This document describes the development of a low-cost test rig for evaluating fault diagnosis techniques for wind turbine generators (WTGs). The test rig uses a DC motor coupled to a self-excited induction generator (SEIG) and interfaced with a wind turbine model in LabVIEW. It is able to simulate the varying wind conditions seen by real WTGs. The document presents the modeling of the wind turbine, DC motor control strategy, SEIG modeling, and integration of the system instrumentation. It is shown that the test rig can effectively test fault diagnosis methods for the SEIG under non-stationary conditions like those experienced by WTGs. In particular, the paper evaluates the use of wavelet transform analysis for detecting

1. Development of a low cost test rig for standalone WECS subject
to electrical faults
Himani n
, Ratna Dahiya
Department of Electrical Engineering National Institute of Technology Kurukshetra, India
a r t i c l e i n f o
Article history:
Received 30 October 2015
Received in revised form
24 July 2016
Accepted 15 August 2016
Available online 21 September 2016
This paper was recommended for publica-
tion by Jeff Pieper
Keywords:
Wind turbine generator (WTG)
Wind Turbine Emulator
Non-stationary signals
Self-excited induction generator (SEIG)
Condition monitoring
Short circuit fault
a b s t r a c t
In this paper, a contribution to the development of low-cost wind turbine (WT) test rig for stator fault
diagnosis of wind turbine generator is proposed. The test rig is developed using a 2.5 kW, 1750 RPM DC
motor coupled to a 1.5 kW, 1500 RPM self-excited induction generator interfaced with a WT mathe-
matical model in LabVIEW. The performance of the test rig is benchmarked with already proven wind
turbine test rigs. In order to detect the stator faults using non-stationary signals in self-excited induction
generator, an online fault diagnostic technique of DWT-based multi-resolution analysis is proposed. It
has been experimentally proven that for varying wind conditions wavelet decomposition allows good
differentiation between faulty and healthy conditions leading to an effective diagnostic procedure for
wind turbine condition monitoring.
& 2016 ISA. Published by Elsevier Ltd. All rights reserved.
1. Introduction
The recent technological developments of the wind turbine
(WT) systems focus on O&M cost reduction [1] and operational
reliability. Condition monitoring of wind turbine generator (WTG)
is important as defects in generators have shown to be a major
reason for WT downtime [2,3]. Among all the possible faults
occurring in WTG, faults related to stator comprise a significant
percentage [4,5]. Stator short circuit fault is quite common in
electrical machines [2]. In most cases, it begins as an inter-turn
fault and eventually grows in to major one's such as coil to coil,
phase to phase, phase open circuit and phase to ground, that may
lead to system break down. It can cause catastrophic damage to
the WTG in a very short time, making any fault compensation
impossible. This demands better fault-detection and remediation
strategies [5].
The evaluation of fault diagnosis method on the real physical
system by creating a fault can be dangerous as this may lead to the
destruction of wind turbine [6]. Therefore, for CM evaluation,
adequate models of the test rigs are needed [6]. The test rig should
drive the WTG in a similar way as a wind turbine imitating the
non-stationary conditions and the torque developed for a given
wind velocity under laboratory conditions [7]. The widely used
and studied standalone wind turbine systems are based on
induction generators due to their advantages over synchronous
generators, such as low cost, small size, and low maintenance
requirements. Therefore, induction generators are the suitable
option as a WTG for isolated applications [8].
Though many researchers have worked on the stator fault both
for induction motors as well as for induction generator, but a very
few have done the research using the dynamic characteristic of the
WT. WTs are of variable speed; variable load machines as a result,
standard analysis techniques like Fourier analysis cannot be
directly applied simply to the monitoring of non-stationary signals
produced by WT [9,10]. Non-stationary signal faults detection by
using current, voltage [11], power [12,13] has been performed
using advanced signal processing techniques such as instanta-
neous frequency [10,14], wavelet-based techniques [5,15],
sequence network analysis[16], power signature analysis [12] and
Short-Time Fourier transform (STFT) [6]. The quantification of the
fault has been done using DWT [24,25], artificial neural networks
[26,27]. Both these technique has been used successfully to model
complex nonlinear dynamic systems.
