Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification...

28
© 2019 Fraunhofer IWES 1 © 2019 Fraunhofer IWES 1 © 2019 Fraunhofer IWES 1 G. Wolken-Möhlmann, J. Gottschall Dependence of Floating LiDAR Performance on External Parameters – Are existing onshore classification methods Applicable? EERA Deepwind 2020, Trondheim, 15-17 Jan 2020

Transcript of Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification...

Page 1: Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification are important for the commercial acceptance of FLS Both IWES FLS using Windcube

copy 2019 Fraunhofer IWES 1copy 2019 Fraunhofer IWES 1copy 2019 Fraunhofer IWES 1

G Wolken-Moumlhlmann J Gottschall

Dependence of Floating LiDAR Performance on External

Parameters ndash Are existing onshore classification

methods Applicable

EERA Deepwind 2020 Trondheim 15-17 Jan 2020

2copy 2019 Fraunhofer IWES 2copy 2019 Fraunhofer IWES 2copy 2019 Fraunhofer IWES

Introduction

FLS verification vs class ification

Case Study Fraunhofer IWES LiDAR Buoy

Resume

Outline

3copy 2019 Fraunhofer IWES 3copy 2019 Fraunhofer IWES 3copy 2019 Fraunhofer IWES

Introduction

Floating LiDAR Systems (FLS) Commercially available since 2010 Several providers for systems or

measurements number growing FLS can replace offshore

meteorological masts for site assessment power curve measurements etchellip

From Gottschall et al Floating lidar as an advanced offshore wind speedmeasurement technique WIREs Energy and Environment 2017 [1]

4copy 2019 Fraunhofer IWES 4copy 2019 Fraunhofer IWES 4copy 2019 Fraunhofer IWES

Introduction

Technology Applications

Wind resource assessment (WRA)

Power curve measurements

hellip

5copy 2019 Fraunhofer IWES 5copy 2019 Fraunhofer IWES 5copy 2019 Fraunhofer IWES

Introduction

Technology Applications

Wind resource assessment (WRA)

Power curve measurements

hellip

Standards Recommended

practices

VerificationClass ification

[5]

[234]

6copy 2019 Fraunhofer IWES 6copy 2019 Fraunhofer IWES 6copy 2019 Fraunhofer IWES

Introduction

Technology Applications

Wind resource assessment (WRA)

Power curve measurements

hellip

Standards Recommended

practices

VerificationClass ification

[5]

[234]

7copy 2019 Fraunhofer IWES 7copy 2019 Fraunhofer IWES 7copy 2019 Fraunhofer IWES

FLS verification vs classificationVerification Class ification

For a distinct system For selected conditions short term measurement ~1 month

For a FLS type Correlation WSP deviation and

independent variable At least 3 months measurement

hellip

8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES

Fraunhofer IWES LiDAR Buoy

System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator

batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t

9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES

Fraunhofer IWES LiDAR Buoy

System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator

batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t

Analysed Measurements (exceeding 6 months 2016) LiDAR Buoy at FINO3 (Windcube) LiDAR Buoy at FINO1 (ZephIR)

10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES

Verification

Comparison of FLS wind speed and wind direction compared to reference

11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES

Verification

Comparison of FLS wind speed and wind direction compared to reference-gt Key parameter (slope and Rsup2) exceed Best Practice requirements

12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES

Classification ndash Environmental Variables

Wind speed deviation (FLS -Reference) vs environmental variables (EV)

Wind speed Wind direction Wind shear Wind veer Temperature and

temperature difference Air density hellip

Meteorological variables (defined in IEC 64100-12-1)

13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES

Classification ndash Environmental Variables

Wind speed deviation (FLS -Reference) vs environmental variables (EV)

Wind speed Wind direction Wind shear Wind veer Temperature and

temperature difference Air density hellip

Meteorological variables (defined in IEC 64100-12-1) Oceanographic variables

Wave height Wave period Water level Currents hellip

Tilting Yawing Heave Translation hellip

Platform motion variables

14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear (example)

15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear

FLS is sensitive for independent variable if

- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01

16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height Hs

17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height HsSensitive Not sensitive

18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

Are the variables independent correlations

19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable

See Barker Et al [6]

Considering

shear

no

no

yes

no

no

no

20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

Oceanographic variables and motion variable (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR2

Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no

Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no

Current 0096 -1382 -0133 0002 -0006 no

Heave range 0570 -0219 -0125 0000 -0002 no

Tilt Range 3811 0027 0105 0000 0001 no

Yaw increment range 8559 -0008 -0069 0002 -0003 no

Static tilt 0473 -0387 -0183 0002 -0008 no

- No sensitivities for oceanographic or platform motion variables

21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES

Classification ndash Final classification

Classification results for FINO1 campaign

-gt Most uncertainty comes from reference measurement uncertainty

22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES

Classification ndash Results

FLS (Windcube) FLS (ZXZephIR) 100m

For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified

Independant variableSensitivity

m x std

Sensitivit

y x RSensitive

[-] [] []

Significant wave height (buoy) -0101 -0001 no

Peak period Tp (Buoy) 0059 0001 no

Current -0133 -0006 no

Heave range -0125 -0002 no

Tilt Range 0105 0001 no

Yaw increment range -0069 -0003 no

Static tilt -0183 -0008 no

Independant variableSensitivity

m x std

Sensitivity

x rSensitive

[-] [] []

Significant wave height -0063 -0001 no

Peak period Tp (Buoy) -0191 -0007 no

Tm02 (radar) 0013 0000 no

Waterlevel -0069 0000 no

Heave range -0118 -0002 no

Tilt Range 0078 0000 no

Yaw increment range -0054 -0001 no

Static tilt 0075 0002 no

23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

Meteorological variables

FLS set-up

Oceanographic variables

Motion variables

Measurement

25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES

Resume

Verification and classification are important for the commercial acceptance of FLS

Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables

Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast

26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES

Federal Republic of Germany

Federal Ministry for Economic Affairs and Energy

Federal Ministry of Education and Research

European Regional Development Fund (ERDF)

Federal State of Bremen

Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH

Federal State of Lower Saxony

Free and Hanseatic City of Hamburg

Acknowledgements

Fraunhofer IWES is funded by

The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)

27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES

Thanks a lot for your attention

gerritwolken-moehlmanniwesfraunhoferde

28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES

References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain

Page 2: Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification are important for the commercial acceptance of FLS Both IWES FLS using Windcube

2copy 2019 Fraunhofer IWES 2copy 2019 Fraunhofer IWES 2copy 2019 Fraunhofer IWES

Introduction

FLS verification vs class ification

Case Study Fraunhofer IWES LiDAR Buoy

Resume

Outline

3copy 2019 Fraunhofer IWES 3copy 2019 Fraunhofer IWES 3copy 2019 Fraunhofer IWES

Introduction

Floating LiDAR Systems (FLS) Commercially available since 2010 Several providers for systems or

measurements number growing FLS can replace offshore

meteorological masts for site assessment power curve measurements etchellip

From Gottschall et al Floating lidar as an advanced offshore wind speedmeasurement technique WIREs Energy and Environment 2017 [1]

4copy 2019 Fraunhofer IWES 4copy 2019 Fraunhofer IWES 4copy 2019 Fraunhofer IWES

Introduction

Technology Applications

Wind resource assessment (WRA)

Power curve measurements

hellip

5copy 2019 Fraunhofer IWES 5copy 2019 Fraunhofer IWES 5copy 2019 Fraunhofer IWES

Introduction

Technology Applications

Wind resource assessment (WRA)

Power curve measurements

hellip

Standards Recommended

practices

VerificationClass ification

[5]

[234]

6copy 2019 Fraunhofer IWES 6copy 2019 Fraunhofer IWES 6copy 2019 Fraunhofer IWES

Introduction

Technology Applications

Wind resource assessment (WRA)

Power curve measurements

hellip

Standards Recommended

practices

VerificationClass ification

[5]

[234]

7copy 2019 Fraunhofer IWES 7copy 2019 Fraunhofer IWES 7copy 2019 Fraunhofer IWES

FLS verification vs classificationVerification Class ification

For a distinct system For selected conditions short term measurement ~1 month

For a FLS type Correlation WSP deviation and

independent variable At least 3 months measurement

hellip

8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES

Fraunhofer IWES LiDAR Buoy

System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator

batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t

9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES

Fraunhofer IWES LiDAR Buoy

System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator

batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t

Analysed Measurements (exceeding 6 months 2016) LiDAR Buoy at FINO3 (Windcube) LiDAR Buoy at FINO1 (ZephIR)

10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES

Verification

Comparison of FLS wind speed and wind direction compared to reference

11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES

Verification

Comparison of FLS wind speed and wind direction compared to reference-gt Key parameter (slope and Rsup2) exceed Best Practice requirements

12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES

Classification ndash Environmental Variables

Wind speed deviation (FLS -Reference) vs environmental variables (EV)

Wind speed Wind direction Wind shear Wind veer Temperature and

temperature difference Air density hellip

Meteorological variables (defined in IEC 64100-12-1)

13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES

Classification ndash Environmental Variables

Wind speed deviation (FLS -Reference) vs environmental variables (EV)

Wind speed Wind direction Wind shear Wind veer Temperature and

temperature difference Air density hellip

Meteorological variables (defined in IEC 64100-12-1) Oceanographic variables

Wave height Wave period Water level Currents hellip

Tilting Yawing Heave Translation hellip

Platform motion variables

14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear (example)

15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear

FLS is sensitive for independent variable if

- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01

16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height Hs

17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height HsSensitive Not sensitive

18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

Are the variables independent correlations

19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable

See Barker Et al [6]

Considering

shear

no

no

yes

no

no

no

20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

Oceanographic variables and motion variable (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR2

Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no

Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no

Current 0096 -1382 -0133 0002 -0006 no

Heave range 0570 -0219 -0125 0000 -0002 no

Tilt Range 3811 0027 0105 0000 0001 no

Yaw increment range 8559 -0008 -0069 0002 -0003 no

Static tilt 0473 -0387 -0183 0002 -0008 no

- No sensitivities for oceanographic or platform motion variables

21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES

Classification ndash Final classification

Classification results for FINO1 campaign

-gt Most uncertainty comes from reference measurement uncertainty

22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES

Classification ndash Results

FLS (Windcube) FLS (ZXZephIR) 100m

For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified

Independant variableSensitivity

m x std

Sensitivit

y x RSensitive

[-] [] []

