Wind Turbine-Radar Interference and Opportunities at the ARM Southern Great Plains Site: Identification, Characterization, and Mitigation

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Bradley Isom

Radar Engineer

ARM Climate User Facility

Electromagnetic Waves and Wind Turbines – 2018 Delft

Review of Previous EMWT Symposiums

State of Wind Turbine Clutter Mitigation Efforts – Weather Radar

ARM Climate Research Facility Overview

ARM Radar Program Overview

Examples of WTC at the Southern Great Plains Site

Opportunities

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"#$% & "#$' (

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3 2016 Coordinated by Fraunhofer Institute for High

Frequency Physics and Radar Techniques (FHR)

Christoph Wasserzier, Frank Weinmann, and Josef Worms

“To bring together the increasing number of experts

and researchers in the field of electromagnetic

waves within the context of wind energy plants and

to offer a platform to discuss arising and existing

problems”

2017 Coordinated by Physikalisch-Technische

Bundesanstalt (PTB)

Vincenza Kramer and Thorsten Schrader

"#$% & "#$' (

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Covered a range of topics

UAV measurements of wind turbine impacts

Modeling wind turbine impacts and interference

Impacts on birds and bats

Radar measurements and mitigation

Impact on radio astronomy

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"#$% & "#$' (

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5 Radar signal characterization/degradation measurements

over wind farms using UAVs and ground measurements

(WERAN: Schrader, EinfÅhrung, Mihalachi, Bredemeyer, Sandmann)

"#$% & "#$' (

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Modeling the impact of wind

turbines on EM signals

Assessment tools for

impact and mitigation

Electromagnetic scattering

models

Weinmann

_PERSEUS

Van Gent

(4)

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7 Modeling wind turbine EM interactions

and simulations

Krasnov

Medagli

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Ecological impacts of wind turbines: detecting birds and bats

Real-time detection and classification (BirdScan)

Sensor recommendations for discriminating birds/WTs

Liechti

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9 Radar measurements and mitigation

_{Jiapang Yin}

-PARSAX

Evaristo

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Uysal

Norin - SMHI

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11 Radio astronomy and wind turbine interference

B. Winkel

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13 Several avenues to explore for WTC mitigation

Wind turbine siting

Minimize contamination by strategically locating wind

turbines relative to radars

Use models to identify impact prior to construction

Wind turbine materials

Use EM absorbing materials to reduce the influence of

the blades on the scattered signal

Signal processing techniques

Not a simple endeavor

Three components: characterization, identification, and

mitigation

Characterization

Allows for better understanding of the clutter, generating models and/or thresholds

Provides a baseline for identification Identification

Dynamic masking is useful for strong target signals and anomalous

propagation

Provides a baseline for mitigation Fuzzy Logic/PDF Examples:

Hood et al (2010) Seo et al (2015) Hall et al (2017)

CPA – Clutter Phase Alignment Spectral Flatness

4th_-moment

Hub-to-weather Ratio

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15 Mitigation is the ultimate goal

Use in conjunction with

identification algorithm to focus

computational efforts (time

series, spectral) or ‘clean’ gates

Several techniques

Isom et al. (2009) – Interpolation Zheng et al. (2011) and Perfetti et al. (2012) –

Identification+Interpolation

Nai et al. (2013) – range-Doppler regression

More ->

Isom (2009) Nai (2013)

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Techniques continued:

Uysal et al. (2016) – Time/Frequency sparsity

Beauchamp and Chandrasekar (2017) – data-derived model-based mitigation

Norin (2017) – pattern matching and interpolation

Where can we test these

algorithms?

ARM Southern Great Plains

Use multiple radars to

simultaneously collect WTC data

Utilize frequency diversity

Long-term data collection

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Research sites – permanent, mobile, and aerial

Instruments and measurements

Field campaigns – ground-based, ship-based, airborne

Data processing, data quality, data archive

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Southern Great Plains (1993)

North Slope of Alaska: Barrow (1998) and Atqasuk (1999)

Tropical Western Pacific: Manus (1996), Nauru (1998), and Darwin (2002) First ARM Mobile Facility (2005); Second ARM Mobile Facility (2010) ARM Aerial Facility (2007)

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ARM is underwent a reconfiguration to

better integrate observations and

high-resolution modeling with the continued

goal of improving climate models. The

reconfiguration has three main facets:

Optimization of the ARM measurement facilities at two megasites to better support high-resolution modeling

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Development of a routine modeling strategy for process studies and to provide a link to Global-scale models

Development of a data processing strategy to bridge measurements and models

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Science questions raised through the May 2014 high-resolution modeling workshop for the SGP megasite focused on five main themes:

Shallow convection Deep convection Aerosols

Radiation

Land surface and carbon cycle

Ground instrumentation, including radars, will be used to: Provide initial conditions and forcing data to the models Provide data for assimilation into models

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Baseline Capabilities

Cloud property profiles and 3-D measurements: radar and lidar

T/RH/Wind profiles: radiosondes Column water: microwave radiometer Column aerosol: solar spectral radiometer In situ aerosol optical and cloud nucleation properties

Enhanced measurements atmospheric aerosol absorption, scattering, composition and chemistry Profiles of humidity and vertical motion

Expanded capabilities for airborne measurements

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Data Management Facility External Data Center General Scientific Community Southern Great Plains North Slope of Alaska Mobile Facility 1, 2, 3 Field Campaigns and Aircraft Eastern North Atlantic

Data from over 300 instruments are processed to standard (netCDF) format and to higher order products Products are then reviewed for completeness and quality

Data Quality Assessment

Data Discovery

Tool

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Data are available for use by anyone through the ARM archive.

A new Data Discovery browser for finding and ordering data is now available at:

adc.arm.gov

Resources and guidance for using and visualizing data are also available.

Data Archive:

Collects approximately 1 TB/day Each month approximately 9 TB are

requested by 150 users from most states and around the world

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Data access

Field campaigns and facility deployments

Data product requests

Feedback for new capabilities

Individuals become ARM science users through several processes including successful field campaign proposals, successful proposals to use ARM computing facilities, and through peer-reviewed science projects that involve the use of ARM data.

Science users interact with the ARM Facility in several ways:

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Radars 33 radars

8 scanning cloud radars (2 independent radars each) – Ka+W/X

2 zenith pointing cloud radars - W 7 zenith pointing cloud radars - Ka 5 scanning precipitation radars - X 3 scanning precipitation radars – C 4 different vendors

3 fixed sites and 3 mobile facilities

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Scanning Cloud Radars

Ka band

2kW EIKA

Tx: H, Rx:H+V

0.3-degree beamwidth

W band

1.6kW EIKA

Tx:H, Rx:H+V

0.3-degree beamwidth

29 Zenith Cloud Radars

Ka band

150W TWTA

Pulse compression

Tx: H, Rx:H+V

0.19-degree beamwidth

W band

1.6kW EIKA

Pulse compression

Tx:H, Rx:H+V

0.18-degree beamwidth

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Scanning Precipitation Radars

C band (NE)

350kW magnetron

Tx:H+V, Rx:H+V

1-degree beamwidth

X band

3 radars (NW, SW, SE)

200kW magnetron

Tx:H+V, Rx:H+V

1-degree beamwidth

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1 7 radars

X-bands (3)

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1

33 7 radars

X-bands (3)

C-band

1 7 radars

X-bands (3)

C-band

Ka/W

KAZR

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35 Significant number

(564) of wind turbines

surrounding the SGP

site

2016

+ 2

Many of the turbines

fall within a 30km

radar field of view

(FOV)

(20)

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37 Many more (713)

turbines were

proposed within the

FOV

Clear view over CF is

compromised with

proposed outlook

2016

+ 2

Some (428) of the

proposed turbines

were actually built

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39 Some (428) of the

proposed turbines

were actually built

Growth seems to be

slowing (only 100

more proposed)

2018

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Wind farms will

impact the modeling

efforts planned for

SGP

Boundary layer

interactions

Model validations

Data assimilation

A significant portion

of the model domain

is compromised

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41 Radars play an

important role in the

modeling efforts

Providing initial

conditions

Data assimilation

Validation

Radiative forcing in particular can be

greatly affected

Relies on accurate classification of

sub-storm structures, particularly

between the anvil and convective

cores

Feng et al. (2012)

3 *

-Impact on moment

data is observed at

all three frequencies

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3 *

-43

Impact on moment

data is observed at

all three frequencies

X-band

I4 – 0.5 el

3 *

-Impact on moment

data is observed at

all three frequencies

X-band

(24)

