Estimation of wind turbine shaft remaining lifetime using ...

Estimation of wind turbine shaft remaining lifetime using SCADAmeasurements

Dheelibun Remigius & Anand Natarajan

Wind Turbine Structures and Component Design Section

Outline

• Introduction

• Inverse Problem

• Validation and Results

• Summary and Future Steps

2 DTU Wind Energy Estimation of main shaft fatigue lifetime from SCADA measurements 20.6.2019

IntroductionIntroduction

• A significant number of wind turbines installed in Europe will reach their design life (20 years) in thenext few years.

• Decision process for lifetime extension requires an assessment of the risk of failure upon life extensionbased on the present condition of the wind turbine.

• For those wind turbines with SCADA based measurements and inspection reports available, therecorded measurements can be utilized to predict the life consumption of specific components such asthe main shaft.


IntroductionScope of the Work

• To estimate the remaining lifetime of the main shaft based on SCADA measurements.• To compute the torsional fatigue damage of the shaft based on measured rotor speed, generatorspeed and generator torque.

• Focus is on shafts and but its dynamics will affect all the members in drive train.


IntroductionWind Turbine Drive Train• Main shaft is the integral component of WT drive train.• Subjected to bending and torsional loads.• Torsional member, torsional loads are dominating the shaft fatigue life.• Torsional loads are transmitted to main bearings.

Figure: Typical SN curve. Courtesy: Kang et al., Energies 2019, 12(1), 7.


Inverse ProblemTorsional Fatigue Damage

• Torsional loads are needed for fatigue damage calculation.• Using the following shaft equations, these torsional loads are estimated.

Jrωr = Tr −Kθ − Cθ, (1)

Jgωg = −Tg + K

Nθ + C

Nθ, (2)

θ = ωr − ωg/N. (3)

• Lack of design basis complicate the load estimation process.


Inverse ProblemLoad Estimation Procedure

• Available SCADA signals are rotor speed (ωr), generator speed (ωg), generator power (Pg), bladepitch angle (β) and mean wind speed (U).

• Now with ωr, and ωg, the torsional displacement is estimated using Eq. (3) as,

θ =∫θ dt =

∫(ωr − ωg

N) dt (4)

• After estimating θ, with generator torque obtained from Tg = Pg/ωg, the system parametersK,C,M are obtained by applying collage method on Eq. (2).

Jgωg = −Tg + K

Nθ + C

Nθ (5)

• Then, the torsional moment is calculated as Mz = Kθ.


Inverse ProblemCollage Method

• Model based system identification technique.• Consider a SDOF system, mx+ cx+ kx = 0, x(0) = x0, x0 = x0.• From observations of response x(t), one can estimate the system parameters as follows:

E = m(x(t) − x0 − x0(t)) + c

∫ t

0x(t) dt+ k

∫ t

0x(t) dt = 0 (6)

• Using method of least squares, parameters are obtained by minimizing E2 with respect to theunknown parameters.


Inverse ProblemTorsional Fatigue Life Calculation

Figure: Torsional fatigue life calculation from SCADA data.


Validation and ResultsTesting of Collage Method on Shaft Dynamics

• To test the applicability of the collage method on the wind turbine drive train dynamics, the followingthree wind turbines are chosen, (i) NREL 5 MW, (ii) DTU 10 MW and (ii) Vestas v52 850 kW.

• For this purpose, the forward problem is solved first for these turbines at 14 m/s and the results arecompared with the results of collage method.

Wind turbine Percentage Error in J, C, K respectivelyNRWL 5 MW 0.13, 52, 0.69DTU 10 MW 0.03, 70, 2.349Vestas v52 0.12, 62, 0.5

Table: Estimated of system parameters using collage method.


Validation and ResultsContd· · ·

• Minima of the error function for the NREL 5 MW.

