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Contract No. DE-AC36-08GO28308

Gearbox and Drivetrain Models 

to Study Dynamic Effects of 

Modern Wind Turbines

Preprint

I.P. Girsang and J.S. Dhupia

Nanyang Technological University

E. Muljadi and M. Singh

National Renewable Energy Laboratory

L.Y. Pao

University of Colorado at Boulder

Presented at the IEEE Energy Conversion Congress and 

Exposition

Denver, Colorado

September 15–19, 2013

Conference Paper

NREL/CP-5500-58960

October 2013
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Gearbox and Drivetrain Models to Study Dynamic 

Effects of Modern Wind Turbines

Irving P. Girsang and Jaspreet S. Dhupia

School of Mechanical and Aerospace Engineering

Nanyang Technological University

Singapore

irving1@e.ntu.edu.sg djaspreet@ntu.edu.sg 

Eduard Muljadi and Mohit Singh

Transmission and Grid Integration Group

National Renewable Energy Laboratory

Golden, Colorado

eduard.muljadi@nrel.gov mohit.singh@nrel.gov 

Lucy Y. Pao

Department of Electrical, Computer, and Energy Engineering

University of Colorado at Boulder

Boulder, Colorado

pao@colorado.edu 

Abstract—Wind turbine drivetrains consist of components that 

directly convert kinetic energy from the wind to electrical 

energy. Therefore, guaranteeing robust and reliable drivetrain 

designs is important to minimize turbine downtime. Current 

drivetrain models often lack the ability to model both the 

impacts of electrical transients as well as wind turbulence and 

shear in one package. In this work, the capability of the FAST

wind turbine computer-aided engineering tool, developed by 

the National Renewable Energy Laboratory, is enhanced 

through integration of a dynamic model of the drivetrain. The 

dynamic drivetrain model is built using Simscape in the 

MATLAB/Simulink environment and incorporates detailed 

electrical generator models. This model can be used in the 

future to test advanced control schemes to extend life of the 

gearbox. 

Index Terms—gears, stress control, variable-speed drives, 

wind power generation

I. NOMENCLATURE

αrot turbine rotor angular acceleration

β gear helical angle

λopt optimum tip speed ratio

ωrot turbine rotor angular speed

ceff effective drivetrain torsional damping with respect 

to the low-speed side of the gearbox

fn nonzero eigenfrequency of the two-mass model

keff effective drivetrain torsional stiffness with respect 

to the low-speed side of the gearbox

kgear gear tooth stiffness

kmesh constant gear meshing stiffness

kY torsional stiffness of each drivetrain shaft, Y ∈

{low-speed shaft (LSS), high-speed shaft (HSS), 

and l

th intermediate shaft, l = 1, 2}

rb gear base circle radius

Jeff effective inertia of the generator and the gearbox 

with respect to the low-speed side of the gearbox

JX inertia of the drivetrain components, X ∈ {turbine 

rotor (rot), planet gears and the carrier (PC), carrier 

only (C), sun gear (S), generator (gen), and planet 

gears (Pm), m = 1, ..., M, and parallel gear 

components (Gi), i = 1, 2, 3, 4}

M number of planet gears

N overall drivetrain gear ratio

Nj gear ratio of each gear stage, j = 1, 2, 3, Nj ≥ 1 

Qaero wind aerodynamic torque

Qem generator electromagnetic torque

Qopp rotor opposing torque

Rrot turbine rotor radius

Vw effective wind speed

II. INTRODUCTION

HE wind energy industry has experienced substantial 

growth in recent decades, and there has been similar 

growth in the structural size of and power output from wind 

turbines. The operating conditions of wind turbines are 

largely determined by structural loadings such as wind 

turbulence and, in offshore wind turbines, sea wave 

excitations. Aeroelastic computer-aided engineering (CAE)

tools such as FAST [1], GH Bladed [2], and HAWC2 [3] 

have been developed to model and simulate the dynamics of

wind turbines in response to different wind fields and 

controllers. These codes simulate the most relevant 

environmental conditions and output time series of turbine 

operational load variations.

