improved wind turbine efficiency using synchronized sensorsdtu wind energy, technical university of...

Improved Wind Turbine Efficiency using Synchronized Sensors

26/03/2015

Uwe Schmidt Paulsen [email protected]

Oscar Moñux

Claus Brian Pedersen

Karen Enevoldsen

DTU Wind Energy, Technical University of Denmark

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Project objective & overview

To improve the efficiency of wind turbine and wind turbine farms using synchronized sensors on wind turbines, their wings, and in wind fields. The technology is used in development, test, modeling, and active control of both wind turbines and wind turbine farms, thus optimizing their efficiency, life span, durability,

and noise emissions while lowering production costs and increasing reliability.

Demonstration of: 1) an inflow wind measurement sensor suitable for wing mounting, and 2) a lightweight, electronic device “SyncBoard” providing precision transducer

synchronization and A/D conversion, data storage, and communications.

13 June 2016

EUDP aerial sensor



Project objective & overview

To improve the efficiency of wind turbine and wind turbine farms using synchronized sensors on wind turbines, their wings, and in wind fields. The technology is used in development, test, modeling, and active control of both wind turbines and wind turbine farms, thus optimizing their efficiency, life span, durability, and noise emissions while lowering production costs and increasing reliability.

Demonstration of: 1) an inflow wind measurement sensor suitable for wing mounting, and 2) a lightweight, electronic device “SyncBoard” providing precision transducer

synchronization and A/D conversion, data storage, and communications.

13 June 2016

EUDP aerial sensor

Design calibration testing 500kW WT final reporting

Meetings month 6 12 18..

2012-09-17 2015-09-17



What is “inflow” sensor

• A robust transducer which measures the instantaneous inflow

• Flow correlated with other signals- synchronized in time (e.g. trailing edge noise, electrical power)

• Have capabilities to measure statistics

• Adds on: rotor blade azimuth angle & speed

• Indicates yaw error, no-good rotor blade settings..

• Nice:

– Insensitive to hazards, Lightning, Rain/impact

– Cheap technology

Siemens 2.3MW

HAAM

13 June 2016

EUDP aerial sensor

1990



What is “inflow” sensor

• A robust transducer which measures the instantaneous flow in front of the rotor blade

• Flow correlated with other signals- synchronized in time (e.g. trailing edge noise, electrical power)

• Have capabilities to measure statistics

• Adds on: rotor blade azimuth angle & speed

• Indicates yaw error, no-good rotor blade settings..

• Nice:

– Insensitive to hazards, Lightning, Rain/impact

– Cheap technology

Siemens 2.3MW

HAAM

13 June 2016

EUDP aerial sensor



What is required Solution

• Technology: dynamic pressure to wind speed

• Datalogger:processor & add-on transducers

• WiFi

• Accuracy& precision

– σ WS ±0.5m/s-±1m/s, angle<0.1°

• Operating range -25 m/s, -10-+40°

6 13 June 2016

• 5-hole pitot(MHP -pneumatic)

• ‘Synch board’, Rasperry pi..

• Wireless technology

• UAV presission(20-40 ms-1)

– σ WS 0.02 m/s angle 0.01°

• Wind tunnel calibration (90 m/s)

• Weather proof(sealed, ventilated)

Competitive technologies Nacelle/spinner Lidar LIDIC MEMS: combining several functionalities(acoustic, optical, vibrational, thermal, WIFI) into small space at low power consumption • KuLIte absolute pressure transducers

new technology $$$$




• Technology:dynamic pressure to wind speed

• Datalogger with processor & add-on

• WiFi


– σ WS ±0.5%-±1%, angle<1o

7 13 June 2016

• 5-hole pitot(pneumatic)




Competitive technologies Nacelle Lidar LIDIC MEMS: combining several functionalities(acoustic, optical, vibrational, thermal, WIFI) into small space and low power consumption • KuLIte absolute pressure transducers

$$$$






• WiFi


– σ WS ±0.5%-±1%, angle<1o

8 13 June 2016






$$$$






• WiFi


– σ WS ±0.5%-±1%, angle<1o

9 13 June 2016






$$$$






• WiFi


– σ WS ±0.5%-±1%, angle<1o

10 13 June 2016






$$$$

MEMS Silicone pressure transducer(SensorTechnics)

