High Reliability Monitoring and Control of Wind Turbines Peter - - PowerPoint PPT Presentation

AEROSPACE ENGINEERING AND MECHANICS

High Reliability Monitoring and Control

• f Wind Turbines

Peter Seiler Department of Aerospace Engineering & Mechanics University of Minnesota

AEROSPACE ENGINEERING AND MECHANICS

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Turbine Components

Figure from the US DOE Eolos Field Station

AEROSPACE ENGINEERING AND MECHANICS

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• 1. Maximize captured power
• 2. Minimize structural loads
• 3. Reduce operational downtime

Performance Objectives

p

C Av P

3 2 1 



Power in Wind Power Coefficient: Function of turbine design, wind conditions, and control

AEROSPACE ENGINEERING AND MECHANICS

Outline

4

• 1. Overview of UMN

/ Eolos Research

• 2. Redundancy Management

in Commercial Aviation

• 3. Blade health monitoring

using energy harvesting

• 4. Conclusions…

AEROSPACE ENGINEERING AND MECHANICS

Eolos Consortium

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Triaxial DC Accels Fiber-optic Strain LP HP/LP HP/LP/LE/TE LP/ HP

Established via US DOE Grant http://www.eolos.umn.edu/ Wind Field Station 2.5MW / 96M Clipper Liberty (Commissioned on 10/25/2012)

UMN Field Station ~25mi

AEROSPACE ENGINEERING AND MECHANICS

Collaboration with Mesabi Range CTC

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~190mi UMN MRCTC

96m 80m 27m 31m 36.6m 40m

Liberty C96 (Eolos) 225kW Vestas V27 (Mesabi Range) CART3 (NREL)

Mesabi Range CTC

Wind Energy Technology Program

• ffers A.A.S. degree for maintenance
• f utility scale wind turbines.

(V27 shipped from Antwerp on 9/28/2010)

AEROSPACE ENGINEERING AND MECHANICS

Overview of Research Projects

7

1.31 1.32 1.33 1.34 1.35 1.36 1.37 1.38 x 10

6 6.5 7 7.5 8 10 min Averaged Wind Speed Time (s) 10 min Averaged Wind Speed (m 8.2 8.4 8.6 8.8 9 9.2 9.4 9.6 0.2 0.25 0.3 Clipper C96 Cp Curve for beta=1.25 Tip Speed Ratio Cp

V27 Control (Thorson, Janisch) Blade Health Monitoring (Lim, Mantell, Yang) Distributed Estimation (Showers) Wind Farm Control (Annoni, Yang, Sotiropolous, Bitar) Active Power Control (Wang) Multivariable Design Tools (Ozdemir, Escobar Sanabria, Balas)

AEROSPACE ENGINEERING AND MECHANICS

V27 Control Design

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Accomplishments:

• Mesabi Range rewired turbine, removed stock

controller and installed Master/Slave CRIOs

• UMN designed turbine state logic and rotor

speed tracking. Future: Fixed speed power generation References:

• Vestas V27 Test, Petersen, 90
• CART Commissioning, Fingersh/Johnson 02, 04

AEROSPACE ENGINEERING AND MECHANICS

Wind Farm Modeling and Control

Objectives:

• Develop control-oriented models
• Design control laws for increased power capture and load

mitigation (Bitar, Seiler, ‘13 ACC)

Simulators:

• Saint Anthony Falls Virtual Wind Simulator (Yang, Kang,

Sotiropoulos 2012; Chamorro, Porte-Agel 2011)

• NREL SOWFA (Churchfield, Lee, Michalakes, Moriarty, 2012)

Selected References:

• Jensen, ‘83 Risø Report
• Steinbuch, de Boer, Bosgra, Peters, Ploeg, ‘88 JWEIA
• Johnson, Thomas, ‘09 ACC
• Pao, Johnson, ‘09 ACC
• Brand, Soleimanzadeh, 11 EWEA
• Marden, Ruben, Pao, ‘12 ASM
• Wagenaar, Machielse, Schepers, 12 EWEA
• Fleming, Gebraad, van Wingerden, Lee, Churchfield, Scholbrock,

Michalakes, Johnson, Moriarty, ‘13 EWEA

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SAFL Wind Tunnel Tests (Chamorro, Porte-Agel)

AEROSPACE ENGINEERING AND MECHANICS

CFD Results

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Decreasing Lead Turbine Induction Factor

Park Model (Jensen, ‘83):

 

2 2

2 where ) 1 (

kx D D

a v v v v

 

    

Simulation: Turbine Located at x=0.5 Park model fit shown with k=0.01

Summary: Opportunity to optimize total power output but validated control-oriented models are needed.