0 0.5 1 1.5 2

K 109

0

5

10

Err

function

1022

X 8.758e+08

Y 1.823e+19

(a) Inertia

0 2 4 6 8

J 106

0

0.5

1

1.5

2

Err

function

1019

X 3.053e+06

Y 5.285e+17

(b) Stiffness

0 100 200 300 400 500 600 700

Time (s)

-1

0

1

2

3

4

5

Tors

iona

l mom

ent

106

HAWC2

Estimated

(c) NREL 5 MW

0 100 200 300 400 500 600 700

Time (s)

0

2

4

6

8

10

12

Tors

iona

l mom

ent

106

HAWC2

Estimated

(d) DTU 10 MW

0 100 200 300 400 500 600 700

Time (s)

-2

-1

0

1

2

3

4

Tors

iona

l mom

ent

105

HAWC2

Esimated

(e) Vestas v5211 DTU Wind Energy Estimation of main shaft fatigue lifetime from SCADA measurements 20.6.2019

Validation and ResultsTesting of the Proposed Methodology on Vestas V52 Dynamics

• The shaft torsional moments for Vestas V52 turbine are estimated using the above mentionedprocedure.

• Inputs are obtained from HAWC2 simulations.• Rotor speed, generator speed and generator torques are obtained for DLC 1.2.


Validation and ResultsShaft Torsional Moment at 8 m/s

0 100 200 300 400 500 600 700

Time (s)

-4

-3

-2

-1

0

1

To

rsio

na

l mo

me

nt,

Mz (

Nm

)

105

Hawc2

Estimated

(f) Torsional moment, Mz

0 1 2 3 4 5

Frequency (Hz)

0

200

400

600

800

1000

|Mz|

Hawc2

Estimated

(g) FFT


Validation and ResultsShaft Torsional Moment at 14 m/s

0 100 200 300 400 500 600 700

Time (s)

-4

-3

-2

-1

0

1

To

rsio

na

l mo

me

nt,

Mz (

Nm

)

105

Hawc2

Estimated

(h) Torsional moment, Mz

0 1 2 3 4 5

Frequency (Hz)

0

2000

4000

6000

8000

10000

|Mz|

Hawc2

Estimated

(i) FFT


Validation and ResultsTorsional DEL• Torsional loads are simulated for all wind speeds as per DLC 1.2.• Damage Equivalent Load (DEL) is calculated as,

DEL =(

1Nref

∑i

(Tlife,i

Tsim,i

∑kNikS

mik

))(1/m)(7)

0 5 10 15 20 25 30

Mean wind speed (m/s)

0

1

2

3

4

5T

ors

ion

al D

EL

(N

m)

105

Estimated

HAWC2


Validation and ResultsEnhanced Frequency Domain Decomposition Technique• From the estimated torisonal load, using enhanced frequency domain decomposition (EFDD), thedamping coefficient (ζ) are obtained.

• EFDD is the output only modal identification technique.

100 200 300 400 500 600 700

Time (s)

-1.2

-1.15

-1.1

-1.05

-1

-0.95

Shaft tors

ional m

om

ent, M

z

104

(j) Torsional moment, Mz

0 5 10 15

Frequency (Hz)

-100

-50

0

50

100

150

200

250

1st S

ingula

r valu

es o

f th

e P

SD

matr

ix (

db)

1

(k) Singular values of PSD

0 20 40 60 80 100 120 140

Time (s)

-1

-0.5

0

0.5

1

auto

corr

ela

tion function

(l) Auto-correlation function

• By using the estimated damping coefficient, the accuracy of the collage method can be improved.16 DTU Wind Energy Estimation of main shaft fatigue lifetime from SCADA measurements 20.6.2019

Validation and ResultsVestas v52 turbine - Torsional Load from SCADA• Measurements of the Vestas V52 turbine installed in DTU RISØ, Denmark has been used for thesimulation of the shaft torsional loads.

• Rotor speed, generator speed and generator torque signals are calibrated and used as inputs.

0 100 200 300 400 500 600

Time (s)

-4

-3

-2

-1

0

1

2

3

Tors

ional m

om

ent, M

z (N

m)

105

(m) Torsional moment, Mz (n) FFT17 DTU Wind Energy Estimation of main shaft fatigue lifetime from SCADA measurements 20.6.2019

Summary• Inverse problem technique has been developed to estimate the shaft torsional loads from themeasurements.

• For the validation, inverse problem has been solved with HAWC2 inputs and the results were matchedqualitatively well with HAWC2.

Future Steps• Improvement of the accuracy of the proposed method using EFDD has to be tested.• Implementation and validation of the proposed procedure for the estimations from SCADA


Thank you


Estimation of wind turbine shaft remaining lifetime using ...

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