Significant wave height (buoy) -0101 -0001 no

Peak period Tp (Buoy) 0059 0001 no

Current -0133 -0006 no

Heave range -0125 -0002 no

Tilt Range 0105 0001 no

Yaw increment range -0069 -0003 no

Static tilt -0183 -0008 no

Independant variableSensitivity

m x std

Sensitivity

x rSensitive

[-] [] []

Significant wave height -0063 -0001 no

Peak period Tp (Buoy) -0191 -0007 no

Tm02 (radar) 0013 0000 no

Waterlevel -0069 0000 no

Heave range -0118 -0002 no

Tilt Range 0078 0000 no

Yaw increment range -0054 -0001 no

Static tilt 0075 0002 no

23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

Meteorological variables

FLS set-up

Oceanographic variables

Motion variables

Measurement

25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES

Resume

Verification and classification are important for the commercial acceptance of FLS

Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables

Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast

26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES

Federal Republic of Germany

Federal Ministry for Economic Affairs and Energy

Federal Ministry of Education and Research

European Regional Development Fund (ERDF)

Federal State of Bremen

Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH

Federal State of Lower Saxony

Free and Hanseatic City of Hamburg

Acknowledgements

Fraunhofer IWES is funded by

The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)

27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES

Thanks a lot for your attention

gerritwolken-moehlmanniwesfraunhoferde

28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES

References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain

Page 3: Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification are important for the commercial acceptance of FLS Both IWES FLS using Windcube

3copy 2019 Fraunhofer IWES 3copy 2019 Fraunhofer IWES 3copy 2019 Fraunhofer IWES

Introduction

Floating LiDAR Systems (FLS) Commercially available since 2010 Several providers for systems or

measurements number growing FLS can replace offshore

meteorological masts for site assessment power curve measurements etchellip

From Gottschall et al Floating lidar as an advanced offshore wind speedmeasurement technique WIREs Energy and Environment 2017 [1]

4copy 2019 Fraunhofer IWES 4copy 2019 Fraunhofer IWES 4copy 2019 Fraunhofer IWES

Introduction

Technology Applications

Wind resource assessment (WRA)

Power curve measurements

hellip

5copy 2019 Fraunhofer IWES 5copy 2019 Fraunhofer IWES 5copy 2019 Fraunhofer IWES

Introduction

Technology Applications

Wind resource assessment (WRA)

Power curve measurements

hellip

Standards Recommended

practices

VerificationClass ification

[5]

[234]

6copy 2019 Fraunhofer IWES 6copy 2019 Fraunhofer IWES 6copy 2019 Fraunhofer IWES

Introduction

Technology Applications

Wind resource assessment (WRA)

Power curve measurements

hellip

Standards Recommended

practices

VerificationClass ification

[5]

[234]

7copy 2019 Fraunhofer IWES 7copy 2019 Fraunhofer IWES 7copy 2019 Fraunhofer IWES

FLS verification vs classificationVerification Class ification

For a distinct system For selected conditions short term measurement ~1 month

For a FLS type Correlation WSP deviation and

independent variable At least 3 months measurement

hellip

8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES

Fraunhofer IWES LiDAR Buoy

System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator

batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t

9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES

Fraunhofer IWES LiDAR Buoy

System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator

batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t

Analysed Measurements (exceeding 6 months 2016) LiDAR Buoy at FINO3 (Windcube) LiDAR Buoy at FINO1 (ZephIR)

10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES

Verification

Comparison of FLS wind speed and wind direction compared to reference

11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES

Verification

Comparison of FLS wind speed and wind direction compared to reference-gt Key parameter (slope and Rsup2) exceed Best Practice requirements

12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES

Classification ndash Environmental Variables

Wind speed deviation (FLS -Reference) vs environmental variables (EV)

Wind speed Wind direction Wind shear Wind veer Temperature and

temperature difference Air density hellip

Meteorological variables (defined in IEC 64100-12-1)

13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES

Classification ndash Environmental Variables

Wind speed deviation (FLS -Reference) vs environmental variables (EV)

Wind speed Wind direction Wind shear Wind veer Temperature and

temperature difference Air density hellip

Meteorological variables (defined in IEC 64100-12-1) Oceanographic variables

Wave height Wave period Water level Currents hellip

Tilting Yawing Heave Translation hellip

Platform motion variables

14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear (example)

15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear

FLS is sensitive for independent variable if

- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01

16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height Hs

17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height HsSensitive Not sensitive

18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

Are the variables independent correlations

19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable

See Barker Et al [6]

Considering

shear

no

no

yes

no

no

no

20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

Oceanographic variables and motion variable (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR2

Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no

Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no

Current 0096 -1382 -0133 0002 -0006 no

Heave range 0570 -0219 -0125 0000 -0002 no

Tilt Range 3811 0027 0105 0000 0001 no

Yaw increment range 8559 -0008 -0069 0002 -0003 no

Static tilt 0473 -0387 -0183 0002 -0008 no

- No sensitivities for oceanographic or platform motion variables

21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES

Classification ndash Final classification

Classification results for FINO1 campaign

-gt Most uncertainty comes from reference measurement uncertainty

22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES

Classification ndash Results

FLS (Windcube) FLS (ZXZephIR) 100m

For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified

Independant variableSensitivity

m x std

Sensitivit

y x RSensitive

[-] [] []

Significant wave height (buoy) -0101 -0001 no

Peak period Tp (Buoy) 0059 0001 no

Current -0133 -0006 no

Heave range -0125 -0002 no

Tilt Range 0105 0001 no

Yaw increment range -0069 -0003 no

Static tilt -0183 -0008 no

Independant variableSensitivity

m x std

Sensitivity

x rSensitive

[-] [] []

Significant wave height -0063 -0001 no

Peak period Tp (Buoy) -0191 -0007 no

Tm02 (radar) 0013 0000 no

Waterlevel -0069 0000 no

Heave range -0118 -0002 no

Tilt Range 0078 0000 no

Yaw increment range -0054 -0001 no

Static tilt 0075 0002 no

23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

Meteorological variables

FLS set-up

Oceanographic variables

Motion variables

Measurement

25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES

Resume

Verification and classification are important for the commercial acceptance of FLS

Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables

Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast

26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES

Federal Republic of Germany

Federal Ministry for Economic Affairs and Energy

Federal Ministry of Education and Research

European Regional Development Fund (ERDF)

Federal State of Bremen

Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH

Federal State of Lower Saxony

Free and Hanseatic City of Hamburg

Acknowledgements

Fraunhofer IWES is funded by

The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)

27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES

Thanks a lot for your attention

gerritwolken-moehlmanniwesfraunhoferde

28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES

References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain

Page 4: Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification are important for the commercial acceptance of FLS Both IWES FLS using Windcube

4copy 2019 Fraunhofer IWES 4copy 2019 Fraunhofer IWES 4copy 2019 Fraunhofer IWES

Introduction

Technology Applications

Wind resource assessment (WRA)

Power curve measurements

hellip

5copy 2019 Fraunhofer IWES 5copy 2019 Fraunhofer IWES 5copy 2019 Fraunhofer IWES

Introduction

Technology Applications

Wind resource assessment (WRA)

Power curve measurements

hellip

Standards Recommended

practices

VerificationClass ification

[5]

[234]

6copy 2019 Fraunhofer IWES 6copy 2019 Fraunhofer IWES 6copy 2019 Fraunhofer IWES

Introduction

Technology Applications

Wind resource assessment (WRA)

Power curve measurements

hellip

Standards Recommended

practices

VerificationClass ification

[5]

[234]

7copy 2019 Fraunhofer IWES 7copy 2019 Fraunhofer IWES 7copy 2019 Fraunhofer IWES

FLS verification vs classificationVerification Class ification

For a distinct system For selected conditions short term measurement ~1 month

For a FLS type Correlation WSP deviation and

independent variable At least 3 months measurement

hellip

8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES

Fraunhofer IWES LiDAR Buoy

System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator

batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t

9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES

Fraunhofer IWES LiDAR Buoy

System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator

batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t

Analysed Measurements (exceeding 6 months 2016) LiDAR Buoy at FINO3 (Windcube) LiDAR Buoy at FINO1 (ZephIR)

10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES

Verification

Comparison of FLS wind speed and wind direction compared to reference

11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES

Verification

Comparison of FLS wind speed and wind direction compared to reference-gt Key parameter (slope and Rsup2) exceed Best Practice requirements

12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES

Classification ndash Environmental Variables

Wind speed deviation (FLS -Reference) vs environmental variables (EV)

Wind speed Wind direction Wind shear Wind veer Temperature and

temperature difference Air density hellip

Meteorological variables (defined in IEC 64100-12-1)

13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES

Classification ndash Environmental Variables

Wind speed deviation (FLS -Reference) vs environmental variables (EV)

Wind speed Wind direction Wind shear Wind veer Temperature and

temperature difference Air density hellip

Meteorological variables (defined in IEC 64100-12-1) Oceanographic variables

Wave height Wave period Water level Currents hellip

Tilting Yawing Heave Translation hellip

Platform motion variables

14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear (example)

15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear

FLS is sensitive for independent variable if

- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01

16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height Hs

17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height HsSensitive Not sensitive

18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

Are the variables independent correlations

19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable

See Barker Et al [6]

Considering

shear

no

no

yes

no

no

no

20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

Oceanographic variables and motion variable (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR2

Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no

Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no

Current 0096 -1382 -0133 0002 -0006 no

Heave range 0570 -0219 -0125 0000 -0002 no

Tilt Range 3811 0027 0105 0000 0001 no

Yaw increment range 8559 -0008 -0069 0002 -0003 no

Static tilt 0473 -0387 -0183 0002 -0008 no

- No sensitivities for oceanographic or platform motion variables

21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES

Classification ndash Final classification

Classification results for FINO1 campaign

-gt Most uncertainty comes from reference measurement uncertainty

22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES

Classification ndash Results

FLS (Windcube) FLS (ZXZephIR) 100m

For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified

Independant variableSensitivity

m x std

Sensitivit

y x RSensitive

[-] [] []