Ptot = 0.3834 Ptot = 0.3888 Ptot = 0.3726

AEROSPACE ENGINEERING AND MECHANICS

Active Power Control

Objectives:

• Use gain-scheduling to track arbitrary power set-

point commands (Wang, Seiler, ‘13 Draft)

• Investigate feasibility for ancillary services

Selected References:

• Kirby, Dyer, Martinez, Shoureshi, Guttromson,

‘02 Oak Ridge Report

• Keung, Li, Banakar, Ooi, ‘09 TPS
• Juankorena, Esandi, Lopez, Marroyo, ‘09 CPEEED
• Spudić, Jelavić, Baotić, Perić, ‘10 Torque
• Tarnowski, Kjaer, Dalsgaard, Nyborg, ‘10 PES
• Laks, Pao, Wright, ‘12 ACC
• Aho, Buckspan, Pao, Fleming, ‘13 ASM
• Jeong, Johnson, Fleming, ‘13 WE

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AEROSPACE ENGINEERING AND MECHANICS

Multivariable Control Design

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Objective:

• Develop a framework to easily tune advanced

(robust) control designs for wind turbines (Ozdemir, ‘13 PhD)

• Integrate advanced sensors (LIDAR) for preview

control (Ozdemir, Seiler, Balas, ‘12 ASM, ‘12 ACC, ‘13 ASM, ‘13 TCST)

• Optimal Multi-Blade Coordinate Transformation

(Seiler, Ozdemir, ‘13 ACC)

Selected (LIDAR) References:

• Harris, Hand, and Wright, ’06 NREL Report
• Laks, Pao, Wright, ‘09 ASM
• Mikkelsen, Hansen, Angelou, Sjöholm, Harris, Hadley,

Scullion, Ellis, Vives, ‘10 AWEA

• Schlipf, Schuler, Grau, Allgöwer, Kühn, ‘10 Torque
• Laks, Pao, Wright, Kelley, B. Jonkman, ‘10 ASM
• Laks, Pao, Simley, Wright, Kelley, ‘11 ASM
• Dunne, Pao, Wright, B. Jonkman, Kelley, Simley, ‘11 ASM
• Korber, King, ‘11 AWEA

Figure from Harris, Hand, and Wright, ‘06

AEROSPACE ENGINEERING AND MECHANICS

Distributed Estimation

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Objectives:

• Identify turbine model from real-time data
• Use measurements from upstream turbines to

estimate wind for use as feedforward signal for downstream turbines.

Selected References:

• Odgaard, Damgaard, Nielsen, ‘08 IFAC
• Knudsen, Bak, Soltani, ‘11 WE
• Van Wingerden, Houtzager, Felici, Verhaegen, 09 IJRNC
• Gebraad, van Wingerden, Fleming, Wright, 11 CCA

40 60 80 100 120 140 160 7.9 8 8.1 Wind Speed Time (s) Wind Speed (m/s) 40 60 80 100 120 140 160 0.4 0.5 0.6 Cp Comparison Time (s) Cp Turbine Model Table Cp Curve 40 60 80 100 120 140 160 5 10 15 Wind Speed Time (s) Wind Speed (m/s) 40 60 80 100 120 140 160 0.5 1 Cp Comparison Time (s) Cp Turbine Model Table Cp Curve

1.31 1.32 1.33 1.34 1.35 1.36 1.37 1.38 x 10

5

6 6.5 7 7.5 8 10 min Averaged Wind Speed Time (s) 10 min Averaged Wind Speed (m 8.2 8.4 8.6 8.8 9 9.2 9.4 9.6 0.2 0.25 0.3 Clipper C96 Cp Curve for beta=1.25 Tip Speed Ratio Cp

FAST Simulations Liberty Real-time Data

AEROSPACE ENGINEERING AND MECHANICS

Overview of Research Projects

14

1.31 1.32 1.33 1.34 1.35 1.36 1.37 1.38 x 10

6 6.5 7 7.5 8 10 min Averaged Wind Speed Time (s) 10 min Averaged Wind Speed (m 8.2 8.4 8.6 8.8 9 9.2 9.4 9.6 0.2 0.25 0.3 Clipper C96 Cp Curve for beta=1.25 Tip Speed Ratio Cp