In most of the earlier research papers for CM, the various
experimental setups are used for the non-stationary signal gen-
eration at the generator output. These setups include test rig
controlled by aerodynamics forces [12,17], wind tunnel [18], speed
fluctuations by PLC [14] and analog inputs [10].
Contents lists available at ScienceDirect
journal homepage: www.elsevier.com/locate/isatrans
ISA Transactions
http://dx.doi.org/10.1016/j.isatra.2016.08.013
0019-0578/& 2016 ISA. Published by Elsevier Ltd. All rights reserved.
n
Corresponding author.
E-mail addresses: singlahimani@gmail.com (Himani),
ratna_dahiya@yahoo.co.in (R. Dahiya).
ISA Transactions 65 (2016) 537–546

2. The main objective of this paper is modeling of the low-cost
test rig and fault diagnosis of stator faults in SEIG (self-excited
induction generator) in non-stationary conditions.
The novelties of this research paper are summarized as follows:
For CM of WT, a low-cost setup is designed and developed. The
real-time interface for monitoring and control of WTE is
designed in LabVIEW. It controls the prime mover according
to the WT principle and follows the torque speed
characteristics of WT.
Hardware part of the emulator is developed to imitate the WT
characteristics. Each component has been designed keeping in
consideration flexibility and cost. The setup can be reconfigured
for given specifications as each component is designed inde-
pendently. The total cost of the setup is approx. $1200.
The designed WT test rig follows the power curves of WT
characteristics. The results show a very good operation in the
whole operating range of the SCIG and due to its simplicity,
real-time interfacing and control for monitoring; – it is suitable
for practical use. Benchmarking of stator current signatures
with already proven setup – available in the literature [9,13,17]
(University of Manchester and Durham University).
An experimental study shows that the proposed approach using
the DWT quantitative analysis offers a good diagnostic cap-
ability for interpreting the non-stationary WT CM signals. The
method is in real time, it detects the fault at the moment of its
existence in WT, which allows easy maintenance without
damaging other parts of the system.
The organization of this paper is as follows. The development of
a wind turbine test rig is discussed in Section 2. Section 3 presents
the experimental test results. The stator short circuit fault results
are discussed in Section 4. The conclusion is given in Section 5.
2. Development of wind turbine test rig
2.1. Wind turbine modeling
The aerodynamic power output of wind turbines (Po) can be
modeled as [7,19]:
Po ¼ 1=2ρCpAv3
ð1Þ
where ρ¼air density (kg/m3
), A¼WT swept area (m2
), v¼wind
speed (m/s)
The fraction of the power extracted by the turbine is called the
power coefficient (Cp), which depends on wind speed, shaft speed
and the mechanical parameters such as shape and pitch angle (β)
of the blades. The maximum value of Cp, theoretically given by the
Betz limit [7,19] is 0.593. It is a function of tip speed ratio (TSR) (λ),
which is defined as the ratio between the linear velocity of blade
tip and the wind velocity. The TSR is given as:
λ ¼
ωwR
v
ð2Þ
where ωw ¼angular speed of WT (rad/s), R¼blade radius (m)
The mechanical torque generated can be calculated from the
WT power and the shaft speed as given by [19]:
Tw ¼
Po
ωw
¼
1
2λ
ρCpπR3
v2
ð3Þ
2.2. DC motor control strategy
The DC motor control is based on the armature current reg-
ulation. The schematic representation of a separately excited DC
motor is in Fig. 1 [7].