Significant wave height (buoy) -0101 -0001 no

Peak period Tp (Buoy) 0059 0001 no

Current -0133 -0006 no

Heave range -0125 -0002 no

Tilt Range 0105 0001 no

Yaw increment range -0069 -0003 no

Static tilt -0183 -0008 no

Independant variableSensitivity

m x std

Sensitivity

x rSensitive

[-] [] []

Significant wave height -0063 -0001 no

Peak period Tp (Buoy) -0191 -0007 no

Tm02 (radar) 0013 0000 no

Waterlevel -0069 0000 no

Heave range -0118 -0002 no

Tilt Range 0078 0000 no

Yaw increment range -0054 -0001 no

Static tilt 0075 0002 no

23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

Meteorological variables

FLS set-up

Oceanographic variables

Motion variables

Measurement

25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES

Resume

Verification and classification are important for the commercial acceptance of FLS

Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables

Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast

26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES

Federal Republic of Germany

Federal Ministry for Economic Affairs and Energy

Federal Ministry of Education and Research

European Regional Development Fund (ERDF)

Federal State of Bremen

Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH

Federal State of Lower Saxony

Free and Hanseatic City of Hamburg

Acknowledgements

Fraunhofer IWES is funded by

The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)

27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES

Thanks a lot for your attention

gerritwolken-moehlmanniwesfraunhoferde

28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES

References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain

Page 5: Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification are important for the commercial acceptance of FLS Both IWES FLS using Windcube

5copy 2019 Fraunhofer IWES 5copy 2019 Fraunhofer IWES 5copy 2019 Fraunhofer IWES

Introduction

Technology Applications

Wind resource assessment (WRA)

Power curve measurements

hellip

Standards Recommended

practices

VerificationClass ification

[5]

[234]

6copy 2019 Fraunhofer IWES 6copy 2019 Fraunhofer IWES 6copy 2019 Fraunhofer IWES

Introduction

Technology Applications

Wind resource assessment (WRA)

Power curve measurements

hellip

Standards Recommended

practices

VerificationClass ification

[5]

[234]

7copy 2019 Fraunhofer IWES 7copy 2019 Fraunhofer IWES 7copy 2019 Fraunhofer IWES

FLS verification vs classificationVerification Class ification

For a distinct system For selected conditions short term measurement ~1 month

For a FLS type Correlation WSP deviation and

independent variable At least 3 months measurement

hellip

8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES

Fraunhofer IWES LiDAR Buoy

System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator

batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t

9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES

Fraunhofer IWES LiDAR Buoy

System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator

batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t

Analysed Measurements (exceeding 6 months 2016) LiDAR Buoy at FINO3 (Windcube) LiDAR Buoy at FINO1 (ZephIR)

10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES

Verification

Comparison of FLS wind speed and wind direction compared to reference

11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES

Verification

Comparison of FLS wind speed and wind direction compared to reference-gt Key parameter (slope and Rsup2) exceed Best Practice requirements

12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES

Classification ndash Environmental Variables

Wind speed deviation (FLS -Reference) vs environmental variables (EV)

Wind speed Wind direction Wind shear Wind veer Temperature and

temperature difference Air density hellip

Meteorological variables (defined in IEC 64100-12-1)

13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES

Classification ndash Environmental Variables

Wind speed deviation (FLS -Reference) vs environmental variables (EV)

Wind speed Wind direction Wind shear Wind veer Temperature and

temperature difference Air density hellip

Meteorological variables (defined in IEC 64100-12-1) Oceanographic variables

Wave height Wave period Water level Currents hellip

Tilting Yawing Heave Translation hellip

Platform motion variables

14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear (example)

15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear

FLS is sensitive for independent variable if

- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01

16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height Hs

17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height HsSensitive Not sensitive

18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

Are the variables independent correlations

19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable

See Barker Et al [6]

Considering

shear

no

no

yes

no

no

no

20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

Oceanographic variables and motion variable (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR2

Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no

Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no

Current 0096 -1382 -0133 0002 -0006 no

Heave range 0570 -0219 -0125 0000 -0002 no

Tilt Range 3811 0027 0105 0000 0001 no

Yaw increment range 8559 -0008 -0069 0002 -0003 no

Static tilt 0473 -0387 -0183 0002 -0008 no

- No sensitivities for oceanographic or platform motion variables

21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES

Classification ndash Final classification

Classification results for FINO1 campaign

-gt Most uncertainty comes from reference measurement uncertainty

22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES

Classification ndash Results

FLS (Windcube) FLS (ZXZephIR) 100m

For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified

Independant variableSensitivity

m x std

Sensitivit

y x RSensitive

[-] [] []

Significant wave height (buoy) -0101 -0001 no

Peak period Tp (Buoy) 0059 0001 no

Current -0133 -0006 no

Heave range -0125 -0002 no

Tilt Range 0105 0001 no

Yaw increment range -0069 -0003 no

Static tilt -0183 -0008 no

Independant variableSensitivity

m x std

Sensitivity

x rSensitive

[-] [] []

Significant wave height -0063 -0001 no

Peak period Tp (Buoy) -0191 -0007 no

Tm02 (radar) 0013 0000 no

Waterlevel -0069 0000 no

Heave range -0118 -0002 no

Tilt Range 0078 0000 no

Yaw increment range -0054 -0001 no

Static tilt 0075 0002 no

23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

Meteorological variables

FLS set-up

Oceanographic variables

Motion variables

Measurement

25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES

Resume

Verification and classification are important for the commercial acceptance of FLS

Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables

Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast

26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES

Federal Republic of Germany

Federal Ministry for Economic Affairs and Energy

Federal Ministry of Education and Research

European Regional Development Fund (ERDF)

Federal State of Bremen

Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH

Federal State of Lower Saxony

Free and Hanseatic City of Hamburg

Acknowledgements

Fraunhofer IWES is funded by

The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)

27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES

Thanks a lot for your attention

gerritwolken-moehlmanniwesfraunhoferde

28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES

References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain

Page 6: Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification are important for the commercial acceptance of FLS Both IWES FLS using Windcube

6copy 2019 Fraunhofer IWES 6copy 2019 Fraunhofer IWES 6copy 2019 Fraunhofer IWES

Introduction

Technology Applications

Wind resource assessment (WRA)

Power curve measurements

hellip

Standards Recommended

practices

VerificationClass ification

[5]

[234]

7copy 2019 Fraunhofer IWES 7copy 2019 Fraunhofer IWES 7copy 2019 Fraunhofer IWES

FLS verification vs classificationVerification Class ification

For a distinct system For selected conditions short term measurement ~1 month

For a FLS type Correlation WSP deviation and

independent variable At least 3 months measurement

hellip

8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES

Fraunhofer IWES LiDAR Buoy

System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator

batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t

9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES

Fraunhofer IWES LiDAR Buoy

System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator

batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t

Analysed Measurements (exceeding 6 months 2016) LiDAR Buoy at FINO3 (Windcube) LiDAR Buoy at FINO1 (ZephIR)

10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES

Verification

Comparison of FLS wind speed and wind direction compared to reference

11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES

Verification

Comparison of FLS wind speed and wind direction compared to reference-gt Key parameter (slope and Rsup2) exceed Best Practice requirements

12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES

Classification ndash Environmental Variables

Wind speed deviation (FLS -Reference) vs environmental variables (EV)

Wind speed Wind direction Wind shear Wind veer Temperature and

temperature difference Air density hellip

Meteorological variables (defined in IEC 64100-12-1)

13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES

Classification ndash Environmental Variables

Wind speed deviation (FLS -Reference) vs environmental variables (EV)

Wind speed Wind direction Wind shear Wind veer Temperature and

temperature difference Air density hellip

Meteorological variables (defined in IEC 64100-12-1) Oceanographic variables

Wave height Wave period Water level Currents hellip

Tilting Yawing Heave Translation hellip

Platform motion variables

14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear (example)

15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear

FLS is sensitive for independent variable if

- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01

16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height Hs

17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height HsSensitive Not sensitive

18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

Are the variables independent correlations

19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable

See Barker Et al [6]

Considering

shear

no

no

yes

no

no

no

20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

Oceanographic variables and motion variable (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR2

Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no

Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no

Current 0096 -1382 -0133 0002 -0006 no

Heave range 0570 -0219 -0125 0000 -0002 no

Tilt Range 3811 0027 0105 0000 0001 no

Yaw increment range 8559 -0008 -0069 0002 -0003 no

Static tilt 0473 -0387 -0183 0002 -0008 no

- No sensitivities for oceanographic or platform motion variables

21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES

Classification ndash Final classification

Classification results for FINO1 campaign

-gt Most uncertainty comes from reference measurement uncertainty

22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES

Classification ndash Results

FLS (Windcube) FLS (ZXZephIR) 100m

For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified

Independant variableSensitivity

m x std

Sensitivit

y x RSensitive

[-] [] []

Significant wave height (buoy) -0101 -0001 no

Peak period Tp (Buoy) 0059 0001 no

Current -0133 -0006 no

Heave range -0125 -0002 no

Tilt Range 0105 0001 no

Yaw increment range -0069 -0003 no

Static tilt -0183 -0008 no

Independant variableSensitivity

m x std

Sensitivity

x rSensitive

[-] [] []

Significant wave height -0063 -0001 no

Peak period Tp (Buoy) -0191 -0007 no

Tm02 (radar) 0013 0000 no

Waterlevel -0069 0000 no

Heave range -0118 -0002 no

Tilt Range 0078 0000 no

Yaw increment range -0054 -0001 no

Static tilt 0075 0002 no

23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

Meteorological variables

FLS set-up

Oceanographic variables

Motion variables

Measurement

25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES

Resume

Verification and classification are important for the commercial acceptance of FLS

Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables

Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast

26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES

Federal Republic of Germany

Federal Ministry for Economic Affairs and Energy

Federal Ministry of Education and Research

European Regional Development Fund (ERDF)