Because the wind turbine drivetrain consists of 

components that directly convert rotational kinetic energy 

from the wind to electrical energy, ensuring the reliability 

of drivetrain designs is critical to preventing wind turbine 

downtime. Because of the steadily increasing size of wind 

turbines, larger forces and torques bring up the influence of

the gearbox and other drivetrain flexibilities in the overall 

turbine dynamic response [4]–[5], often leading to failure in 

T
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at www.nrel.gov/publications.

the drivetrain components. Failure in drivetrain components 

is currently listed among the more problematic failures 

during the operational lifetime of a wind turbine. In 

particular, gearbox-related failures are responsible for more 

than 20% of the downtime of wind turbines. Although the 

expected lifetime of gearboxes is usually advertised as 20 

years, in practice gearboxes usually need to be replaced 

every 6 to 8 years [6]–[7].

TABLE I 

MODELING PROPERTIES OF GRC WIND TURBINE

Configuration, Rating 3 Blades, 750 kW

Control Variable Speed, Collective Pitch

Gearbox, Overall Ratio 3 Stages, 81.49

Rotor, Hub Diameter 24.1, 0.6 m

Hub Height 54.8 m

Rated Rotor Speed 22.1 RPM

Maximum Rotor CP 0.43

Fig. 1. Schematic of DFIG

Further insights into wind turbine drivetrain dynamics 

will be helpful in understanding the global dynamic 

response of a wind turbine as well as in designing and 

preserving its internal drivetrain components. Thus far, 

however, in most of the aforementioned codes, the

drivetrain model is reduced to a few (mostly two) degrees 

of freedom, resulting in restricted detail in describing its 

complex dynamic behavior. Although researchers have 

developed dynamic models for wind turbine drivetrains 

with various levels of fidelity [8]–[18], these studies do not 

provide direct insights on the dynamic interactions between 

the drivetrain and other components of the entire wind 

turbine. The most recent study [18] takes a decoupled 

approach in which the global turbine response was first 

simulated using an aeroelastic CAE tool (i.e., HAWC2). 

After the simulation, the resulting loads and motions of the 

rotor as well as the nacelle were used as inputs to a highfidelity model of the drivetrain to simulate its internal 

dynamic behavior. Thus, this approach fails to capture the 

influence of the drivetrain dynamics onto the overall turbine 

response. 

This study aims to investigate the global dynamic 

response of a wind turbine drivetrain by enhancing the 

capability of FAST through integration of a dynamic model 

of the drivetrain. The drivetrain model is built using 

Simscape [19]. This tool extends the FAST capability in the

MATLAB/Simulink environment, which in the future can 

help in design and verification of active control strategies to 

mitigate the drivetrain loads.

This paper is organized as follows. Section III highlights 

the wind turbine properties modeled in this study. Section 

IV describes the various levels of the drivetrain model 

investigated in this study. Section V discusses the 

integration of the developed models into the FAST CAE 

tool. Finally, concluding remarks and a discussion 

regarding future work are given in Section VI. 

TABLE II

PARAMETERS OF THE 750 KW DFIG 

Generator

Line-Line Voltage (RMS) 690 V

Frequency, No. of Pole Pairs 60 Hz, 2

Stator Resistance, Leakage Inductance (pu) 0.016, 0.06

Rotor Resistance, Leakage Inductance, Both 

Referred to Stator (pu) 0.016, 0.06

Magnetizing Inductance (pu) 2.56

Inertia Constant (s) 2

Converter

Converter Maximum Power (pu) 0.5

Grid-Side Coupling Inductance, Reactance 

(pu) 0.15, 0.0015

Nominal DC Bus Voltage 1200 V

DC Bus Capacitor 0.1 F

Fig. 2. Modular drivetrain configuration of wind turbine

III. WIND TURBINE DESCRIPTION

The turbine modeled in this study is based on the 

Gearbox Research Collaborative (GRC) turbine [20] at the 

National Renewable Energy Laboratory’s (NREL’s) 

National Wind Technology Center (NWTC). Table I 

summarizes the important properties of this turbine.