V27 Control (Thorson, Janisch) Blade Health Monitoring (Lim, Mantell, Yang) Distributed Estimation (Showers) Wind Farm Control (Annoni, Yang, Sotiropolous, Bitar) Active Power Control (Wang) Multivariable Design Tools (Ozdemir, Escobar Sanabria, Balas)

AEROSPACE ENGINEERING AND MECHANICS

Motivation for Monitoring

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Damaged Gearbox

(Image courtesy of Mesabi Range Community and Tech. College)

Failures Rates

Table from: “Wind turbine downtime and its importance for offshore deployment”, Faulstich, Hahn, Tavner, Wind Energy, 2010.

AEROSPACE ENGINEERING AND MECHANICS

Motivation for Monitoring

• Cost of wind energy dominated by capital (installation)

+ operations & maintenance

• Monitoring can be used to reduce O&M costs
• Preventative maintenance during low wind
• Continued operation after failures
• Large literature of wind turbine monitoring
• 2011 IFAC Competition (Benchmark from Odgaard,

Stoustrup, and Kinnaert, 2009 SAFEPROCESS).

• Variety of methods including model-based, data-driven,

physical redundancy

• Question: Can design techniques developed for

aerospace systems be applied for turbines?

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AEROSPACE ENGINEERING AND MECHANICS

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Commercial Fly-by-Wire

Boeing 787-8 Dreamliner

• 210-250 seats
• Length=56.7m, Wingspan=60.0m
• Range < 15200km, Speed< M0.89
• First Composite Airliner
• Honeywell Flight Control Electronics

Boeing 777-200

• 301-440 seats
• Length=63.7m, Wingspan=60.9m
• Range < 17370km, Speed< M0.89
• Boeing’s 1st Fly-by-Wire Aircraft
• Ref: Y.C. Yeh, “Triple-triple redundant

777 primary flight computer,” 1996.

AEROSPACE ENGINEERING AND MECHANICS

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777 Primary Flight Control Surfaces [Yeh, 96]

• Advantages of fly-by-wire:
• Increased performance (e.g. reduced drag with smaller rudder), increased

functionality (e.g. “soft” envelope protection), reduced weight, lower recurring costs, and possibility of sidesticks.

• Issues: Strict reliability requirements
• <10-9 catastrophic failures/hr
• No single point of failure

AEROSPACE ENGINEERING AND MECHANICS

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Classical Feedback Diagram

Sensors Primary Flight Computer Pilot Inputs Actuators

Reliable implementation of this classical feedback loop adds many layers of complexity.

AEROSPACE ENGINEERING AND MECHANICS

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Triplex Control System Architecture

Sensors Primary Flight Computer Column

Actuator Control Electronics

Pilot Inputs Each PFC votes on redundant sensor/pilot inputs Each ACE votes on redundant actuator commands All data communicated

• n redundant data buses

Actuators

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777 Triple-Triple Architecture [Yeh, 96]

Sensors x3 Databus x3 Triple-Triple Primary Flight Computers Actuator Electronics x4

AEROSPACE ENGINEERING AND MECHANICS

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777 Triple-Triple Architecture [Yeh, 96]

Sensors x3 Databus x3 Actuator Electronics x4 Left PFC INTEL AMD MOTOROLA Triple-Triple Primary Flight Computers

AEROSPACE ENGINEERING AND MECHANICS

Ram Air Turbine

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Ram air turbine: F-105 (Left) and Boeing 757 (Right) http://en.wikipedia.org/wiki/Ram_air_turbine

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Summary of Redundancy Management

• Main Design Requirements:
• < 10-9 catastrophic failures per hour
• No single point of failure
• Must protect against random and common-mode failures
• Basic Design Techniques
• Hardware redundancy to protect against random failures
• Dissimilar hardware / software to protect against common-mode failures
• Voting: To choose between redundant sensor/actuator signals
• Encryption: To prevent data corruption by failed components
• Monitoring: Software/Hardware monitoring testing to detect latent faults
• Operating Modes: Degraded modes to deal with failures
• Equalization to handle unstable / marginally unstable control laws
• Model-based design and implementation for software

AEROSPACE ENGINEERING AND MECHANICS

Blade Structural Health Monitoring (SHM)