Federal State of Bremen

Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH

Federal State of Lower Saxony

Free and Hanseatic City of Hamburg

Acknowledgements

Fraunhofer IWES is funded by

The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)

27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES

Thanks a lot for your attention

gerritwolken-moehlmanniwesfraunhoferde

28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES

References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain

Page 7: Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification are important for the commercial acceptance of FLS Both IWES FLS using Windcube

7copy 2019 Fraunhofer IWES 7copy 2019 Fraunhofer IWES 7copy 2019 Fraunhofer IWES

FLS verification vs classificationVerification Class ification

For a distinct system For selected conditions short term measurement ~1 month

For a FLS type Correlation WSP deviation and

independent variable At least 3 months measurement

hellip

8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES

Fraunhofer IWES LiDAR Buoy

System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator

batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t

9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES

Fraunhofer IWES LiDAR Buoy

System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator

batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t

Analysed Measurements (exceeding 6 months 2016) LiDAR Buoy at FINO3 (Windcube) LiDAR Buoy at FINO1 (ZephIR)

10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES

Verification

Comparison of FLS wind speed and wind direction compared to reference

11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES

Verification

Comparison of FLS wind speed and wind direction compared to reference-gt Key parameter (slope and Rsup2) exceed Best Practice requirements

12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES

Classification ndash Environmental Variables

Wind speed deviation (FLS -Reference) vs environmental variables (EV)

Wind speed Wind direction Wind shear Wind veer Temperature and

temperature difference Air density hellip

Meteorological variables (defined in IEC 64100-12-1)

13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES

Classification ndash Environmental Variables

Wind speed deviation (FLS -Reference) vs environmental variables (EV)

Wind speed Wind direction Wind shear Wind veer Temperature and

temperature difference Air density hellip

Meteorological variables (defined in IEC 64100-12-1) Oceanographic variables

Wave height Wave period Water level Currents hellip

Tilting Yawing Heave Translation hellip

Platform motion variables

14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear (example)

15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear

FLS is sensitive for independent variable if

- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01

16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height Hs

17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height HsSensitive Not sensitive

18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

Are the variables independent correlations

19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable

See Barker Et al [6]

Considering

shear

no

no

yes

no

no

no

20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

Oceanographic variables and motion variable (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR2

Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no

Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no

Current 0096 -1382 -0133 0002 -0006 no

Heave range 0570 -0219 -0125 0000 -0002 no

Tilt Range 3811 0027 0105 0000 0001 no

Yaw increment range 8559 -0008 -0069 0002 -0003 no

Static tilt 0473 -0387 -0183 0002 -0008 no

- No sensitivities for oceanographic or platform motion variables

21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES

Classification ndash Final classification

Classification results for FINO1 campaign

-gt Most uncertainty comes from reference measurement uncertainty

22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES

Classification ndash Results

FLS (Windcube) FLS (ZXZephIR) 100m

For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified

Independant variableSensitivity

m x std

Sensitivit

y x RSensitive

[-] [] []

Significant wave height (buoy) -0101 -0001 no

Peak period Tp (Buoy) 0059 0001 no

Current -0133 -0006 no

Heave range -0125 -0002 no

Tilt Range 0105 0001 no

Yaw increment range -0069 -0003 no

Static tilt -0183 -0008 no

Independant variableSensitivity

m x std

Sensitivity

x rSensitive

[-] [] []

Significant wave height -0063 -0001 no

Peak period Tp (Buoy) -0191 -0007 no

Tm02 (radar) 0013 0000 no

Waterlevel -0069 0000 no

Heave range -0118 -0002 no

Tilt Range 0078 0000 no

Yaw increment range -0054 -0001 no

Static tilt 0075 0002 no

23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

Meteorological variables

FLS set-up

Oceanographic variables

Motion variables

Measurement

25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES

Resume

Verification and classification are important for the commercial acceptance of FLS

Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables

Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast

26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES

Federal Republic of Germany

Federal Ministry for Economic Affairs and Energy

Federal Ministry of Education and Research

European Regional Development Fund (ERDF)

Federal State of Bremen

Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH

Federal State of Lower Saxony

Free and Hanseatic City of Hamburg

Acknowledgements

Fraunhofer IWES is funded by

The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)

27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES

Thanks a lot for your attention

gerritwolken-moehlmanniwesfraunhoferde

28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES

References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain

Page 8: Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification are important for the commercial acceptance of FLS Both IWES FLS using Windcube

8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES 8copy 2019 Fraunhofer IWES

Fraunhofer IWES LiDAR Buoy

System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator

batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t

9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES

Fraunhofer IWES LiDAR Buoy

System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator

batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t

Analysed Measurements (exceeding 6 months 2016) LiDAR Buoy at FINO3 (Windcube) LiDAR Buoy at FINO1 (ZephIR)

10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES

Verification

Comparison of FLS wind speed and wind direction compared to reference

11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES

Verification

Comparison of FLS wind speed and wind direction compared to reference-gt Key parameter (slope and Rsup2) exceed Best Practice requirements

12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES

Classification ndash Environmental Variables

Wind speed deviation (FLS -Reference) vs environmental variables (EV)

Wind speed Wind direction Wind shear Wind veer Temperature and

temperature difference Air density hellip

Meteorological variables (defined in IEC 64100-12-1)

13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES

Classification ndash Environmental Variables

Wind speed deviation (FLS -Reference) vs environmental variables (EV)

Wind speed Wind direction Wind shear Wind veer Temperature and

temperature difference Air density hellip

Meteorological variables (defined in IEC 64100-12-1) Oceanographic variables

Wave height Wave period Water level Currents hellip

Tilting Yawing Heave Translation hellip

Platform motion variables

14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear (example)

15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear

FLS is sensitive for independent variable if

- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01

16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height Hs

17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height HsSensitive Not sensitive

18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

Are the variables independent correlations

19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable

See Barker Et al [6]

Considering

shear

no

no

yes

no

no

no

20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

Oceanographic variables and motion variable (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR2

Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no

Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no

Current 0096 -1382 -0133 0002 -0006 no

Heave range 0570 -0219 -0125 0000 -0002 no

Tilt Range 3811 0027 0105 0000 0001 no

Yaw increment range 8559 -0008 -0069 0002 -0003 no

Static tilt 0473 -0387 -0183 0002 -0008 no

- No sensitivities for oceanographic or platform motion variables

21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES

Classification ndash Final classification

Classification results for FINO1 campaign

-gt Most uncertainty comes from reference measurement uncertainty

22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES

Classification ndash Results

FLS (Windcube) FLS (ZXZephIR) 100m

For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified

Independant variableSensitivity

m x std

Sensitivit

y x RSensitive

[-] [] []

Significant wave height (buoy) -0101 -0001 no

Peak period Tp (Buoy) 0059 0001 no

Current -0133 -0006 no

Heave range -0125 -0002 no

Tilt Range 0105 0001 no

Yaw increment range -0069 -0003 no

Static tilt -0183 -0008 no

Independant variableSensitivity

m x std

Sensitivity

x rSensitive

[-] [] []

Significant wave height -0063 -0001 no

Peak period Tp (Buoy) -0191 -0007 no

Tm02 (radar) 0013 0000 no

Waterlevel -0069 0000 no

Heave range -0118 -0002 no

Tilt Range 0078 0000 no

Yaw increment range -0054 -0001 no

Static tilt 0075 0002 no

23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

Meteorological variables

FLS set-up

Oceanographic variables

Motion variables

Measurement

25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES

Resume

Verification and classification are important for the commercial acceptance of FLS

Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables

Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast

26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES

Federal Republic of Germany

Federal Ministry for Economic Affairs and Energy

Federal Ministry of Education and Research

European Regional Development Fund (ERDF)

Federal State of Bremen

Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH

Federal State of Lower Saxony

Free and Hanseatic City of Hamburg

Acknowledgements

Fraunhofer IWES is funded by

The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)

27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES

Thanks a lot for your attention

gerritwolken-moehlmanniwesfraunhoferde

28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES

References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain

Page 9: Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification are important for the commercial acceptance of FLS Both IWES FLS using Windcube

9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES 9copy 2019 Fraunhofer IWES

Fraunhofer IWES LiDAR Buoy

System Hull from light fire buoy developed in 1980 Power supply 3 micro wind turbine PV back-up generator

batteries LiDAR WindCube V2 or ZX 300 (ZephIR) Weight ca 35 t

Analysed Measurements (exceeding 6 months 2016) LiDAR Buoy at FINO3 (Windcube) LiDAR Buoy at FINO1 (ZephIR)

10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES

Verification

Comparison of FLS wind speed and wind direction compared to reference

11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES

Verification

Comparison of FLS wind speed and wind direction compared to reference-gt Key parameter (slope and Rsup2) exceed Best Practice requirements

12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES

Classification ndash Environmental Variables

Wind speed deviation (FLS -Reference) vs environmental variables (EV)

Wind speed Wind direction Wind shear Wind veer Temperature and

temperature difference Air density hellip

Meteorological variables (defined in IEC 64100-12-1)

13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES

Classification ndash Environmental Variables

Wind speed deviation (FLS -Reference) vs environmental variables (EV)

Wind speed Wind direction Wind shear Wind veer Temperature and

temperature difference Air density hellip

Meteorological variables (defined in IEC 64100-12-1) Oceanographic variables

Wave height Wave period Water level Currents hellip

Tilting Yawing Heave Translation hellip

Platform motion variables

14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear (example)

15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear

FLS is sensitive for independent variable if

- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01

16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height Hs

17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height HsSensitive Not sensitive

18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

Are the variables independent correlations

19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable

See Barker Et al [6]

Considering

shear

no

no

yes

no

no

no

20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

Oceanographic variables and motion variable (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR2

Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no

Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no

Current 0096 -1382 -0133 0002 -0006 no

Heave range 0570 -0219 -0125 0000 -0002 no

Tilt Range 3811 0027 0105 0000 0001 no

Yaw increment range 8559 -0008 -0069 0002 -0003 no

Static tilt 0473 -0387 -0183 0002 -0008 no

- No sensitivities for oceanographic or platform motion variables

21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES

Classification ndash Final classification

Classification results for FINO1 campaign

-gt Most uncertainty comes from reference measurement uncertainty

22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES

Classification ndash Results

FLS (Windcube) FLS (ZXZephIR) 100m

For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified

Independant variableSensitivity

m x std

Sensitivit

y x RSensitive

[-] [] []