Thin Line Pressure Transducer KuLite

KuLite FAP-250 (2016)






• WiFi


– σ WS ±0.5%-±1%, angle<1o

11 13 June 2016






$$$$

MEMS Silicone pressure transducer(SensorTechnics)

Thin Line Pressure Transducer KuLite

KuLite FAP-250 (2016)

SONIC(Cambell)



Transducer replacement for traditional sensors:

12 13 June 2016

Strain gauge

Power

List of sensors: System 1 Aerial board - Strain gauges - GPS System 2 Aerial board

- Wind turbine power - GPS System 3: Aerial board - Pitot with pressure transducers - Temperature sensor - GPS



Position of pitot

13 13 June 2016

undisturbed

disturbed



Project goals/what we worked on

14 13 June 2016

1

2

3

4




15 13 June 2016

1

2

3

4

DELTA SYNCHBOARD



History First Prototype(34x25x4 cm)

16 13 June 2016

GPS Antenna

ADIS (3D gyro, 3D acceleration, 3D magnetometer)

GPS Board 3x4 Ch NI DAQ modules FPGA processor

NAS

Power supply



Aerial board NI Prototype trial

17 13 June 2016

Pressure Transducers

I2c

High reduction of Size and

weight: 2.55Kg

FPGA

GPS




18 13 June 2016

1

2

3

4

Blade mounting




19 13 June 2016


I2c


weight: 2.55Kg

FPGA

GPS




20 13 June 2016


I2c


weight: 2.55Kg

FPGA

GPS Easy mounting O&M



Pitot 1st measurement campaign

21 13 June 2016

- combining online signals data base from Nordtank system and Pitot system

- GPS worked. Synchronization Turbine -aerial sensor was not used




22 13 June 2016

1

2

3

4

Power Supply/energy harvesting



Energy harvesting

• Different prototypes. Two different approaches:

– Solar panels: flexible up to X m^2

– Propellers: several tested:

• Power

• Noise Emission

23 13 June 2016



Harvesting power from wind

24 13 June 2016




25 13 June 2016

1

2

3

4 Calibration: wind tunnel



Set of pre-tests:

• Main goal: calibrate the blower and get the best design

• Assessment of the wind profile – Calibration test performed with a laser scanner

• Test 1. 85 cm from the outlet

• Test 2. Center and adapters outlet

• Test 3 to 5. Profiles at the adapters outlet

– Calibration without the Aluminum transition piece

• Test 6. Profiles of the blower outlet

26 13 June 2016

Laser

10%



Set of pre-tests:

• Main goal: calibrate the blower and get the best design

• Assessment of the wind profile – Calibration test performed with a laser scanner

• Test 1. 85 cm from the outlet

• Test 2. Center and adapters outlet

• Test 3 to 5. Profiles at the adapters outlet

– Calibration without the Aluminum transition piece

• Test 6. Profiles of the blower outlet

27 13 June 2016

Laser



Calibration Blower

• Need to develop our blower for calibrating the measurement devices at high speeds (85m/s)

• The calibration methodology will be developed for Ma around 0.3

28 13 June 2016



Stability

29 13 June 2016

Distance to Mark

blower in cm -4 -3 -2 -1 0 1 2 3 4 5 6 traverse

-0.58 25

1.96 24

4.5 X X X X X X X X X 23

7.04 22

9.58 21

12.12 20

14.66 19

17.2 18

19.74 17

22.28 16

24.82 15

27.36 14

prandtl

pitot 13.5 12.5 11.5 10.5 9.5 8.5 7.5 6.5 5.5 4.5

• Uniform, symmetrical wind profile over exit

• Turbulence intensity 0.15% at 90 m/s

• Jet core down stream ~5D

• Traversing the core area at different lateral and transverse positions

• Tube vibrating-reinforcement with annular tube



Calibration results

30 13 June 2016

Assessment of the wind profile of the fan Calibration test performed to 50Hz with Furness Calibration test performed to 77Hz

