
  • 4. Page © Vaisala | www.vaisala.com/energy Why Remote Sensing? Higher height data 10/27/154
  • 5. Page © Vaisala | www.vaisala.com/energy Why Remote Sensing? Additional advantages 10/27/155 •  Easy to install •  No permitting delays •  Easy to move, relocate, and service
  • 6. Page © Vaisala | www.vaisala.com/energy Applications of Higher Height Data 10/27/156
  • 7. Page © Vaisala | www.vaisala.com/energy Accuracy 10/27/157 The words accuracy and uncertainty are sometimes interchanged… Accuracy of measurement [means] ‘the closeness of the agreement between the result of a measurement and a true value of the measurand’… Accuracy is a qualitative concept.’ It can be high or low for example but strictly it should not be used quantitatively. In practice, though, it is often used quantitatively…this unofficial definition breaks down because it inherently assumes that a true value can be defined, known and realized perfectly. … Uncertainty of measurement acknowledges that no measurements can be perfect and is defined as a '… parameter, associated with the result of a measurement, that characterizes the dispersion of values that could reasonably be attributed to the measurand.’ National Physical Laboratory (UK): http://www.npl.co.uk/reference/faqs/is-there-a-difference-between-'accuracy'-and-'uncertainty'-%28faq-length%29
  • 8. Page © Vaisala | www.vaisala.com/energy Uncertainty 10/27/158
  • 9. Page © Vaisala | www.vaisala.com/energy “P values” (P90, P50, . . .) 10/27/159 § Probability of exceedance § P50 – median value § P90 – 90% chance of achieving at least this value P99 P90 P75 P50
  • 10. Page © Vaisala | www.vaisala.com/energy Correlation: R and R2 R: correlation coefficient R2: correlation squared, a.k.a. coefficient of determination. It represents the fraction of variance in the y-variable “explained” by the linear fit to the x-variable. 10/27/1510 Remotesensorwindspeed(m-1s) Tower wind speed (m-1s)
  • 11. Page © Vaisala | www.vaisala.com/energy Validation Studies § Who does them §  Wind developers and operators §  Remote sensing manufacturer §  Research organizations § What they measure §  Wind speed, direction, etc. à comparisons §  Data recovery, up-time, power consumption, … § Not to be confused with calibration 10/27/1511
  • 12. Page © Vaisala | www.vaisala.com/energy Ongoing Validation Studies § Triton® Wind Profiler has been independently evaluated and verified for accuracy by: § Studies in progress – 2015 10/27/1512 China Hydroelectric Corporation
  • 13. Page © Vaisala | www.vaisala.com/energy Our Main Question § How does Triton uncertainty compare to a met tower system, in actual commercial use? 10/27/1513
  • 14. Page © Vaisala | www.vaisala.com/energy Vaisala’s Unique Position 10/27/1514 MANY DEPLOYMENTS HAD COLLOCATED TOWERS MANY LEADING CUSTOMERS VOLUNTEERED DATA FOR A GLOBAL STUDY IN-HOUSE WIND RESOURCE ASSESSMENT EXPERTISE 2500+ TRITON LOCATIONS IN 30+ COUNTRIES
  • 15. Page © Vaisala | www.vaisala.com/energy The Data Set §  30 Triton / Tower data sets included in the study: §  Triton data came from 24 different units manufactured between 2008 and 2014 §  A total of 100 correlation pairs (heights) were used –  Shortest tower measurement height was 34m –  Tallest tower measurement height was 120m –  Average tower to Triton distance was 134m –  Most Triton / tower elevation differences were less than 2m, and all were within 6m §  18 of the data sets used Tritons with the original speaker array §  12 of the data sets used Tritons with an improved speaker array that was released in 2013 §  All Tritons were deployed as they were shipped from the factory with no special modifications or equipment 10/27/1515
  • 16. Page © Vaisala | www.vaisala.com/energy Geographic Distribution § Well distributed geographically § Exact locations not shown to preserve customer confidentiality 10/27/1516
  • 17. Page © Vaisala | www.vaisala.com/energy Key Take-aways § Certainty: Triton has the same uncertainty on mean wind speed as a well designed met tower—about 1% RMSE. §  Not just in one experiment, but over 10’s of 1000’s of data-hours §  Measuring with Triton cuts met tower shear extrapolation error in half § Repeatability: You can expect continued, repeatable performance from one Triton to another § Longevity: Study included data collected over 6 years – with no degradation due to age of unit 10/27/1517
  • 18. Page © Vaisala | www.vaisala.com/energy But what is the “Truth”? 10/27/1518 “TRUE” WIND SPEED ? TOWER UNCERTAINTY SOURCES: CALIBRATION | TURBULENCE & OFF-HORIZONTAL FLOW | SENSOR DEGRADATION | TOWER FLOW DISTORTION | TO NAME A FEW!
  • 19. Page © Vaisala | www.vaisala.com/energy Conclusion: Wind speed “When the mean wind speed differences at all 100 qualifying anemometer measurement heights within the 30 Triton/met tower pairs are aggregated, the average percent difference is +0.09%, and the percent root mean-square of the differences is 1.27%. This is consistent with an estimated uncertainty of the Triton of approximately 1%, if the met tower measurement uncertainty is assumed to be independent and approximately 1% as well, a reasonable assumption for a large set of met towers maintained by many different Triton users.” 10/27/1519
  • 20. Page © Vaisala | www.vaisala.com/energy Data Recovery “All Tritons (both original units and those with the upgraded speaker array, or “TPU” units) exhibit high data recovery (>=90%) up to 80m. Data recovery for the newer TPU units is considerably improved compared to that of original units at higher heights: •  17% higher at 100 m, •  47% higher at 140 m, and •  106% higher at 180 m.” 10/27/1520
  • 21. Page © Vaisala | www.vaisala.com/energy Reducing Shear Extrapolation Uncertainty When mean winds directly measured by Triton are compared with estimates sheared up from lower met tower heights, the Triton- measured mean wind speeds exhibit uncertainties less than half that of estimates sheared up from met towers. 10/27/1521
  • 22. Page © Vaisala | www.vaisala.com/energy Summary: Main Points § Remote sensing is in widespread use and will continue to grow § Accuracy, uncertainty, calibration, validation, correlation are frequently misunderstood or misused § Vaisala Triton has been shown to have equivalent uncertainty to met towers – all over the world 10/27/1522
  • 23. #WindWebinar Michelle Froese Senior Editor - Moderator Windpower Engineering & Development @Windpower_Eng Lee Alnes Key Account Manager Vaisala lee.alnes@vaisala.com Dr. Mark Stoelinga Senior Scientist Vaisala mark.stoelinga@vaisala.com Comparing Remote Sensing Systems to Met Towers Questions?
  • 24. #WindWebinar Thank You q  This webinar will be available at www.windpowerengineering.com & email q  Tweet with hashtag #WindWebinar q  Connect with Windpower Engineering & Development q  Discuss this on EngineeringExchange.com