Significant wave height (buoy) -0101 -0001 no

Peak period Tp (Buoy) 0059 0001 no

Current -0133 -0006 no

Heave range -0125 -0002 no

Tilt Range 0105 0001 no

Yaw increment range -0069 -0003 no

Static tilt -0183 -0008 no

Independant variableSensitivity

m x std

Sensitivity

x rSensitive

[-] [] []

Significant wave height -0063 -0001 no

Peak period Tp (Buoy) -0191 -0007 no

Tm02 (radar) 0013 0000 no

Waterlevel -0069 0000 no

Heave range -0118 -0002 no

Tilt Range 0078 0000 no

Yaw increment range -0054 -0001 no

Static tilt 0075 0002 no

23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

Meteorological variables

FLS set-up

Oceanographic variables

Motion variables

Measurement

25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES

Resume

Verification and classification are important for the commercial acceptance of FLS

Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables

Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast

26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES

Federal Republic of Germany

Federal Ministry for Economic Affairs and Energy

Federal Ministry of Education and Research

European Regional Development Fund (ERDF)

Federal State of Bremen

Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH

Federal State of Lower Saxony

Free and Hanseatic City of Hamburg

Acknowledgements

Fraunhofer IWES is funded by

The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)

27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES

Thanks a lot for your attention

gerritwolken-moehlmanniwesfraunhoferde

28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES

References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain

Page 10: Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification are important for the commercial acceptance of FLS Both IWES FLS using Windcube

10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES 10copy 2019 Fraunhofer IWES

Verification

Comparison of FLS wind speed and wind direction compared to reference

11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES

Verification

Comparison of FLS wind speed and wind direction compared to reference-gt Key parameter (slope and Rsup2) exceed Best Practice requirements

12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES

Classification ndash Environmental Variables

Wind speed deviation (FLS -Reference) vs environmental variables (EV)

Wind speed Wind direction Wind shear Wind veer Temperature and

temperature difference Air density hellip

Meteorological variables (defined in IEC 64100-12-1)

13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES

Classification ndash Environmental Variables

Wind speed deviation (FLS -Reference) vs environmental variables (EV)

Wind speed Wind direction Wind shear Wind veer Temperature and

temperature difference Air density hellip

Meteorological variables (defined in IEC 64100-12-1) Oceanographic variables

Wave height Wave period Water level Currents hellip

Tilting Yawing Heave Translation hellip

Platform motion variables

14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear (example)

15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear

FLS is sensitive for independent variable if

- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01

16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height Hs

17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height HsSensitive Not sensitive

18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

Are the variables independent correlations

19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable

See Barker Et al [6]

Considering

shear

no

no

yes

no

no

no

20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

Oceanographic variables and motion variable (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR2

Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no

Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no

Current 0096 -1382 -0133 0002 -0006 no

Heave range 0570 -0219 -0125 0000 -0002 no

Tilt Range 3811 0027 0105 0000 0001 no

Yaw increment range 8559 -0008 -0069 0002 -0003 no

Static tilt 0473 -0387 -0183 0002 -0008 no

- No sensitivities for oceanographic or platform motion variables

21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES

Classification ndash Final classification

Classification results for FINO1 campaign

-gt Most uncertainty comes from reference measurement uncertainty

22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES

Classification ndash Results

FLS (Windcube) FLS (ZXZephIR) 100m

For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified

Independant variableSensitivity

m x std

Sensitivit

y x RSensitive

[-] [] []

Significant wave height (buoy) -0101 -0001 no

Peak period Tp (Buoy) 0059 0001 no

Current -0133 -0006 no

Heave range -0125 -0002 no

Tilt Range 0105 0001 no

Yaw increment range -0069 -0003 no

Static tilt -0183 -0008 no

Independant variableSensitivity

m x std

Sensitivity

x rSensitive

[-] [] []

Significant wave height -0063 -0001 no

Peak period Tp (Buoy) -0191 -0007 no

Tm02 (radar) 0013 0000 no

Waterlevel -0069 0000 no

Heave range -0118 -0002 no

Tilt Range 0078 0000 no

Yaw increment range -0054 -0001 no

Static tilt 0075 0002 no

23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

Meteorological variables

FLS set-up

Oceanographic variables

Motion variables

Measurement

25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES

Resume

Verification and classification are important for the commercial acceptance of FLS

Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables

Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast

26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES

Federal Republic of Germany

Federal Ministry for Economic Affairs and Energy

Federal Ministry of Education and Research

European Regional Development Fund (ERDF)

Federal State of Bremen

Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH

Federal State of Lower Saxony

Free and Hanseatic City of Hamburg

Acknowledgements

Fraunhofer IWES is funded by

The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)

27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES

Thanks a lot for your attention

gerritwolken-moehlmanniwesfraunhoferde

28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES

References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain

Page 11: Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification are important for the commercial acceptance of FLS Both IWES FLS using Windcube

11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES 11copy 2019 Fraunhofer IWES

Verification

Comparison of FLS wind speed and wind direction compared to reference-gt Key parameter (slope and Rsup2) exceed Best Practice requirements

12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES

Classification ndash Environmental Variables

Wind speed deviation (FLS -Reference) vs environmental variables (EV)

Wind speed Wind direction Wind shear Wind veer Temperature and

temperature difference Air density hellip

Meteorological variables (defined in IEC 64100-12-1)

13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES

Classification ndash Environmental Variables

Wind speed deviation (FLS -Reference) vs environmental variables (EV)

Wind speed Wind direction Wind shear Wind veer Temperature and

temperature difference Air density hellip

Meteorological variables (defined in IEC 64100-12-1) Oceanographic variables

Wave height Wave period Water level Currents hellip

Tilting Yawing Heave Translation hellip

Platform motion variables

14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear (example)

15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear

FLS is sensitive for independent variable if

- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01

16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height Hs

17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height HsSensitive Not sensitive

18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

Are the variables independent correlations

19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable

See Barker Et al [6]

Considering

shear

no

no

yes

no

no

no

20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

Oceanographic variables and motion variable (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR2

Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no

Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no

Current 0096 -1382 -0133 0002 -0006 no

Heave range 0570 -0219 -0125 0000 -0002 no

Tilt Range 3811 0027 0105 0000 0001 no

Yaw increment range 8559 -0008 -0069 0002 -0003 no

Static tilt 0473 -0387 -0183 0002 -0008 no

- No sensitivities for oceanographic or platform motion variables

21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES

Classification ndash Final classification

Classification results for FINO1 campaign

-gt Most uncertainty comes from reference measurement uncertainty

22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES

Classification ndash Results

FLS (Windcube) FLS (ZXZephIR) 100m

For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified

Independant variableSensitivity

m x std

Sensitivit

y x RSensitive

[-] [] []

Significant wave height (buoy) -0101 -0001 no

Peak period Tp (Buoy) 0059 0001 no

Current -0133 -0006 no

Heave range -0125 -0002 no

Tilt Range 0105 0001 no

Yaw increment range -0069 -0003 no

Static tilt -0183 -0008 no

Independant variableSensitivity

m x std

Sensitivity

x rSensitive

[-] [] []

Significant wave height -0063 -0001 no

Peak period Tp (Buoy) -0191 -0007 no

Tm02 (radar) 0013 0000 no

Waterlevel -0069 0000 no

Heave range -0118 -0002 no

Tilt Range 0078 0000 no

Yaw increment range -0054 -0001 no

Static tilt 0075 0002 no

23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

Meteorological variables

FLS set-up

Oceanographic variables

Motion variables

Measurement

25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES

Resume

Verification and classification are important for the commercial acceptance of FLS

Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables

Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast

26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES

Federal Republic of Germany

Federal Ministry for Economic Affairs and Energy

Federal Ministry of Education and Research

European Regional Development Fund (ERDF)

Federal State of Bremen

Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH

Federal State of Lower Saxony

Free and Hanseatic City of Hamburg

Acknowledgements

Fraunhofer IWES is funded by

The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)

27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES

Thanks a lot for your attention

gerritwolken-moehlmanniwesfraunhoferde

28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES

References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain

Page 12: Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification are important for the commercial acceptance of FLS Both IWES FLS using Windcube

12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES 12copy 2019 Fraunhofer IWES

Classification ndash Environmental Variables

Wind speed deviation (FLS -Reference) vs environmental variables (EV)

Wind speed Wind direction Wind shear Wind veer Temperature and

temperature difference Air density hellip

Meteorological variables (defined in IEC 64100-12-1)

13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES

Classification ndash Environmental Variables

Wind speed deviation (FLS -Reference) vs environmental variables (EV)

Wind speed Wind direction Wind shear Wind veer Temperature and

temperature difference Air density hellip

Meteorological variables (defined in IEC 64100-12-1) Oceanographic variables

Wave height Wave period Water level Currents hellip

Tilting Yawing Heave Translation hellip

Platform motion variables

14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear (example)

15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear

FLS is sensitive for independent variable if

- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01

16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height Hs

17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height HsSensitive Not sensitive

18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

Are the variables independent correlations

19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable

See Barker Et al [6]

Considering

shear

no

no

yes

no

no

no

20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

Oceanographic variables and motion variable (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR2

Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no

Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no

Current 0096 -1382 -0133 0002 -0006 no

Heave range 0570 -0219 -0125 0000 -0002 no

Tilt Range 3811 0027 0105 0000 0001 no

Yaw increment range 8559 -0008 -0069 0002 -0003 no

Static tilt 0473 -0387 -0183 0002 -0008 no

- No sensitivities for oceanographic or platform motion variables

21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES

Classification ndash Final classification

Classification results for FINO1 campaign

-gt Most uncertainty comes from reference measurement uncertainty

22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES

Classification ndash Results

FLS (Windcube) FLS (ZXZephIR) 100m

For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified

Independant variableSensitivity

m x std

Sensitivit

y x RSensitive

[-] [] []