The GRC wind turbine originally employed two fixedspeed wind turbine generators (WTGs). To model variablespeed operation, the WTG is modeled using a doubly-fed 

induction generator (DFIG), shown in Fig. 1. Because the 

drivetrain has much slower dynamics than the mechanical 

drivetrain, an average model of the AC-DC-AC converter is 

used in this study, in which the power electronic devices are 

replaced by controlled current sources. The induction 

generator model is based on a commercial wind turbine of 

the same rating available in the market. Table II summarizes 

the key parameters of the generator and the converter, 

which are used to build the variable-speed WTG model in 

the Simscape/ SimPowerSystems environment.
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For wind speeds below 12.5 m/s, which is the rated wind 

speed of this turbine, the output power of this WTG is 

controlled to track the maximum power coefficient (CPmax), 

while maintaining constant pitch angle at its optimum (-3.5o

for this turbine). In variable-speed operation, the rotor 

speed,ωrot , is proportional to the wind speed,Vw : 

rot

wopt

rot R

λ V

ω = (1)

where λopt is the desired optimum tip speed ratio.

IV. DRIVETRAIN MODELING

A. Five-Mass Model

This study focuses on a commonly used modular 

drivetrain configuration in operating turbines [8]. Fig. 2 

shows the building blocks of the configuration. In this 

turbine, the multistage gearbox consists of a planetary 

(epicyclic) gear set and two parallel gear sets, with two 

intermediate shafts.

Fig. 3 shows the five-mass model of the wind turbine 

drivetrain with fixed-speed generator, developed in the 

Simscape/SimDriveline environment. A fixed-speed

generator has an electrical torsional stiffness between the air 

gap magnetic field and the generator rotor. This stiffness 

behaves as a spring to the inertial reference frame of the 

drivetrain, which provides restoring torque to the rest of the 

drivetrain. Such stiffness arises because of tight, allowable 

speed variation in the fixed-speed turbine. Effects of this 

stiffness are prominent in the transient response of the 

generator (e.g., during generator start-up or grid-fault 

events). This stiffness value can be obtained through 

experiments [21]. For a variable-speed generator, this 

restoring effect does not exist, and the drivetrain model 

shown in Fig. 3 will have a free boundary condition on the 

other side of the generator.

Frequency analysis on this five-mass model reveals

eigenfrequencies of the drivetrain, as summarized in Table 

III. These frequencies imply several possible resonant 

excitations that will lead to amplified loads in the drivetrain. 

B. Two-Mass Model

Fig. 4 illustrates the configuration of the two-mass model

commonly used to model the dynamics of drivetrains in 

wind turbine aeroelastic CAE tools, such as FAST [1]. 

Inputs into the model are the five parameters: Jrot, keff, ceff, 

N, and Jeff/N2

. The generator electrical torsional stiffness is 

generally not required in such codes, because this stiffness 

is inherent to the generator model used for the analysis. 

Parameters of the two-mass model can be derived from the 

five-mass model as follows: 

( ( ( ))) eff PC GS GG G gen ++++++= JJNJJNJJNJJ 1 2 3 4

2

3

2

2

2

1 (2)

( ) ( ) eff LSS HSS kk kN kNNNkNN

2

3212

2

1 21

2

1

1111 1

++= + (3)

The effective drivetrain torsional damping, ceff, can be 

determined experimentally through several braking events 

[8]. The nonzero eigenfrequency of this two-mass model 

can be calculated as:















 = +

rot eff

n eff JJ kf 11

2

1

π

(4)

The resulting eigenfrequency using the two-mass model 

for the GRC drivetrain is 2.96 Hz, which is quite different 

from the first nonzero eigenfrequency of the five-mass 

model that is 2.52 Hz. This first eigenfrequency is of

importance because it stores the most torsional energy. As 

exhibited in the following section, the two-mass model has 

limitations in providing insights on possible resonant 

excitations of the drivetrain as well as in analyzing the loads 

experienced by different components of the drivetrain.