• Data/Power transportation to/from sensors
• Retrofit capability desirable (no cabling)
• Preventative maintenance
• Shortened down time
• Good for unpredictable

working conditions

SHM benefits Challenges

SHM Example (Rumsey, Paquette, White, Werlock,

Beattie, Pitchford, van Dam, Structural health monitoring

• f wind turbine blades, 2008)

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AEROSPACE ENGINEERING AND MECHANICS

Proposed SHM System

Sensors

(Strain/Acceleration)

Telemetry

(Wireless transceiver)

Energy Harvester

(Piezo-electric)

Issues:

• 1. Low power in blade vibration
• 2. Blade loading difficult to model / measure

26

AEROSPACE ENGINEERING AND MECHANICS

Proposed SHM System

Energy Harvestor = Sensor Telemetry

(Wireless transceiver)

Solution:

• 1. Use harvested energy as the sensor
• 2. Rely on triple redundant measurements

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AEROSPACE ENGINEERING AND MECHANICS

Approach

• Estimate harvested energy
• Properties of energy harvester (size, efficiency, etc)
• Power available in blade vibrations
• Design low-rate health monitoring algorithm
• Assess feasibility of proposed SHM algorithm

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AEROSPACE ENGINEERING AND MECHANICS

Harvested Strain Energy

t f E E E V wstrain    

2

 

Harvested Strain Energy

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AEROSPACE ENGINEERING AND MECHANICS

Harvested Strain Energy

EH Design Variables:

t f E E E V wstrain    

2

 

= KEH , EH Design Factor Harvested Strain Energy

η,

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AEROSPACE ENGINEERING AND MECHANICS

Harvested Strain Energy

EH Design Variables:

t f E E E V wstrain    

2

 

= Pavail , Available Strain Power = KEH , EH Design Factor Harvested Strain Energy

η,

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AEROSPACE ENGINEERING AND MECHANICS

Harvested Strain Energy

EH Design Variables:

t f E E E V wstrain    

2

 

= Pavail , Available Strain Power = KEH , EH Design Factor

Charging Time

Harvested Strain Energy

η,

32

AEROSPACE ENGINEERING AND MECHANICS

Experimental Set-up

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Front Side P1 Type Back Side P2 Type Overall set-up Energy Harvester Strain Gages SMART MATERIAL MFC P2 M2814 Energy Harvester

3

27 . 14 34 . 30 60 . 117 004 . mm Keh    

Signal Conditioner Transceiver

t f E E E V wstrain    

2

 

92.4mJ (Transmission) 280mJ (Strain Meas.+Trans.)

AEROSPACE ENGINEERING AND MECHANICS

Experimental Set-up

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Front Side P1 Type Back Side P2 Type Overall set-up Energy Harvester Strain Gages SMART MATERIAL MFC P2 M2814 Energy Harvester

3

27 . 14 34 . 30 60 . 117 004 . mm Keh    

Signal Conditioner Transceiver

t f E E E V wstrain    

2

 

92.4mJ (Transmission) 280mJ (Strain Meas.+Trans.)

Need to estimate available strain power in blade vibrations

AEROSPACE ENGINEERING AND MECHANICS

Modeling Blade Strain

Flap DOF Edge DOF Initial conditions Number of Blades Blade Mode Shapes Wind Conditions . . .

Inputs

Blade Displacement Shear Force Bending Moments . . .

Outputs

• Ref. Jonkman, J. M., Buhl Jr, M. L., “FAST user’s

guide,” NREL, Golden, Colorado, USA, 2005.

35

AEROSPACE ENGINEERING AND MECHANICS

Modeling Blade Strain

Flap DOF Edge DOF Initial conditions Number of Blades Blade Mode Shapes Wind Conditions . . .

Inputs

Blade Displacement Shear Force Bending Moments . . .

Outputs

• Ref. Jonkman, J. M., Buhl Jr, M. L., “FAST user’s

guide,” NREL, Golden, Colorado, USA, 2005.