Ve ¼ IeRe þLe
dIe
dt
: For the excitation ð4Þ
Va ¼ IaRa þLa
dIa
dt
þLmIeωm: For the excitation ð5Þ
ζem ¼ ζr þfωm þJ
dωm
dt
ð6Þ
ζem ¼ KcIaKe ¼ LmIeKc ¼ Ke ð7Þ
ζr: Resistive Torque; ζem: Electromagnetic Torque; Lm: Mutual
inductance excitation armature; f: Coefficient of friction; J: Inertia
moment; Ke: Torque constant; ωm ¼angular speed of DC motor; Va
is the voltage applied to the motor armature, Ra ,Re are the resis-
tance; La ,Le are the inductance of the armature circuit and field
respectively.
The DC motor characteristic is such that, the mechanical power
output of the machine is a function of the armature voltage Va and
angular speed ωm under steady state. When a constant Va is
applied to the motor the output power is a conic function of speed
ωm.
2.3. Self-excited induction generator (SEIG) modeling
The generated stator voltage and current are derived from d–q
axis values using the equations below.
Fig. 2(i) and (ii) shows the equivalent d–q axis circuit of SEIG.
Across the stator windings of the generator a capacitor is con-
nected. Using Kirchhoff’s voltage the loop Eqs. (8)–(11) of the SEIG
equivalent circuit are written as [214]:
Rsiqs þLIs
diqs
dt
þLm
diqr
dt
þ
1
C
diqs
dt
¼ Vcq ð8Þ
Rriqr þLIr
diqr
dt
þLm
diqs
dt
þ
1
C
diqr
dt
¼ ωrλdr ð9Þ
Rsids þLIs
dids
dt
þLm
didr
dt
þ
1
C
dids
dt
¼ ÀVcd ð10Þ
Rridr þLIr
didr
dt
þLm
dids
dt
þ
1
C
didr
dt
¼ Àωrλqr ð11Þ
At each step of integration, the magnetizing current has to be
updated. Therefore, by using the Eq. (12), the new magnitude of
the magnetizing current is obtained
jim j ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ðiqs þiqrÞ2
þðiqs þiqrÞ2
q
ð12Þ
Fig. 1. The electric model of the DC motor.
Himani, R. Dahiya / ISA Transactions 65 (2016) 537–546538

3. Hence, in the steady state the magnitude of the generated air
gap voltage of SEIG is
Eg ¼ ωLm imj
ð13Þ
Lmis not a constant but depends on magnetizing current imj
.
This dependency is determined by a synchronous impedance test
and can be expressed as
Lm ¼ f m imj
ð14Þ
The developed electromagnetic torque Te and torque balance
equations are
Te ¼
3
2
P
2
Lmðidriqs ÀiqridsÞ ð15Þ
2.4. Capacitor bank model
Current drawn by each capacitor [22]: Icap ¼
Q1
Ell
¼ ¼ 0:849 A; Capacitive reactance: Xc ¼ Ell
Icap
¼ 1
2ÂπÂf ÂC
¼ 488:38Ω:
So, the capacitance (connected in delta) required for excitation in
full load condition is: C ¼ 1
2ÂπÂf ÂXc
¼ 6:52μF:
2.5. Stator modeling taking into account stator fault
The dynamic model for the stator windings of the three-phase
squirrel cage induction generator (Fig. 3) is developed and the
relevant volt–ampere equations are:
ÀVs ¼ RsIs þ
d∅s
dt
ð16Þ
Vs ¼ Vsa; Vsb; Vsc½ ŠT
is the stator voltage vector; Is ¼ Isa; Isb; Isc½ ŠT
is
the stator current vector; Rs ¼ diag Rsa; Rsb; Rsc½ Š is a 3 by 3 stator
resistance matrix;.
∅s ¼ ∅sa; ∅sb; ∅Vsc½ ŠT
is a stator flux vector.
With the Park transformation ðPsÞ, the voltage equations for the
stator windings can be written as:
ÀVsdq ¼ PsRsIsdqPÀ1
s þPs
dðPÀ1
s ∅sdqÞ
dt
¼ RSDQ Isdq þ
d∅sdq
dt
þωs
0 À1
1 0
!