Fig. 4. Two-mass model of wind turbine drivetrain

Fig. 3. Five-mass model of wind turbine drivetrain in SimDriveline with fixed-speed induction generator
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TABLE III

EIGENFREQUENCIES OF FIVE-MASS MODEL

Drivetrain with Fixed-Speed Generator

Mode Description Frequency (Hz)

1 Low-Speed Shaft Mode 0.86

2 Generator Start-Up Mode 6.05

3 High-Speed Shaft Mode 312

4 1st Gearbox Mode 402

5 2nd Gearbox Mode 1960

Drivetrain with Variable-Speed Generator 

Mode Description Frequency (Hz)

1 Generator Static Model 0

2 Low-Speed Shaft Mode 2.52

3 High-Speed Shaft Mode 312

4 1st Gearbox Mode 402

5 2nd Gearbox Mode 1960

Fig. 5. Parallel gear stage and gear mesh stiffness representations

Fig. 6. Planetary gear set with three planet gears.

C. Pure Torsional Model of Gearbox

In the two previously described drivetrain models, the 

meshing gear is modeled as an ideal static gain for torque 

and speed transmission. In reality, the gear transmission 

error, which is defined as the difference between the actual 

and ideal angular positions of the rotating gear mainly 

caused by the gear elastic deformation, contributes to the 

dynamics of the pair meshing gear. This phenomenon 

contributes to the definition of gear meshing stiffness. 

Several studies have been performed to investigate the 

dynamics of the gearbox using flexible multibody models 

built in various software packages [8]–[18]. These studies, 

however, focus on the internal dynamics of the gearbox, 

and the developed model packages are not readily 

compatible with available wind turbine aeroelastic CAE 

tools. 

In this study, a purely torsional model of the gearbox 

with constant meshing stiffness is built in the Simscape/ 

SimDriveline environment. The model development and 

analysis on both planetary and parallel gear stages will be 

discussed in this subsection, followed by some remarks on 

the integration and simulation of this model into the 

aeroelastic CAE tool of interest (i.e., FAST) in the 

following section.

1) Parallel Gear Stage

Fig. 5a shows a parallel gear set, which is a torque 

reducer, commonly employed in wind turbine drivetrains. 

Fig. 5b represents its flexible equivalent, in which the 

meshing stiffness acts on the line of action of the meshing 

gears. This meshing stiffness kmesh, with respect to the input 

gear, can be represented as [11]:

2

1 ( cos β ) mesh bgear = rkk (5)

where the gear tooth stiffness kgear can be determined 

according to standards [22]–[23]. Because this system can 

be seen as a two-mass system, the local eigenfrequency of

this gear set can be determined using a similar approach as 

in (4).

Fig. 7. Torsional model of planetary gear stage with M planet gears

2) Planetary Gear Stage

Fig. 6 shows a planetary gear set with three planet gears 

and similar configuration to the one installed in the GRC 

turbine. 

The rotational input is from the carrier of the planetary 

gear stage, which provides rotational motion through the 

planet gears, and finally to the sun gear. The ring gear is 

modeled to have flexible coupling with the gear housing. 

Flexibility between the meshing planet and ring gears and

between the meshing planet and sun gears can be modeled 
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similar to that of a parallel gear set using (5), as shown in 

Fig. 5b.

Fig. 7 shows a torsional model of a planetary gear set 

built in the Simscape/SimDriveline environment. This 

model can be adapted for any M equispaced-planet gear set. 

To validate this model, comparisons with published

frequency analysis [11] of planetary gear sets with various

numbers of planet gears were performed. The results of the 

comparisons show good agreement and are summarized in 

Table IV. Fig. 8 shows the frequency response function 

(FRF) of the planetary gear set in Table IV that has three 

planet gears. The FRF was produced by inputting torque at 

the carrier and taking the rotational speed of the sun gear as 

the output. It is important to note that the planetary gear sets 

used for the analysis in Table IV and Fig. 8 are different 

from the planetary gear set used in the GRC wind turbine 

drivetrain.