36

 

i E i i E i e

EI c M

, , ,

2  

 

i F i i F i f

EI t M

, , ,

2  

Result: Calculate blade edge/flap strain using (FAST) simulated nodal bending moments

AEROSPACE ENGINEERING AND MECHANICS

Characterize the strain energy available for typical wind turbines: Wind Conditions : 6 m/s, Rated Speed, 24 m/s + Low / High Turbulence

Wind Turbine Case Studies

CART3 WindPact Offshore 600 kW 1.5 MW 5.0 MW 37.1 rpm 20.5 rpm 12.1 rpm Rated Power Rated Speed 6, 14, 20 m/s 3, 12, 28 m/s 3, 11, 25 m/s Wind Speed 20m/1.8ton 35m/3.9ton 63m/17.7ton

Length/Weight

34.9 m 84 m 87.6 m

Hub Height

37

AEROSPACE ENGINEERING AND MECHANICS 20 40 60 80 120 130 140 150

• 1500
• 1000
• 500

500 1000 1500 20 40 60 80 120 130 140 150

• 1500
• 1000
• 500

500 1000 1500

Strain Simulation in Time & Span

Wind Conditions

24 m/s, Low Turbulence

FAST Flapwise Strain Edgewise Strain

Strain (μ-ε)

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AEROSPACE ENGINEERING AND MECHANICS

Strain Analysis

120 125 130 135 140 145 150

• 1500
• 1000
• 500

500 1000 1500 Time (sec) Strain (micro) Edgewise Flapwise

0.2 0.4 0.6 0.8 1 200 400 600 Max Strain (Edgewise) |Strain| (u-strain) Frequency (Hz) 0.2 0.4 0.6 0.8 1 200 400 600 Max Strain (Flapwise) |Strain| (u-strain) Frequency (Hz)

FFT

Pick one location

Dominant Freq.

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AEROSPACE ENGINEERING AND MECHANICS

5 MW WT model 24 m/s, Low Turbulence

Available Strain Power in Blade Span

10 20 30 40 50 60 10 20 30 40 50 60

f E P

avail 2 0



Span direction (m) Strain Power (W/m3) Edgewise Flapwise

Nominal E0 = 1 GPa FFT analysis for 9 locations

ε = Strain Amplitude (mean to peak) f = Dominant Frequency

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AEROSPACE ENGINEERING AND MECHANICS

Available Strain Power

LWS: 6 m/s MWS: Rated HWS: 24 m/s LT: Low HT: High 6 Wind Conditions: 3 Wind Turbines: 600 kW, 1.5 MW, 5.0 MW Wind Speed Turbulence Intensity

600kW 1.5MW 5.0MW LWS/HT LWS/LT MWS/HT MWS/LT HWS/HT HWS/LT 10 20 30 40 50 60 Edgewise Strain Power Power available [W/m3] 600kW 1.5MW 5.0MW LWS/HT LWS/LT MWS/HT MWS/LT HWS/HT HWS/LT 10 20 30 40 50 60 Flapwise Strain Power Power available [W/m3]

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AEROSPACE ENGINEERING AND MECHANICS

Apply to EH/Telemetry Design

t P K w

avail EH strain

   

Harvested Strain Energy (μJ)

42

AEROSPACE ENGINEERING AND MECHANICS

Apply to EH/Telemetry Design

t P K w

avail EH strain

   

Harvested Strain Energy (μJ) 92.4 μJ, Transmission only Available for 5MW WT Power Pavail = 60, 40, 13 W/m3

43

AEROSPACE ENGINEERING AND MECHANICS

11 12 13 14 15 16 17 18 1 2 3 4 5 6 7 8 9 10 11 Design factor of EH, KEH (mm3) Charging time (min)

Apply to EH/Telemetry Design

t P K w

avail EH strain

   

Harvested Strain Energy (μJ) 92.4 μJ, Transmission only Available for 5MW WT Power Pavail = 60, 40, 13 W/m3

44

13 W/m3 40 60

AEROSPACE ENGINEERING AND MECHANICS

11 12 13 14 15 16 17 18 1 2 3 4 5 6 7 8 9 10 11 Design factor of EH, KEH (mm3) Charging time (min)

KEH = 14.27 mm3 MFC P2

Apply to EH/Telemetry Design

t P K w

avail EH strain

   

Harvested Strain Energy (μJ) 92.4 μJ, Transmission only Available for 5MW WT Power Pavail = 60, 40, 13 W/m3

45

13 W/m3 40 60

AEROSPACE ENGINEERING AND MECHANICS

11 12 13 14 15 16 17 18 1 2 3 4 5 6 7 8 9 10 11 Design factor of EH, KEH (mm3) Charging time (min)

KEH = 14.27 mm3 MFC P2

Apply to EH/Telemetry Design

t P K w

avail EH strain

   

Harvested Strain Energy (μJ) 92.4 μJ, Transmission only Available for 5MW WT Power Pavail = 60, 40, 13 W/m3

46

13 W/m3 40 60

Summary: Power only sufficient for very low transmission rates. Question: Can blades be monitored with low rate data?