∅sdq
ð17Þ
where RSDQ is the equivalent resistance matrix and is given by:
RSDQ ¼ PsRsPÀ 1
s ¼
Rds Rsdq
Rsdq Rqs
#
ð18Þ
PS ¼
ffiffiffi
2
3
r
sin ðθÞ sin θÀ2π
3
À Á
sin θþ2π
3
À Á
cos θ
À Á
cos θÀ2π
3
À Á
cos θþ2π
3
À Á
#
ð19Þ
is not a constant but depends on magnetizing current.
2.6. Wind Turbine Emulator (WTE)
The emulation scheme is shown in Fig. 4. Steady-state char-
acteristics of a given WT at various wind velocity are studied. The
wind speed, turbine radius and angular speed of motor are con-
sidered as inputs of this model. To calculate the reference torque,
the control program reads wind velocity from an input file, shaft
speed from tacho-generator and supply current from the current
transducer [7]. The wind data file can be generated in different
ways, depending on the desired test conditions, or even being real
data from an anemometer. The electromagnetic torque of a DC
motor should be equal to the reference torque of WT. From the
mathematical model, the reference armature current is obtained,
which is used to control the armature current of the DC motor by
using PI controller and semi converter rectifier. It gives controlled
rectified voltage depending upon the triggering pulses to thyr-
istors for the regulation of DC motor armature voltage. The var-
iation in armature voltage controls the motor current in accor-
dance with the reference current and the system reaches a steady
state. Here, the DC motor current is directly controlled so that the
required torque is directly proportional to the current.
Fig. 2. d–q axes equivalent circuit of SEIG.
Fig. 3. SEIG with a capacitor excitation system driven by the wind.
Fig. 4. Schematic of Wind Turbine Emulator.
Himani, R. Dahiya / ISA Transactions 65 (2016) 537–546 539

4. 2.7. Integration of system instrumentation
A low-cost test bench equipped with 220 V, 1.5 kW squirrel
cage induction machine with the prime mover, a 2.5 kW sepa-
rately excited DC machine has been designed [20,21]. Based on
required specifications and cost, Advantech 4704 based data
acquisition card is selected. The model of a wind turbine and the PI
controller are developed using LabVIEW software. The control
panel designed in LabVIEW (Fig. 5) allows real time communica-
tion between the setup and the user. Turbine parameters such as
the wind velocity and turbine radius can be set by the user
through the software. Various off grid-connected wind turbine
system parameters as stator voltage, generated current and speed
of machine are sampled at 2 kHz sampling rate using DAQ. A low-
cost interfacing circuitry including the current transducer (CTs)
and voltage transducer (VTs) are used to monitor the generator
currents and terminal voltages. Signal conditioning is done to
provide the interface the signals/sensors and the data acquisition
system. The coordinate transformations, controllers and further
calculations are carried out and current reference values are given
to the PI controller. The block diagram of test rig is shown Fig. 6
and parameters are reported in Appendix.
3. Experimental results of wind turbine test rig
The experimental results of the designed test rig are verified
with the test rigs made at University of Manchester, UK and Dur-
ham University UK [12,13,17]. The specifications of these test rigs
are tabled in Appendix.
The equations describing the stator current spectral content for
the healthy conditions is [12]
f
k
ind ¼ 6kð1ÀsÞ7l f
ð20Þ
Fig. 5. Control panel for Wind Turbine Emulation.
Fig. 6. Block diagram of the system instrumentation.
Himani, R. Dahiya / ISA Transactions 65 (2016) 537–546540

5. where f is the fundamental frequency, s is the induction generator
fractional slip, l ¼ 1; 2; 3…, k ¼ 1; 2; 3…: Constants k and l relate
respectively to air-gap field space harmonics resulting from the
layout of the machine and supply time harmonics in the current.
For benchmarking [12], reduced set of steady state frequencies are
listed in Table 1.