Significant wave height (buoy) -0101 -0001 no

Peak period Tp (Buoy) 0059 0001 no

Current -0133 -0006 no

Heave range -0125 -0002 no

Tilt Range 0105 0001 no

Yaw increment range -0069 -0003 no

Static tilt -0183 -0008 no

Independant variableSensitivity

m x std

Sensitivity

x rSensitive

[-] [] []

Significant wave height -0063 -0001 no

Peak period Tp (Buoy) -0191 -0007 no

Tm02 (radar) 0013 0000 no

Waterlevel -0069 0000 no

Heave range -0118 -0002 no

Tilt Range 0078 0000 no

Yaw increment range -0054 -0001 no

Static tilt 0075 0002 no

23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

Meteorological variables

FLS set-up

Oceanographic variables

Motion variables

Measurement

25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES

Resume

Verification and classification are important for the commercial acceptance of FLS

Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables

Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast

26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES

Federal Republic of Germany

Federal Ministry for Economic Affairs and Energy

Federal Ministry of Education and Research

European Regional Development Fund (ERDF)

Federal State of Bremen

Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH

Federal State of Lower Saxony

Free and Hanseatic City of Hamburg

Acknowledgements

Fraunhofer IWES is funded by

The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)

27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES

Thanks a lot for your attention

gerritwolken-moehlmanniwesfraunhoferde

28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES

References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain

Page 13: Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification are important for the commercial acceptance of FLS Both IWES FLS using Windcube

13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES 13copy 2019 Fraunhofer IWES

Classification ndash Environmental Variables

Wind speed deviation (FLS -Reference) vs environmental variables (EV)

Wind speed Wind direction Wind shear Wind veer Temperature and

temperature difference Air density hellip

Meteorological variables (defined in IEC 64100-12-1) Oceanographic variables

Wave height Wave period Water level Currents hellip

Tilting Yawing Heave Translation hellip

Platform motion variables

14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear (example)

15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear

FLS is sensitive for independent variable if

- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01

16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height Hs

17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height HsSensitive Not sensitive

18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

Are the variables independent correlations

19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable

See Barker Et al [6]

Considering

shear

no

no

yes

no

no

no

20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

Oceanographic variables and motion variable (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR2

Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no

Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no

Current 0096 -1382 -0133 0002 -0006 no

Heave range 0570 -0219 -0125 0000 -0002 no

Tilt Range 3811 0027 0105 0000 0001 no

Yaw increment range 8559 -0008 -0069 0002 -0003 no

Static tilt 0473 -0387 -0183 0002 -0008 no

- No sensitivities for oceanographic or platform motion variables

21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES

Classification ndash Final classification

Classification results for FINO1 campaign

-gt Most uncertainty comes from reference measurement uncertainty

22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES

Classification ndash Results

FLS (Windcube) FLS (ZXZephIR) 100m

For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified

Independant variableSensitivity

m x std

Sensitivit

y x RSensitive

[-] [] []

Significant wave height (buoy) -0101 -0001 no

Peak period Tp (Buoy) 0059 0001 no

Current -0133 -0006 no

Heave range -0125 -0002 no

Tilt Range 0105 0001 no

Yaw increment range -0069 -0003 no

Static tilt -0183 -0008 no

Independant variableSensitivity

m x std

Sensitivity

x rSensitive

[-] [] []

Significant wave height -0063 -0001 no

Peak period Tp (Buoy) -0191 -0007 no

Tm02 (radar) 0013 0000 no

Waterlevel -0069 0000 no

Heave range -0118 -0002 no

Tilt Range 0078 0000 no

Yaw increment range -0054 -0001 no

Static tilt 0075 0002 no

23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

Meteorological variables

FLS set-up

Oceanographic variables

Motion variables

Measurement

25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES

Resume

Verification and classification are important for the commercial acceptance of FLS

Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables

Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast

26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES

Federal Republic of Germany

Federal Ministry for Economic Affairs and Energy

Federal Ministry of Education and Research

European Regional Development Fund (ERDF)

Federal State of Bremen

Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH

Federal State of Lower Saxony

Free and Hanseatic City of Hamburg

Acknowledgements

Fraunhofer IWES is funded by

The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)

27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES

Thanks a lot for your attention

gerritwolken-moehlmanniwesfraunhoferde

28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES

References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain

Page 14: Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification are important for the commercial acceptance of FLS Both IWES FLS using Windcube

14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES 14copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear (example)

15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear

FLS is sensitive for independent variable if

- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01

16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height Hs

17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height HsSensitive Not sensitive

18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

Are the variables independent correlations

19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable

See Barker Et al [6]

Considering

shear

no

no

yes

no

no

no

20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

Oceanographic variables and motion variable (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR2

Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no

Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no

Current 0096 -1382 -0133 0002 -0006 no

Heave range 0570 -0219 -0125 0000 -0002 no

Tilt Range 3811 0027 0105 0000 0001 no

Yaw increment range 8559 -0008 -0069 0002 -0003 no

Static tilt 0473 -0387 -0183 0002 -0008 no

- No sensitivities for oceanographic or platform motion variables

21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES

Classification ndash Final classification

Classification results for FINO1 campaign

-gt Most uncertainty comes from reference measurement uncertainty

22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES

Classification ndash Results

FLS (Windcube) FLS (ZXZephIR) 100m

For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified

Independant variableSensitivity

m x std

Sensitivit

y x RSensitive

[-] [] []

Significant wave height (buoy) -0101 -0001 no

Peak period Tp (Buoy) 0059 0001 no

Current -0133 -0006 no

Heave range -0125 -0002 no

Tilt Range 0105 0001 no

Yaw increment range -0069 -0003 no

Static tilt -0183 -0008 no

Independant variableSensitivity

m x std

Sensitivity

x rSensitive

[-] [] []

Significant wave height -0063 -0001 no

Peak period Tp (Buoy) -0191 -0007 no

Tm02 (radar) 0013 0000 no

Waterlevel -0069 0000 no

Heave range -0118 -0002 no

Tilt Range 0078 0000 no

Yaw increment range -0054 -0001 no

Static tilt 0075 0002 no

23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

Meteorological variables

FLS set-up

Oceanographic variables

Motion variables

Measurement

25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES

Resume

Verification and classification are important for the commercial acceptance of FLS

Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables

Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast

26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES

Federal Republic of Germany

Federal Ministry for Economic Affairs and Energy

Federal Ministry of Education and Research

European Regional Development Fund (ERDF)

Federal State of Bremen

Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH

Federal State of Lower Saxony

Free and Hanseatic City of Hamburg

Acknowledgements

Fraunhofer IWES is funded by

The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)

27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES

Thanks a lot for your attention

gerritwolken-moehlmanniwesfraunhoferde

28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES

References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain

Page 15: Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification are important for the commercial acceptance of FLS Both IWES FLS using Windcube

15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES 15copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear

FLS is sensitive for independent variable if

- |Sensitiv ity | gt 05- |Sensitiv ity R| gt 01

16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height Hs

17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height HsSensitive Not sensitive

18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

Are the variables independent correlations

19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable

See Barker Et al [6]

Considering

shear

no

no

yes

no

no

no

20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

Oceanographic variables and motion variable (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR2

Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no

Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no

Current 0096 -1382 -0133 0002 -0006 no

Heave range 0570 -0219 -0125 0000 -0002 no

Tilt Range 3811 0027 0105 0000 0001 no

Yaw increment range 8559 -0008 -0069 0002 -0003 no

Static tilt 0473 -0387 -0183 0002 -0008 no

- No sensitivities for oceanographic or platform motion variables

21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES

Classification ndash Final classification

Classification results for FINO1 campaign

-gt Most uncertainty comes from reference measurement uncertainty

22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES

Classification ndash Results

FLS (Windcube) FLS (ZXZephIR) 100m

For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified

Independant variableSensitivity

m x std

Sensitivit

y x RSensitive

[-] [] []

Significant wave height (buoy) -0101 -0001 no

Peak period Tp (Buoy) 0059 0001 no

Current -0133 -0006 no

Heave range -0125 -0002 no

Tilt Range 0105 0001 no

Yaw increment range -0069 -0003 no

Static tilt -0183 -0008 no

Independant variableSensitivity

m x std

Sensitivity

x rSensitive

[-] [] []

Significant wave height -0063 -0001 no

Peak period Tp (Buoy) -0191 -0007 no

Tm02 (radar) 0013 0000 no

Waterlevel -0069 0000 no

Heave range -0118 -0002 no

Tilt Range 0078 0000 no

Yaw increment range -0054 -0001 no

Static tilt 0075 0002 no

23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

Meteorological variables

FLS set-up

Oceanographic variables

Motion variables

Measurement

25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES

Resume

Verification and classification are important for the commercial acceptance of FLS

Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables

Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast

26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES

Federal Republic of Germany

Federal Ministry for Economic Affairs and Energy

Federal Ministry of Education and Research

European Regional Development Fund (ERDF)

Federal State of Bremen

Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH

Federal State of Lower Saxony

Free and Hanseatic City of Hamburg

Acknowledgements

Fraunhofer IWES is funded by

The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)

27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES

Thanks a lot for your attention

gerritwolken-moehlmanniwesfraunhoferde

28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES

References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain

Page 16: Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification are important for the commercial acceptance of FLS Both IWES FLS using Windcube

16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES 16copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height Hs

17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height HsSensitive Not sensitive

18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

Are the variables independent correlations

19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable

See Barker Et al [6]

Considering

shear

no

no

yes

no

no

no

20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

Oceanographic variables and motion variable (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR2

Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no

Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no

Current 0096 -1382 -0133 0002 -0006 no

Heave range 0570 -0219 -0125 0000 -0002 no

Tilt Range 3811 0027 0105 0000 0001 no

Yaw increment range 8559 -0008 -0069 0002 -0003 no

Static tilt 0473 -0387 -0183 0002 -0008 no

- No sensitivities for oceanographic or platform motion variables

21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES

Classification ndash Final classification

Classification results for FINO1 campaign

-gt Most uncertainty comes from reference measurement uncertainty

22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES

Classification ndash Results

FLS (Windcube) FLS (ZXZephIR) 100m

For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified

Independant variableSensitivity

m x std

Sensitivit

y x RSensitive

[-] [] []