0 2 4 6 8 10 12 14 16

x 104

-5

0

5

10

15

20

25

30

35

40

45

data1

Speed increasing



Characteristics-Roll-plane

31 13 June 2016

20°

-20°



Characteristics-Pitch-plane

32 13 June 2016

20°

-20°

20°



33 13 June 2016

•Depending on Calpha or Cbetha the angle will be determined and the pressures will be accordingly corrected

Correct the pressures P16 , P45, P23,

P6

•Pressures and Temperatures needed

•Formulae will be described Obtain Wind speed in pitot

•Formulation on the triangle of speeds Correct the speed at the pitot head

on a space point

•Correction of the azimuth angles, accelerations.. Get the real speed Vfree

•from here it is possible to determine the power performance Extrapolate the speeds to a

reference speed umean

𝑢 = 𝛾 ∙ 𝑅 ∙𝑇𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

1𝑀𝑎2 1 +

12 ∙ 𝛾 − 1 ∙ 𝐾 ∙ 𝑀𝑎2

= 𝛾 ∙ 𝑅 ∙𝑇𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

1𝑀𝑎2 +

12 ∙ 𝛾 − 1 ∙ 𝐾

Ma = vlocal/cair ~0.3 K recovery factor 0.7-0.9



Uncertainty

• 𝜁𝑥,𝑦,𝑧,𝑓𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦 =𝑞𝑝𝑟𝑎𝑛𝑑𝑡𝑙

𝑞𝑐𝑜𝑛𝑡𝑟𝑎𝑐𝑡𝑖𝑜𝑛𝑥, 𝑦, 𝑧, 𝑓𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦 + 𝜁𝑜𝑓𝑓𝑠𝑒𝑡