Table V summarizes the frequency analysis results of the 

GRC wind turbine drivetrain using the torsional model of 

the gearbox and variable-speed generator model. Exciting 

any of these eigenfrequencies will lead to amplified loads in 

the drivetrain.

V. MODEL INTEGRATION

Fig. 9 illustrates the proposed strategy to integrate the 

described drivetrain models into the two-mass model 

inherent inside the FAST CAE tool. For simplicity, the 

flexible modes of the other turbine components modeled 

inside FAST, such as those of the blades and tower, are not 

depicted in the schematic diagram in Fig. 9.

In FAST, the two-mass drivetrain model is reduced to a 

single-mass model consisting of solely the rotor and the 

rigid shaft (as shown in the bottom part of Fig. 9). This is 

done by deactivating the flexibility of the drivetrain 

(simulating rigid transmission) and setting the gear ratio and 

the generator inertia to unity and zero, respectively. The 

rotor equation of motion can be expressed as:

α rotrot aero −= QQJ opp (6)

TABLE IV

EIGENFREQUENCIES OF PLANETARY GEAR STAGE 

3-Planet Gears

Mode SimDriveline Model Peeters [11]

1 2.273 kHz 2.217 kHz

2 6.340 kHz 6.159 kHz

3 11.296 kHz 11.205 kHz

4-Planet Gears

1 2.207 kHz 2.138 kHz

2 6.911 kHz 6.688 kHz

3 12.699 kHz 12.577 kHz

5-Planet Gears

1 2.153 kHz 2.059 kHz

2 7.403 kHz 7.105 kHz

3 13.980 kHz 13.810 kHz

TABLE V 

EIGENFREQUENCIES OF GRC WIND TURBINE DRIVETRAIN 

WITH TORSIONAL GEARBOX MODEL

Mode Frequency (Hz)

1 0

2 2.44

3 154

4 307

5 353

6 748

7 1020

8 1530

2000 4000 6000 8000 10000 12000 14000 -200

-150

-100

-50

0

50

100

 p q ( ) y ( )

Magnitude (dB)

Frequency (Hz)

Fig. 8. Frequency response function of three-planet planetary gear stage for 

the gearbox presented in [6]

Fig. 9. Proposed schematic of integrating the Simscape drivetrain model 

into the FAST aeroelastic CAE tool

0 5 10 15 20 25 30 35 40 45 50

22

22.2

22.4

22.6

Time [s]

Rotor Speed [RPM]

with Torsional GB Model

Two-Mass Model

Fig. 10. Rotor speed response
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FAST internally calculates the input aerodynamic torque 

Qaero from the defined wind profile, but does not provide 

this torque as an output. However, as the rotor acceleration 

αrot is an available FAST output, the aerodynamic torque 

Qaero can be reconstructed using (6) to be one of the inputs 

to the external drivetrain model. In this process, the rotor 

inertia Jrot is assumed constant and replicated in the external

drivetrain model. The rotor inertia is connected to the 

flexible low-speed shaft, the purely torsional gearbox 

model, the high-speed shaft, and the generator inertia. The 

electrical machine and grid model will take the generator 

speed and provide the generator electromagnetic torque to 

the drivetrain.

The rotor opposing torque Qopp is required as an input to 

the FAST drivetrain model as well as to calculate the 

aerodynamic torque Qaero. In SimDriveline, this rotoropposing torque can be retrieved by utilizing the torque

sensor element behind the built rotor body. In general, 

torque, velocity, and angular-position sensor elements can 

be placed flexibly within the Simscape drivetrain model to 

monitor the response of the drivetrain under various load 

conditions. 

In the remainder of this section, simulation results 

showing the effectiveness of the torsional model of the 

gearbox under different transient load cases are presented. 

Simulations using the FAST wind turbine aeroelastic code 

were conducted in the Simulink environment. In the 

simulations, all available wind turbine flexible modes in 

FAST—including that of the blades, tower, and 

drivetrain—were activated. No damping is defined within 

the drivetrain model to highlight the transient response of 

the drivetrain. Aerodynamic damping computed within 

FAST is the only source of damping that stabilizes the 

overall drivetrain response. The results are compared with 

those using an undamped two-mass model of the drivetrain

inherent in FAST. 