Significant wave height (buoy) -0101 -0001 no

Peak period Tp (Buoy) 0059 0001 no

Current -0133 -0006 no

Heave range -0125 -0002 no

Tilt Range 0105 0001 no

Yaw increment range -0069 -0003 no

Static tilt -0183 -0008 no

Independant variableSensitivity

m x std

Sensitivity

x rSensitive

[-] [] []

Significant wave height -0063 -0001 no

Peak period Tp (Buoy) -0191 -0007 no

Tm02 (radar) 0013 0000 no

Waterlevel -0069 0000 no

Heave range -0118 -0002 no

Tilt Range 0078 0000 no

Yaw increment range -0054 -0001 no

Static tilt 0075 0002 no

23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

Meteorological variables

FLS set-up

Oceanographic variables

Motion variables

Measurement

25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES

Resume

Verification and classification are important for the commercial acceptance of FLS

Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables

Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast

26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES

Federal Republic of Germany

Federal Ministry for Economic Affairs and Energy

Federal Ministry of Education and Research

European Regional Development Fund (ERDF)

Federal State of Bremen

Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH

Federal State of Lower Saxony

Free and Hanseatic City of Hamburg

Acknowledgements

Fraunhofer IWES is funded by

The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)

27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES

Thanks a lot for your attention

gerritwolken-moehlmanniwesfraunhoferde

28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES

References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain

Page 17: Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification are important for the commercial acceptance of FLS Both IWES FLS using Windcube

17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES 17copy 2019 Fraunhofer IWES

Classification - Sensitivity

Wind shear Significant Wave height HsSensitive Not sensitive

18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

Are the variables independent correlations

19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable

See Barker Et al [6]

Considering

shear

no

no

yes

no

no

no

20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

Oceanographic variables and motion variable (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR2

Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no

Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no

Current 0096 -1382 -0133 0002 -0006 no

Heave range 0570 -0219 -0125 0000 -0002 no

Tilt Range 3811 0027 0105 0000 0001 no

Yaw increment range 8559 -0008 -0069 0002 -0003 no

Static tilt 0473 -0387 -0183 0002 -0008 no

- No sensitivities for oceanographic or platform motion variables

21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES

Classification ndash Final classification

Classification results for FINO1 campaign

-gt Most uncertainty comes from reference measurement uncertainty

22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES

Classification ndash Results

FLS (Windcube) FLS (ZXZephIR) 100m

For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified

Independant variableSensitivity

m x std

Sensitivit

y x RSensitive

[-] [] []

Significant wave height (buoy) -0101 -0001 no

Peak period Tp (Buoy) 0059 0001 no

Current -0133 -0006 no

Heave range -0125 -0002 no

Tilt Range 0105 0001 no

Yaw increment range -0069 -0003 no

Static tilt -0183 -0008 no

Independant variableSensitivity

m x std

Sensitivity

x rSensitive

[-] [] []

Significant wave height -0063 -0001 no

Peak period Tp (Buoy) -0191 -0007 no

Tm02 (radar) 0013 0000 no

Waterlevel -0069 0000 no

Heave range -0118 -0002 no

Tilt Range 0078 0000 no

Yaw increment range -0054 -0001 no

Static tilt 0075 0002 no

23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

Meteorological variables

FLS set-up

Oceanographic variables

Motion variables

Measurement

25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES

Resume

Verification and classification are important for the commercial acceptance of FLS

Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables

Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast

26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES

Federal Republic of Germany

Federal Ministry for Economic Affairs and Energy

Federal Ministry of Education and Research

European Regional Development Fund (ERDF)

Federal State of Bremen

Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH

Federal State of Lower Saxony

Free and Hanseatic City of Hamburg

Acknowledgements

Fraunhofer IWES is funded by

The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)

27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES

Thanks a lot for your attention

gerritwolken-moehlmanniwesfraunhoferde

28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES

References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain

Page 18: Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification are important for the commercial acceptance of FLS Both IWES FLS using Windcube

18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES 18copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

Are the variables independent correlations

19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable

See Barker Et al [6]

Considering

shear

no

no

yes

no

no

no

20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

Oceanographic variables and motion variable (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR2

Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no

Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no

Current 0096 -1382 -0133 0002 -0006 no

Heave range 0570 -0219 -0125 0000 -0002 no

Tilt Range 3811 0027 0105 0000 0001 no

Yaw increment range 8559 -0008 -0069 0002 -0003 no

Static tilt 0473 -0387 -0183 0002 -0008 no

- No sensitivities for oceanographic or platform motion variables

21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES

Classification ndash Final classification

Classification results for FINO1 campaign

-gt Most uncertainty comes from reference measurement uncertainty

22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES

Classification ndash Results

FLS (Windcube) FLS (ZXZephIR) 100m

For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified

Independant variableSensitivity

m x std

Sensitivit

y x RSensitive

[-] [] []

Significant wave height (buoy) -0101 -0001 no

Peak period Tp (Buoy) 0059 0001 no

Current -0133 -0006 no

Heave range -0125 -0002 no

Tilt Range 0105 0001 no

Yaw increment range -0069 -0003 no

Static tilt -0183 -0008 no

Independant variableSensitivity

m x std

Sensitivity

x rSensitive

[-] [] []

Significant wave height -0063 -0001 no

Peak period Tp (Buoy) -0191 -0007 no

Tm02 (radar) 0013 0000 no

Waterlevel -0069 0000 no

Heave range -0118 -0002 no

Tilt Range 0078 0000 no

Yaw increment range -0054 -0001 no

Static tilt 0075 0002 no

23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

Meteorological variables

FLS set-up

Oceanographic variables

Motion variables

Measurement

25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES

Resume

Verification and classification are important for the commercial acceptance of FLS

Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables

Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast

26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES

Federal Republic of Germany

Federal Ministry for Economic Affairs and Energy

Federal Ministry of Education and Research

European Regional Development Fund (ERDF)

Federal State of Bremen

Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH

Federal State of Lower Saxony

Free and Hanseatic City of Hamburg

Acknowledgements

Fraunhofer IWES is funded by

The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)

27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES

Thanks a lot for your attention

gerritwolken-moehlmanniwesfraunhoferde

28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES

References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain

Page 19: Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification are important for the commercial acceptance of FLS Both IWES FLS using Windcube

19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES 19copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

LiDAR Quality Parameter and meteorological variables (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR

2 Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

CNR signal quality 590 -011 -065 001 -006 yes

Wind shear exponent 011 -918 -097 012 -033 yes

Wind veer 013 -966 -121 004 -023 yes

Wind speed 316 -020 -062 006 -015 yes

Turbulence intensity Ti 227 036 081 005 018 yes

Temperature gradient 001 -10426 -110 001 -013 yes

- gt CNR shear wind speed Ti and the temperature gradient correlate- gt Veer is an independent variable

See Barker Et al [6]

Considering

shear

no

no

yes

no

no

no

20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

Oceanographic variables and motion variable (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR2

Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no

Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no

Current 0096 -1382 -0133 0002 -0006 no

Heave range 0570 -0219 -0125 0000 -0002 no

Tilt Range 3811 0027 0105 0000 0001 no

Yaw increment range 8559 -0008 -0069 0002 -0003 no

Static tilt 0473 -0387 -0183 0002 -0008 no

- No sensitivities for oceanographic or platform motion variables

21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES

Classification ndash Final classification

Classification results for FINO1 campaign

-gt Most uncertainty comes from reference measurement uncertainty

22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES

Classification ndash Results

FLS (Windcube) FLS (ZXZephIR) 100m

For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified

Independant variableSensitivity

m x std

Sensitivit

y x RSensitive

[-] [] []

Significant wave height (buoy) -0101 -0001 no

Peak period Tp (Buoy) 0059 0001 no

Current -0133 -0006 no

Heave range -0125 -0002 no

Tilt Range 0105 0001 no

Yaw increment range -0069 -0003 no

Static tilt -0183 -0008 no

Independant variableSensitivity

m x std

Sensitivity

x rSensitive

[-] [] []

Significant wave height -0063 -0001 no

Peak period Tp (Buoy) -0191 -0007 no

Tm02 (radar) 0013 0000 no

Waterlevel -0069 0000 no

Heave range -0118 -0002 no

Tilt Range 0078 0000 no

Yaw increment range -0054 -0001 no

Static tilt 0075 0002 no

23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

Meteorological variables

FLS set-up

Oceanographic variables

Motion variables

Measurement

25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES

Resume

Verification and classification are important for the commercial acceptance of FLS

Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables

Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast

26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES

Federal Republic of Germany

Federal Ministry for Economic Affairs and Energy

Federal Ministry of Education and Research

European Regional Development Fund (ERDF)

Federal State of Bremen

Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH

Federal State of Lower Saxony

Free and Hanseatic City of Hamburg

Acknowledgements

Fraunhofer IWES is funded by

The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)

27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES

Thanks a lot for your attention

gerritwolken-moehlmanniwesfraunhoferde

28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES

References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain

Page 20: Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification are important for the commercial acceptance of FLS Both IWES FLS using Windcube

20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES 20copy 2019 Fraunhofer IWES

Classification ndash Variable Sensitivity Results

Oceanographic variables and motion variable (selection)

Independant variable

std(Indepe

ndant

variable)

m (bin

Fit)

Sensitivity

m x stdR2

Sensitivity

x RSensitive

[-][unit

variable]

[ unit

variable][] [-] []

Significant wave height (buoy) 0721 -0140 -0101 0000 -0001 no

Peak period Tp (Buoy) 2289 0026 0059 0000 0001 no

Current 0096 -1382 -0133 0002 -0006 no

Heave range 0570 -0219 -0125 0000 -0002 no

Tilt Range 3811 0027 0105 0000 0001 no

Yaw increment range 8559 -0008 -0069 0002 -0003 no

Static tilt 0473 -0387 -0183 0002 -0008 no

- No sensitivities for oceanographic or platform motion variables

21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES

Classification ndash Final classification

Classification results for FINO1 campaign

-gt Most uncertainty comes from reference measurement uncertainty

22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES

Classification ndash Results

FLS (Windcube) FLS (ZXZephIR) 100m

For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified

Independant variableSensitivity

m x std

Sensitivit

y x RSensitive

[-] [] []