AEROSPACE ENGINEERING AND MECHANICS

KEH = 0.78 mm3 (ZnO Nanowire EH)

η=6.8%, E=30GPa, V=0.38mm3(10x20cm2, 20 layers)

Apply to EH/Telemetry Design

t P K w

avail EH strain

   

Harvested Strain Energy (μJ) 280 μJ, Single data packet measurement/transmission Available for 5MW WT Power Pavail = 60, 40, 13 W/m3

• Ref. G. Zhu, et al. Flexible High-Output Nanogenerator Based on Lateral ZnO Nanowire Array, 2010

0.5 0.6 0.7 0.8 0.9 1 2 4 6 8 10 12 Design factor of EH, KEH (mm3) Charging time (hour)

13 W/m3 40 60

47

AEROSPACE ENGINEERING AND MECHANICS

Proposed SHM Algorithm

48

Turbine Blade Strain

BL1 BL2 BL3

Harvested Energy

760 770 780 790 800

• 400
• 200

200 Time (sec) B1-LP-25-S Strain (micro) 760 770 780 790 800

• 400
• 200

200 Time (sec) B2-LP-25-S Strain (micro) 760 770 780 790 800 400 600 800 1000 Time (sec) B3-LP-25-S Strain (micro) 760 770 780 790 800 0.05 0.1 0.15 0.2 0.25 Time (sec) Strain Energy (uJ) 760 770 780 790 800 0.05 0.1 0.15 0.2 0.25 Time (sec) Strain Energy (uJ) 760 770 780 790 800 0.05 0.1 0.15 0.2 0.25 Time (sec) Strain Energy (uJ)

2000 4000 6000 200 400 600 t(sec) t(sec) 2000 4000 6000 200 400 600 t(sec) t(sec) 2000 4000 6000 200 400 600 t(sec) t(sec)

Key idea: Transmit single pulse when harvested energy exceeds threshold (Harvested energy is correlated with damage)

Pulses SHM Algorithm Detect blade damage based

• n pulse timing

AEROSPACE ENGINEERING AND MECHANICS

Problem Set-up

49

FAST / EOLOS Wind Turbine Piezo-electric Energy Harvester Blade Strain

BL1 BL2 BL3

Rosemount, MN Wind Data

Fail

Simplified Damage Model Strain Energy Accumulation

760 770 780 790 800

• 400
• 200

200 Time (sec) B1-LP-25-S Strain (micro) 760 770 780 790 800

• 400
• 200

200 Time (sec) B2-LP-25-S Strain (micro) 760 770 780 790 800 400 600 800 1000 Time (sec) B3-LP-25-S Strain (micro) 760 770 780 790 800 0.05 0.1 0.15 0.2 0.25 Time (sec) Strain Energy (uJ) 760 770 780 790 800 0.05 0.1 0.15 0.2 0.25 Time (sec) Strain Energy (uJ) 760 770 780 790 800 0.05 0.1 0.15 0.2 0.25 Time (sec) Strain Energy (uJ) 760 770 780 790 800 1 1.05 1.1 1.15 1.2

Synthesizing Blade Damage

(Paquette, et al. 46th AIAA ASM, ‘08)

AEROSPACE ENGINEERING AND MECHANICS

50 60 70

• 1000

1000 Time (sec) Edge|Strain| (u-strain)

FAST Simulation Result (OS5MW WT)

50

FAST Simulator (OS5MW WT) Transceiver Signal Cond. Strain Energy

EH1 EH2 EH3

TurbSim Wind Data Signal Transmission Damaged Blade (Progressive )

1000 2000 3000 5 10 15 20 Time (sec) WInd Velocity (m/s)

40 50 60 70 5 10 15 EH Energy (mJ) t(sec) 40 50 60 70 5 10 15 EH Energy (mJ) t(sec) 40 50 60 70 5 10 15 EH Energy (mJ) t(sec)

2000 4000 6000 200 400 600 t(sec) t(sec) 2000 4000 6000 200 400 600 t(sec) t(sec) 2000 4000 6000 200 400 600 t(sec) t(sec)

