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Investigating the Efficacy of Floating LIDAR Motion CompensationAlgorithms for Offshore Wind Resource Assessment ApplicationsEuropean Wind Energy Conference, 14 March 2011
Daniel W. JaynesGL Garrad Hassan
Geographical reach
750 staff, in 41 locations, across 22 countries
VancouverOttawa
PortlandSan Diego
MontrealPeterborough
AustinMonterrey
Porto Alegre
BeijingTokyoShanghaiMumbaiBangaloreNewcastleMelbourneWellington
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CopenhagenHinnerupOldenburgHamburgPoland
Lisbon BarcelonaZaragozaMadrid
Imola
GlasgowLondon
Slough
Newly established in Chile, Egypt and South Korea
CO
NTE
NT
1. Introduction
4. Results
5. Conclusion
6. Future work and implications for the industry
2. Technology overview
3. Offshore LIDAR comparison campaign
Introduction
Challenges for offshore wind resource assessment:• Lack of existing sources of data for prospecting purposes• On-site data needed for projects requiring external finance• Fixed met masts pose a significant capital and logistical commitment for project
developers
LIDAR remote sensing devices have made significant advancements in the on-shore domain
• Demonstrated accuracy in simple terrain conditions [1], [2], [3]• Improved autonomous operational up-time since early deployments
In the offshore domain, LIDAR has been successfully deployed on fixed platforms [4]• Terrain no longer a threat, but waves, environment and lack of redundancy are concerns
What role will LIDAR remote sensing play in offshore wind resource measurement?
Technology Overview
LIDAR’s New Frontier: floating buoys equipped with LIDAR technology
Active players:• Natural Power – SeaZephIR• Leosphere – (planned)• Catch the Wind/AXYS – WindSentinel
• Catch The Wind Vindicator LIDAR• AXYS Technologies buoy platform
Motion compensation for dynamic motion of the offshore environment:• Vindicator: Roll, Pitch and Yaw compensation• Buoy: Heave and Translation compensation• Compensation for movement in 6 degrees of freedom
Device under consideration
X
Y
Z
Translation
Heave
Technology Overview: Motion Compensation Algorithms
• Each sensor and data acquisition device records independently• Sensors are synchronized with multiple on-board GPS systems• LIDAR logs data at a frequency of 1 Hz, buoy wave data are logged at 4 Hz
“TRIAXYS” Wave motion and ocean depth sensor
LIDAR: Wind data corrected for tilt & rotational motion
Buoy motion measurements
Data recovery
“WM 500” Buoy data controller
LIDAR measurements
Data for analysisPost-process
Data
Offshore LIDAR Comparison Campaign
Test Location:• Juan de Fuca Strait between the
Olympic Peninsula and Vancouver Island in October, 2009.
• Land LIDAR located 688 meters away from Buoy LIDAR
Reference data: Land-based VindicatorExperimental data: Buoy VindicatorMeasurement Period: 1 month
Measured Variables: • Wind speed/Direction: 100, 150 and 200 m• Wave height, direction• Air and water temperature• Barometric Pressure
Results
• Data filtering criteria: minimum “valid data count” > 300 for each 10-min period• Wind Speed Correlations demonstrate consistent results at all heights
Measurement Height [m]
Buoy Mean Wind Speed [m/s]
Land Mean Wind Speed [m/s]
Percent Difference Slope Offset R2
100 6.48 6.52 -0.60% 0.966 0.093 0.99150 6.57 6.66 -1.33% 0.967 0.161 0.99200 6.68 6.70 -0.34% 0.967 0.162 0.99
Results, ContinuedData Recovery:• Excellent operational uptime during the 1-month test campaign• Net data recovery:
Direction Correlation:
Reference (Land) LIDAR:
Experimental (Buoy) LIDAR:
Measurement Height [m]
Valid Data Records - Land LIDAR [ ]
Total Possible Records - Land LIDAR [ ]
Net Valid Data - Land LIDAR [%]
100 3782 4752 79.6%150 4031 4752 84.8%200 3870 4752 81.4%
Measurement Height [m]
Valid Data Records - Buoy LIDAR [ ]
Total Possible Records - Buoy LIDAR [ ]
Net Valid Data - Buoy LIDAR [%]
100 3207 4752 67.5%150 4160 4752 87.5%200 3589 4752 75.5%
Results, Continued
Error Investigation:• Error as a function of wind speed gradient:
• Wind speed gradient correlates poorly with measurement error• Wind speed gradient was generally low, as expected in offshore wind regimes
Results, Continued
Error Investigation:• Measurement error as a function of wave height
• Relatively low measurement error relative to land-based LIDAR during higher wave height events
• Maximum wave height: approx. 2 m• Average wave height: approx. 0.5 m
Color B
ar: Wind speed [m
/s]
Color B
ar: Wind speed [m
/s]
Color B
ar: Wind speed [m
/s]
Results, ContinuedMeasurement error as a function of “valid data count”• “Valid data count” is a proxy for signal return quality
Color Bar: W
ind speed [m/s]
• Data with valid data count less than 300 have been removed
Results, Concluded
Measurement error distribution:
Mean: -1.4%
Stdev: 8.3%Mean: -0.6%
Stdev: 12.3%
Mean: -0.7%
Stdev: 15.3%
• Scatter increases as a function of range, but mean agrees well with reference measurements.
ConclusionMeasurement campaign:
• Measurement error showed little relationship with a variety of different atmospheric and environmental phenomena, including wave height.
• Experimental (buoy LIDAR) mean wind speed agreed within 2% of the reference (land LIDAR) measurements.
• Given a 688 m separation of sensors, some degree of natural wind flow variation is to be expected.
• Results show promise, but the repeatability of demonstrated findings must be explored for longer periods of time.
Floating LIDAR: Implications for the Offshore Wind Energy Industry
• Floating LIDAR is considered relatively new and has not yet completed conclusive validation testing.
• GL GH have defined broad validation protocol for floating LIDAR devices.• The Current best practice approach is to measure the wind resource near the proposed
turbine hub-height with traditional, in-situ equipment (e.g. cup anemometers).
Conclusion
• Current outlook: A new, site-specific resource measurement option is now commercially available for preliminary assessment applications.
• Future outlook: If results can be shown to agree closely and consistently with accepted reference equipment, then the floating LIDAR system may be used for more formal applications in the future.
• Remote Sensing Limitations to remember:• Turbulence – Temporal and spatial averaging effects of LIDAR measurements• Power supply and redundancy – Risk of data loss exists, but onboard power system
redundancies may help mitigate this risk (further testing to demonstrate potential)• Extreme wind speeds – Volume versus point measurement dichotomies
Comparisons against fixed met mast measurements are planned for the near future with commercial partners.
Thank you
Daniel JaynesGL Garrad [email protected]+1 (503) 222 5590 Ext. 109
References[1] Jaynes, D., LIDAR Validation and Recommendations for Wind Resource Assessments. AWEA WINDPOWER, May 2009.[2] Albers, A., Evaluation of ZephIR. 2006, Deutsche WindGuard Consulting GmbH.[3] Jorgensen, H., T. Mikkelsen, J. Mann, D. Bryce, A. Coffey, M. Harris, and D.A. Smith. Site Wind Field Determination Using a CW Doppler Lidar-
Comparison with Cup Anemometers at Riso. in The Science of Making Torque from Wind. 2004. Delft.[4] Kindler, D., Oldroyd, A., MacAskill, A., Finch, D., An 8 Month Test Campaign of the QinetiQ ZephIR System: Preliminary Results,
Meteorologische Zeitschrift, October 2007.