Significant wave height (buoy) -0101 -0001 no

Peak period Tp (Buoy) 0059 0001 no

Current -0133 -0006 no

Heave range -0125 -0002 no

Tilt Range 0105 0001 no

Yaw increment range -0069 -0003 no

Static tilt -0183 -0008 no

Independant variableSensitivity

m x std

Sensitivity

x rSensitive

[-] [] []

Significant wave height -0063 -0001 no

Peak period Tp (Buoy) -0191 -0007 no

Tm02 (radar) 0013 0000 no

Waterlevel -0069 0000 no

Heave range -0118 -0002 no

Tilt Range 0078 0000 no

Yaw increment range -0054 -0001 no

Static tilt 0075 0002 no

23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

Meteorological variables

FLS set-up

Oceanographic variables

Motion variables

Measurement

25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES

Resume

Verification and classification are important for the commercial acceptance of FLS

Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables

Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast

26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES

Federal Republic of Germany

Federal Ministry for Economic Affairs and Energy

Federal Ministry of Education and Research

European Regional Development Fund (ERDF)

Federal State of Bremen

Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH

Federal State of Lower Saxony

Free and Hanseatic City of Hamburg

Acknowledgements

Fraunhofer IWES is funded by

The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)

27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES

Thanks a lot for your attention

gerritwolken-moehlmanniwesfraunhoferde

28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES

References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain

Page 21: Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification are important for the commercial acceptance of FLS Both IWES FLS using Windcube

21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES 21copy 2019 Fraunhofer IWES

Classification ndash Final classification

Classification results for FINO1 campaign

-gt Most uncertainty comes from reference measurement uncertainty

22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES

Classification ndash Results

FLS (Windcube) FLS (ZXZephIR) 100m

For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified

Independant variableSensitivity

m x std

Sensitivit

y x RSensitive

[-] [] []

Significant wave height (buoy) -0101 -0001 no

Peak period Tp (Buoy) 0059 0001 no

Current -0133 -0006 no

Heave range -0125 -0002 no

Tilt Range 0105 0001 no

Yaw increment range -0069 -0003 no

Static tilt -0183 -0008 no

Independant variableSensitivity

m x std

Sensitivity

x rSensitive

[-] [] []

Significant wave height -0063 -0001 no

Peak period Tp (Buoy) -0191 -0007 no

Tm02 (radar) 0013 0000 no

Waterlevel -0069 0000 no

Heave range -0118 -0002 no

Tilt Range 0078 0000 no

Yaw increment range -0054 -0001 no

Static tilt 0075 0002 no

23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

Meteorological variables

FLS set-up

Oceanographic variables

Motion variables

Measurement

25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES

Resume

Verification and classification are important for the commercial acceptance of FLS

Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables

Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast

26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES

Federal Republic of Germany

Federal Ministry for Economic Affairs and Energy

Federal Ministry of Education and Research

European Regional Development Fund (ERDF)

Federal State of Bremen

Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH

Federal State of Lower Saxony

Free and Hanseatic City of Hamburg

Acknowledgements

Fraunhofer IWES is funded by

The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)

27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES

Thanks a lot for your attention

gerritwolken-moehlmanniwesfraunhoferde

28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES

References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain

Page 22: Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification are important for the commercial acceptance of FLS Both IWES FLS using Windcube

22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES 22copy 2019 Fraunhofer IWES

Classification ndash Results

FLS (Windcube) FLS (ZXZephIR) 100m

For both FLS systems no sensitivities to oceanographic or buoy motion variables could be identified

Independant variableSensitivity

m x std

Sensitivit

y x RSensitive

[-] [] []

Significant wave height (buoy) -0101 -0001 no

Peak period Tp (Buoy) 0059 0001 no

Current -0133 -0006 no

Heave range -0125 -0002 no

Tilt Range 0105 0001 no

Yaw increment range -0069 -0003 no

Static tilt -0183 -0008 no

Independant variableSensitivity

m x std

Sensitivity

x rSensitive

[-] [] []

Significant wave height -0063 -0001 no

Peak period Tp (Buoy) -0191 -0007 no

Tm02 (radar) 0013 0000 no

Waterlevel -0069 0000 no

Heave range -0118 -0002 no

Tilt Range 0078 0000 no

Yaw increment range -0054 -0001 no

Static tilt 0075 0002 no

23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

Meteorological variables

FLS set-up

Oceanographic variables

Motion variables

Measurement

25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES

Resume

Verification and classification are important for the commercial acceptance of FLS

Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables

Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast

26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES

Federal Republic of Germany

Federal Ministry for Economic Affairs and Energy

Federal Ministry of Education and Research

European Regional Development Fund (ERDF)

Federal State of Bremen

Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH

Federal State of Lower Saxony

Free and Hanseatic City of Hamburg

Acknowledgements

Fraunhofer IWES is funded by

The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)

27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES

Thanks a lot for your attention

gerritwolken-moehlmanniwesfraunhoferde

28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES

References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain

Page 23: Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification are important for the commercial acceptance of FLS Both IWES FLS using Windcube

23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES 23copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

Meteorological variables

FLS set-up

Oceanographic variables

Motion variables

Measurement

25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES

Resume

Verification and classification are important for the commercial acceptance of FLS

Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables

Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast

26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES

Federal Republic of Germany

Federal Ministry for Economic Affairs and Energy

Federal Ministry of Education and Research

European Regional Development Fund (ERDF)

Federal State of Bremen

Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH

Federal State of Lower Saxony

Free and Hanseatic City of Hamburg

Acknowledgements

Fraunhofer IWES is funded by

The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)

27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES

Thanks a lot for your attention

gerritwolken-moehlmanniwesfraunhoferde

28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES

References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain

Page 24: Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification are important for the commercial acceptance of FLS Both IWES FLS using Windcube

24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES 24copy 2019 Fraunhofer IWES

Classification ndash Shortcomings

Which variables are important ndash do we miss the important ones Bin-fitting process is not necessarily robust Use of motion instead of oceanographic variables for system with minor

design changes

Meteorological variables

FLS set-up

Oceanographic variables

Motion variables

Measurement

25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES

Resume

Verification and classification are important for the commercial acceptance of FLS

Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables

Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast

26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES

Federal Republic of Germany

Federal Ministry for Economic Affairs and Energy

Federal Ministry of Education and Research

European Regional Development Fund (ERDF)

Federal State of Bremen

Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH

Federal State of Lower Saxony

Free and Hanseatic City of Hamburg

Acknowledgements

Fraunhofer IWES is funded by

The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)

27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES

Thanks a lot for your attention

gerritwolken-moehlmanniwesfraunhoferde

28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES

References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain

Page 25: Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification are important for the commercial acceptance of FLS Both IWES FLS using Windcube

25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES 25copy 2019 Fraunhofer IWES

Resume

Verification and classification are important for the commercial acceptance of FLS

Both IWES FLS using Windcube or ZXZephIR show no sensitivities to motions or oceanographic variables

Method of classification (according to IEC) must be adapted for offshore due to more variableshellip which variables are important for a measurement sensitivity forecast

26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES

Federal Republic of Germany

Federal Ministry for Economic Affairs and Energy

Federal Ministry of Education and Research

European Regional Development Fund (ERDF)

Federal State of Bremen

Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH

Federal State of Lower Saxony

Free and Hanseatic City of Hamburg

Acknowledgements

Fraunhofer IWES is funded by

The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)

27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES

Thanks a lot for your attention

gerritwolken-moehlmanniwesfraunhoferde

28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES

References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain

Page 26: Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification are important for the commercial acceptance of FLS Both IWES FLS using Windcube

26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES 26copy 2019 Fraunhofer IWES

Federal Republic of Germany

Federal Ministry for Economic Affairs and Energy

Federal Ministry of Education and Research

European Regional Development Fund (ERDF)

Federal State of Bremen

Senator of Civil Engineering Environment and TransportationSenator of Economy Labor and PortsSenator of Science Health and Consumer ProtectionBremerhavener Gesellschaft fuumlr Investitionsfoumlrderung und Stadtentwicklung mbH

Federal State of Lower Saxony

Free and Hanseatic City of Hamburg

Acknowledgements

Fraunhofer IWES is funded by

The presented work was done in cooperation with Stiftungslehrstuhl Windenergie SWE Stuttgart within the research project MALIBU funded by the German Federal Ministry For Economics Affairs and Energy (BMWi) under Grant number 0324197B as well as the support of Project Management Juumllich (PTJ)

27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES

Thanks a lot for your attention

gerritwolken-moehlmanniwesfraunhoferde

28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES

References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain

Page 27: Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification are important for the commercial acceptance of FLS Both IWES FLS using Windcube

27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES 27copy 2019 Fraunhofer IWES

Thanks a lot for your attention

gerritwolken-moehlmanniwesfraunhoferde

28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES

References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain

Page 28: Dependence of Floating LiDAR Performance on External ... · Resume Verification and classification are important for the commercial acceptance of FLS Both IWES FLS using Windcube

28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES 28copy 2019 Fraunhofer IWES

References[1] Gottschall Et al Floating lidar as an advanced offshore wind speed measurement technique current technology status and gap analysis in regard to full maturity WIREs Energy Environment 2017 e250 doi 101002wene250[2] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 10 November 2013 [3] Carbon Trust Offshore Wind Accelerator Roadmap for the Commercial Acceptance of Floating LiDAR Technology Version 20 October 2018 [4] IEA Wind Expert Group Report on Recommended Practices 18Floating LiDAR Systems First Edition 2017 O Bischoff I Wuumlrth J Gottschall B Gribben J Hughes D Stein H Verhoef[5] IEC 61400-12-12017 Wind energy generation systems -Part 12-1 Power performance measurements of electricity producing wind turbines Annex L The application of remote sensing technology[6] Barker et Al Correlation effects in the field classification of ground based remote sensors Conference paper EWEA 2014 Barcelona Spain