1000 2000 3000 4000 5000 6000 7000 0.8 1 1.2 1.4 t(sec) Ration (-) Threshold 1-2 2-3 3-1

2000 4000 6000 300 350 400 450 500 Time (sec) t(sec)

AEROSPACE ENGINEERING AND MECHANICS

Clipper Raw Data Result (Healthy)

51

Strain Energy Hub Height Wind Speed Signal Transmission

500 1000 1500 2000 2500 3000 3500 3 3.5 4 4.5 5 5.5 Time (sec) Wind (m/s) 50 60 70 80 90

• 600
• 400
• 200

200 400 Time (sec) Strain (micro) B1-LE-RT-S B2-LE-RT-S B3-LE-RT-S

Strain @ 3 Blades

1000 2000 3000 100 200 300 400 500 600 700 800 900 Signal firing timing of EHs Time between transmissions (sec) Time (sec) EH1(Blade1) EH2(Blade2) EH3(Blade3)

2100 2150 2200 2250 2300 50 60 70 80 EH Energy (mJ) t(sec) 2100 2150 2200 2250 2300 50 60 70 80 EH Energy (mJ) t(sec) 2100 2150 2200 2250 2300 50 60 70 80 EH Energy (mJ) t(sec)

EH1 EH2 EH3

AEROSPACE ENGINEERING AND MECHANICS

Clipper Data (same data) with Synthetic Fault

52

Damage Model Signal Transmission

(Paquette, J., et al. 46th AIAA ASM, NV 2008)

Fail

Interpretation

500 1000 1500 2000 2500 3000 3500 0.95 1 1.05 1.1 1.15 1.2 1.25 Time (sec) Time difference between signal transmissions (sec) Threshold 1-2 2-3 3-1

Blade 3 Strain Output

1000 2000 3000 1 1.05 1.1 1.15 1.2 Time(sec) Strain Weigth (-) 1000 2000 3000 100 200 300 400 500 600 700 800 900 Signal firing timing of EHs Time between transmissions (sec) Time (sec) EH1(Healthy) EH2(Healthy) EH3(Damaged)

AEROSPACE ENGINEERING AND MECHANICS

53

Conclusions

• Advanced monitoring and control techniques can

continue to reduce the costs of wind energy.

• Energy harvesting can be used to power sensors
• Max. strain: ~20 to 33% of the blade length
• Max. available strain power for harvesting: ~60 W/m3
• Long charging time is required given current EH technology
• Total harvested energy can be used to monitor blade
• Harvested energy is correlated with damage
• Transmit single pulse when harvested energy exceeds threshold
• Rely on triple redundant measurements

AEROSPACE ENGINEERING AND MECHANICS

Future Work

• 1. Experimental validation of proposed SHM algorithm
• Build test beam specimens with variety of damage types
• Design a power conditioner/booster to maximize EH performance

(matched resistance).

• Vibrate test specimen to mimic realistic operating conditions
• Evaluate ability of SHM algorithm to detect damage
• 2. EH development: ZnO Nanowire array
• Ref: Zhu, Yang, Wang, Wang, Flexible High-Output Nanogenerator

Based on Lateral ZnO Nanowire Array, ’10 Nano Letters

5 10 15

• 1
• 0.8
• 0.6
• 0.4
• 0.2

0.2 0.4 0.6 0.8 1 Time (sec) Normalized Blade Strain (unitless) Blade 1 Blade 2 Blade3

EOLOS Wind Turbine

Pulses Blade Strain Lab-scale Set-up

Energy Harvester

54

AEROSPACE ENGINEERING AND MECHANICS

55

Acknowledgments

• Institute for Renewable Energy and the Environment
• Grant No. RL-0010-12: “Design Tools for Multivariable Control of Large

Wind Turbines.”

• Grant No. RS-0039-09: “Improved Energy Production for Large Wind

Turbines.”

• Grant No. RS-0029-12: “Development of self-powered wireless sensor for

structural health monitoring in wind turbine blades”

• US Department of Energy
• Grant No. DE-EE0002980: “An Industry/Academe Consortium for Achieving

20% wind by 2030 through Cutting-Edge Research and Workforce Training”

• Eolos Wind Energy Consortium: Provided Liberty data
• US National Science Foundation
• Grant No. NSF-CMMI-1254129: “CAREER: Probabilistic Tools for High

Reliability Monitoring and Control of Wind Farms”