A. Transient Response Caused By Wind Excitation

This simulation was performed under a constant wind 

speed of 9 m/s (below the rated wind speed), and the 

turbine speed was initialized to be 22.1 RPM. The start of 

simulation effectively imparts a large step input to the 

system that can excite all of the drivetrain modes, especially 

during the transient period. 

Fig. 10 shows the turbine rotor speed using the 

integrated drivetrain model as well as the inherent FAST 

two-mass model. The rotor speed steadily increases to reach 

the optimal tip-speed ratio. Both models are in good 

agreement in the speed response of the turbine. Fig. 11 is 

the transient response comparison of the rotor torque 

between two-mass model and the inclusion of gearbox 

model. This figure reveals the importance of including the 

detail model of the gearbox.

Fig. 12 highlights the distinction between the two models 

in predicting the steady-state load response of the 

drivetrain. There are some frequency components coming 

from other parts of the turbine structure. The frequency of 

0.4 Hz comes from the tower fore aft mode, whereas the 

frequency of 1.1 Hz and its harmonics come from the blade 

pass frequency (3P). The response of the drivetrain model 

with the purely torsional gearbox is particularly high at two 

times the blade pass frequency (i.e., 6P) of 2.25 Hz because 

it is quite close to the estimated first eigenfrequency of 2.44 

Hz. Thus, it predicts amplification of load caused by

resonance at the wind speed of 9 m/s. On the other hand,

the two-mass drivetrain model estimates an eigenfrequency 

of 2.96 Hz, which is at some distance from the harmonics of 

the blade pass frequency, and hence predicts much less

resonance. 

B. Transient Response Resulting From Grid Excitation

Another transient load can arise from excitations on the 

grid. One example of grid excitation is simulated in this 

subsection to predict loads on the gearbox. A voltage drop 

for 0.15 second, from 100% to 90% and back to 100% of 

the nominal root mean square voltage, is simulated after the 

turbine has reached steady state.

0 1 2 3 4 5 6 7 8 9 10 -200

0

200

400

600

Time [s]

Rotor Torque [kNm]

with Torsional GB Model

Two-Mass Model

0 50 100 150 200 250 300 350 0

2

4

6

8

Frequency [Hz]

Magnitude [kNm]

Fig. 11. Transient response comparison of the rotor torque 

40 41 42 43 44 45 46 47 48 49 50 147.5

148

148.5

149

Time [s]

Rotor Torque [kNm]

0 1 2 3 4 5 6 7 8 9 10 0

0.1

0.2

Frequency [Hz]

Magnitude [kNm]

with Torsional GB Model

Two-Mass Model

Fig. 12. Steady-state response comparison of the rotor torque
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44.5 45 45.5 46 600

650

700

Time (s)

Line-Line Voltage (VRMS)

44.5 45 45.5 46

1000

2000

3000

Time (s)

Electromagnetic Torque (kNm)

Fig. 13. Electromagnetic torque excitations resulting from a voltage drop 

on the grid

As shown in Fig. 13, this voltage drop results in 

harmonic torque excitations onto the drivetrain with 

frequencies of 50.78 Hz and 56.15 Hz. These frequencies 

are inherent to the generator characteristic. It is important to 

note that the frequency component of this torque excitation 

may cause resonances if the frequency matches any of the 

drivetrain eigenfrequencies. These resonances cannot be 

predicted using the standard two-mass model, because the 

two-mass model can predict only the lowest eigenfrequency 

of the drivetrain. 

Fig. 14 illustrates how this load is transmitted to each 

stage of the multistage gearbox. The torques acting on the 

high-speed side of each gear stage (the pinion of each 

parallel gear stage and the sun gear of the planetary gear 

stage) are shown in both the time and frequency domains. 