3 *

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Impact on moment

data is observed at

all three frequencies

X-band

I4 – 1.1 el

3 *

-Impact on moment

data is observed at

all three frequencies

X-band

(25)

3 *

-47

Impact on moment

data is observed at

all three frequencies

X-band

I4 – 5.3 el

3 *

-Impact on moment

data is observed at

all three frequencies

X-band

(26)

3 *

-49

Impact on moment

data is observed at

all three frequencies

X-band

I4 – 11.7 el

3 *

-Impact on moment

data is observed at

all three frequencies

(27)

3 *

-51

I6

Impact on moment

data is observed at

all three frequencies

X-band

3 *

-I6

Impact on moment

data is observed at

all three frequencies

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3

53 I6 XSAPR

Closest turbine

128 samples (256

padding)

Hamming window

Tower

Blades

Hub

3 I6 XSAPR

Second turbine

128 samples (256

padding)

Hamming window

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*

-55

Impact on moment

data is observed at

all three frequencies

X-band

C-band

*

-Impact on moment

data is observed at

all three frequencies

X-band

C-band

(30)

*

-57

Impact on moment

data is observed at

all three frequencies

X-band

C-band

CSAPR

64 samples (512

padding)

Hamming window

(31)

4 *

-59

Impact on moment

data is observed at

all three frequencies

X-band

C-band

Ka-band

4 *

-Impact on moment

data is observed at

all three frequencies

X-band

C-band

Ka-band

(32)

4

61 Ka-SACR

512 samples

Blackman-Harris

window

• Isom, B. M., et al. (2009). "Detailed Observations of Wind Turbine

Clutter with Scanning Weather Radars." Journal of Atmospheric and

Oceanic Technology, 26(5), 894-910.

• Hood, K., et al. (2010). "Automatic Detection of Wind Turbine

Clutter for Weather Radars." Journal of Atmospheric and Oceanic

Technology, 27(11), 1868-1880.

• Feng, Z., et al. (2011). "Top-of-atmosphere radiation budget of

convective core/stratiform rain and anvil clouds from deep

convective systems." Journal of Geophysical Research-Atmospheres

116, .

• Zheng, J., et al. (2011). “Azimuth-frequency analysis for wind farm

clutter identification and mitigation in doppler weather radar”. 35th

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63 • Perfetti, B., et al. (2012). “Signal processing for wind turbine

interference mitigation in doppler weather radars: Data synthesis,

clutter detector performance, and spectral interpolation in

range-azimuth-doppler”. Radar Systems (Radar 2012), IET International

Conference on, IET, 1–6.

• Nai, F., et al. (2013). “On the mitigation of wind turbine clutter for

weather radars using range-doppler spectral processing”. IET Radar,

Sonar & Navigation, 7 (2), 178–190.

• Seo, B-C., et al. (2015). “Using the new dual-polarimetric capability

of WSR-88D to eliminate anomalous propagation and wind turbine

effects in radar-rainfall”. Atmospheric Research, 153, 296-309.

• Hall, W., M. Rico-Ramirez, S. Kramer, 2016: Offshore wind turbine

clutter characteristics and identification in operational C band

weather radar measurements. Quarterly Journal of the Royal

Meteorological Society, 143 (703), 720-730.

• R. M. Beauchamp and V. Chandrasekar (2017), "Suppressing Wind

Turbine Signatures in Weather Radar Observations," IEEE

Transactions on Geoscience and Remote Sensing, vol. 55, no. 5, pp.

2546-2562.

• Uysal, F., et al. (2016). "Mitigation of Wind Turbine Clutter for

Weather Radar by Signal Separation." IEEE Transactions on

Geoscience and Remote Sensing 54(5), 2925-2934.

• Norin, L. (2017) “Wind turbine impact on operational weather radar

I/Q data: characterization and filtering.” Atmospheric Measurement

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Wind turbines pose a serious problem for the ARM SGP site

Model initiation and validation efforts are subject to biases and misclassifications due to WTC

Opportunities with the ARM SGP Site

Explore other impacts of wind farms on boundary layer interactions Long-term changes in local climate

Wind farm wake field and power considerations

Multi-radar, multi-frequency synergy for exploring identification and mitigation efforts

Currently working toward characterization with an ultimate goal of mitigation Exploring existing identification and mitigation algorithms

Looking for partnerships to test viability of latest techniques! Data can be made available upon request