• 𝒖𝜻𝟎,𝟎,𝟏.𝟓𝑫,𝒇𝒓𝒆𝒒𝒖𝒆𝒏𝒄𝒚

𝟏 = 𝟎. 𝟎𝟎𝟔

• 𝑢𝛼𝐶𝑖2 =

𝜕𝛼𝐶𝑖

𝜕𝑞𝑝𝑖𝑡𝑜𝑡

2

∙ 𝑢𝑞𝑝𝑖𝑡𝑜𝑡2 +

𝜕𝛼𝐶𝑖

𝜕𝑞𝑙𝑎𝑡𝑒𝑟𝑎𝑙

2∙ 𝑢𝑞𝑙𝑎𝑡𝑒𝑟𝑎𝑙

2 +𝜕𝛼𝐶𝑖

𝜕𝑥

2∙ 𝑢𝑥

2 +𝜕𝛼𝐶𝑖

𝜕𝑦

2∙ 𝑢𝑦

2 +𝜕𝛼𝐶𝑖

𝜕𝑧

2∙ 𝑢𝑧

2 +𝜕𝛼𝐶𝑖

𝜕𝜓

2∙ 𝑢𝛼

2 + 2 ∙

𝜕𝛼𝐶𝑖

𝜕𝑞𝑝𝑖𝑡𝑜𝑡∙

𝜕𝛼𝐶𝑖

𝜕𝑞𝑙𝑎𝑡𝑒𝑟𝑎𝑙∙ 𝑢𝑞𝑝𝑖𝑡𝑜𝑡

1 ∙ 𝑢𝑞𝑙𝑎𝑡𝑒𝑟𝑎𝑙1 − 2 ∙

𝜕𝛼𝐶𝑖


𝜕𝛼𝐶𝑖

𝜕𝑥∙ 𝑢𝑞𝑝𝑖𝑡𝑜𝑡

1 ∙ 𝑢𝑥1 − 2 ∙

𝜕𝛼𝐶𝑖


𝜕𝛼𝐶𝑖

𝜕𝑦∙ 𝑢𝑞𝑝𝑖𝑡𝑜𝑡

1 ∙ 𝑢𝑦1 − 2 ∙

𝜕𝛼𝐶𝑖


𝜕𝛼𝐶𝑖

𝜕𝑧∙ 𝑢𝑞𝑝𝑖𝑡𝑜𝑡

1 ∙ 𝑢𝑧1 + 2 ∙

𝜕𝛼𝐶𝑖

𝜕𝑞𝑙𝑎𝑡𝑒𝑟𝑎𝑙∙

𝜕𝛼𝐶𝑖

𝜕𝑥∙ 𝑢𝑞𝑙𝑎𝑡𝑒𝑟𝑎𝑙

1 ∙ 𝑢𝑥1 + 2 ∙

𝜕𝛼𝐶𝑖


𝜕𝛼𝐶𝑖

𝜕𝑦∙ 𝑢𝑞𝑙𝑎𝑡𝑒𝑟𝑎𝑙

1 ∙ 𝑢𝑦1 + 2 ∙

𝜕𝛼𝐶𝑖


𝜕𝛼𝐶𝑖

𝜕𝑧∙ 𝑢𝑞𝑙𝑎𝑡𝑒𝑟𝑎𝑙

1 ∙ 𝑢𝑧1 + 2 ∙

𝜕𝛼𝐶𝑖


𝜕𝛼𝐶𝑖

𝜕𝜓∙ 𝑢𝑞𝑙𝑎𝑡𝑒𝑟𝑎𝑙

1 ∙ 𝑢𝜓1 − 2 ∙

𝜕𝛼𝐶𝑖


𝜕𝛼𝐶𝑖

𝜕𝜓∙ 𝑢𝑞𝑝𝑖𝑡𝑜𝑡

1 ∙ 𝑢𝜓1

• 𝑢𝛼2 = 468.67 ∙

𝜋∗𝜋

180∗180

0.043

38.182 +0.199

5.482 +6.25𝑒−6

5.48

38.18

2 − 2 ∙0.207

38.18∙

0.199

5.48− 2 ∙

1

5.48∙

0.199∙0.0025 5.48

38.18

+ 2 ∙0.207

38.18∙ 0.0025 + 2 ∙

𝜋∗𝜋

180∗180∙ 73.438 ∙

5.48

38.18

1∙ 0.0206

2

∙ 0.01 − 43.30 ∙𝜋∗𝜋

180∗180∙

0.199

5.48+

0.207

38.18∗ 73.438 ∙

5.48

38.18

1∙ 0.0412 ∙ 0.1 =

0.000737 𝑟𝑎𝑑2

• This is equivalent to 1.5°in Roll which as being the maximum will be considered as the uncertainty in that direction

34 13 June 2016




35 13 June 2016

1

2

3

4

Testing 500 kW NTK



Pitot measurement campaign of 2 hours. Some results.

36 13 June 2016

Absolute pressure

Pitot Pressures

Cut-in accelerations triangle

Raw signal



Status Next Actions

37 13 June 2016

• To do

– turbine test (noise & inflow) range:days

– systems calibration in wind tunnel

• New applications-Blade sensor?

– Need for board with all functional requirements as per project (4 HS channels, 8 AD channels, functional & remote data transmission)

– Student projects for progress development?

– PhD associated in the use of blade sensor

• Board upgrade

– Faster, more powerfull processor

– More signals

• Dissemination

– Article: the story and the results

• Hands on 5-hole Pitot(MHP)

– Hands-on pneumatic MEMS transducers

– R&D on response

– σWS ~ 0.18 ms-1 (60 ms-1) σ angle 1.5°

– Installed on 500 kW WT

– Tests (inflow) range:hours

– Cost ~ 250+400 dkk

• Systems calibration in wind tunnel

– Experience on uncertainty

• New wind tunnel

– 90 m/s wind speed

– Reasonable turbulence

– Symmetrical wind speed profile

• NI board experience

– Lab model



Perspectives

• Wireless technology/remote data transmission

– online user interaction( rotor blades and - power checks)

– Electrical(Power curve), structural (loads, dynamics) measurements

– MEMS technology integration with multiple functionalities

• Noise measurements

– System installed on 2 rotor blades or more, windfarms

• Other applications

– Horizontal-axis wind turbines(O&M)

– Vertical-axis wind turbines(Power, O&M..)

– Civil engineering(bridges)

– Signals correlation in new context(optical, ..)

38 13 June 2016



Conclusion

39 13 June 2016

Prototype synchBoard

commercial

+ power curve



Thank you

40 13 June 2016

improved wind turbine efficiency using synchronized sensorsdtu wind energy, technical university of...

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