The pinion that is directly connected with the generator 

through the high-speed shaft experiences the largest 

proportion of high-frequency loads caused by grid 

excitations compared to other gears. Therefore, it is most 

prone to failures from fatigue in the event of grid 

disturbances. Sudden increases in the generator 

electromagnetic torque excite the two lowest and most 

dominant modes of the drivetrain (i.e., 2.44 Hz and 154 

Hz). This torque also excites the system at its excitation 

frequencies of 50.78 Hz and 56.15 Hz, but with less 

dominant effect than at the eigenfrequencies. The 

transmitted loads on the gear are reduced as the gear gets 

further from the source of excitation, but the most dominant 

drivetrain eigenfrequency of 2.44 Hz prevails during the 

transient regime.

The developed high-fidelity model can also be used in 

designing drivetrain components to preserve or extend the 

life of the gearbox. The stiffness of the high-speed shaft, 

which connects the generator and the gearbox, was varied 

to investigate its influence on the load transmitted to the 

gearbox. In practice, this stiffness can be varied by altering 

the size of the shaft. In simulation analysis, the stiffness was 

varied to 0.1 and 10 times of the nominal value.

The transmitted loads on the pinion of the second gear 

stage were evaluated and shown in Fig. 15. A lower 

stiffness appears to transmit more severe loads onto the 

pinion, whereas a higher stiffness slightly reduces the loads 

at the start. A higher stiffness can be achieved by using a 

shorter and/or larger diameter shaft. The developed model 

can help in choosing the optimal stiffness value to meet 

design and cost specifications as well as to maintain a 

certain range of transmitted loads onto the gear.

(a) (b)

Fig. 14. Transmitted loads onto the gears because of grid excitation in the (a) time domain and (b) frequency domain

44.5 45 45.5 46

1800

1850

Torque (Nm)

Pinion of the 2nd Parallel Gear Stage

44.5 45 45.5 46

7100

7200

7300

7400

Torque (Nm)

Pinion of the 1st Parallel Gear Stage

44.5 45 45.5 46

25.5

26

26.5

Sun Gear

Time (s)

Torque (kNm)

0 50 100 150 200

0

5

10

Magnitude (Nm)

Pinion of the 2nd Parallel Gear Stage

0 50 100 150 200

0

20

40

Magnitude (Nm)

Pinion of the 1st Parallel Gear Stage

0 50 100 150 200

0

100

200

Magnitude (Nm)

Frequency (Hz)

Sun Gear
8 This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

44.5 45 45.5 46 46.5 47

1770

1780

1790

1800

1810

1820

1830

1840

1850

1860

Time (s)

Torque (Nm)

10% Initial Stiffness

Initial HSS Stiffness

1000% Initial Stiffness

Fig. 15. Loads on pinion of second parallel gear stage under various highspeed shaft stiffness values

VI. CONCLUSIONS AND FUTURE WORK

In this work, the capability of the FAST CAE tool is 

enhanced through integration of a dynamic model of a 

drivetrain built using Simscape in the MATLAB/Simulink 

environment. As described in this study, implementation of 

the developed drivetrain model enables the software tool to 

be used in a number of ways. First, the model can be 

simulated under different wind and grid conditions to yield 

further insight into the drivetrain dynamics in terms of 

predicting possible resonant excitations. Second, the tool 

can be used to simulate and understand transient loads and 

their couplings across the drivetrain components. Third, the 

model can be used to design the various flexible 

components of the drivetrain such that transmitted loads can 

be reduced on the gearbox.

Ongoing and future work includes extending to a timevarying meshing stiffness in the gearbox model. As evenhigher-fidelity models are developed to give more insight 

into the dynamics of the drivetrain, further control strategies 

will be explored to attenuate unwanted loads and vibrations 

in the drivetrain. In the future, we also intend to perform 

hardware testing on the 2.5-MW or 5-MW dynamometer at

the NWTC of NREL. 

VII. ACKNOWLEDGMENT

The authors thank Dr. Khanh Nguyen and Dr. Jason 

Jonkman for fruitful discussions about integrating FAST 

with the external drivetrain model.

This work was supported by the U.S. Department of 

Energy under Contract No. DE-AC36-08-GO28308 with 

the National Renewable Energy Laboratory.

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9 This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

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