The test rig was run up to the required super-synchronous
speed for a constant wind speed of 7 m/s. The generated current is
acquired using the current transducers and DAQ. The healthy
current spectra in Figs. 7 [12] and Fig. 8 indicates a comparable
spectral content despite different levels of rotor resistance.
Frequencies ‘c’ and ‘d’ are related directly to the fundamental
frequency and 3rd harmonics, consistent for all the test rigs.
Machine dependent frequencies ‘a’ and ‘b’ are also present in all
the machines. Frequencies ‘e’ and ‘f ’ also show the comparable
results despite different operating conditions.
4. Short circuit fault
In order to analyze the phase-to-phase faults, a short-circuit
between two phases is considered. This fault can produce specified
frequency components f short in different signals [15,23] as:
f short ¼ f s n 1Àsð Þ
p 7k
k ¼ 1; 3; …:: n ¼ 1; 2; 3……::Þ
ð21Þ
where p is the number of pole-pairs, s is the per-unit slip and f s is
the supply frequency.
4.1. Stator winding fault-experimentation
Fault conditions were emulated by introducing phase to phase
short circuit fault through a resistor. Terminal voltage contains
unique fault frequency components that can be used for the stator
winding fault detection. The only difficulty with this technique is
that the data needs to be collected very quickly. With the appli-
cation of short circuit fault, the large amount of current flows
through the shortest path causing the machine to heat up very
quickly. So the fault was applied for a very short duration of 0.2 s.
Application of this method allowed multiple tests of the WTG to
be performed without permanent damage. A terminal voltage
measured from the test rig under various cases (Table 2) has been
processed for CM of WTG for the stator winding fault diagnosis.
The first set of experiments was performed under static conditions
with no load and a constant wind speed of 7 m/s as shown in Fig. 9; the
shaft speed almost remains constant at 1480 RPM, generated voltage is
220 V and the generated current is almost negligible.
The next set of experiments was performed with short circuit fault
at no load, with constant and then variable wind conditions applied
through the WT model as shown in Fig. 10. At full load, the generated
current is max 0.5 A and terminal voltage is 220 V as shown in Fig. 11.
Similarly Figs 12 and 13 show the generated current and terminal
voltage at constant wind speed and variable wind speed respectively.
4.2. Signal processing using frequency domain analysis
The power spectrum of voltage in healthy conditions at no load
is shown in Fig. 14(a) and under full load is given in Fig. 14(b). The
odd harmonics (i.e. 1st, 3rd, 5th), at 50 Hz, 143 Hz and 241 Hz are
most evident under no load conditions and 50 Hz, 143 Hz and
239 Hz in full load conditions. In faulty condition and at no load,
with n¼3, k¼1, and slip¼0.22, side bands at 20.75 Hz, 79.25 Hz,
120.75 Hz and 179.25 Hz around first and third harmonics are
quite evident as shown in Fig. 15(a). Similarly, side bands at
22.5 Hz, 77.5 Hz, 118.5 Hz and 177.5 Hz are evident at full load
Table 1
Constants for healthy current spectra.
Frequency label Constant Line current
j k L
a, b (healthy) 1 1 1 f
k
ind ¼ 6kð1ÀsÞ8lj
f
c, d 1 4 1 f
k
ind ¼
k
pð1ÀsÞ8l
f
e 1 8 1 f
k
ind ¼
k
pð1ÀsÞ8l
f 1 16 1 f
k
ind ¼
k
pð1ÀsÞ8l
Fig. 7. Current spectra of test rig made at (a) University of Manchester (b) Durham
University.
Fig. 8. Current spectra of test rig made at NITK.
Table 2
Experimental conditions for short winding fault detection.
Case no. Load Wind speed WTG condition Observed output
1 No-load Constant Healthy Fig. 9
2 No-load Constant Faulty Fig. 10(a)
3 No-load Variable Faulty Fig. 10(b)
4 Full-load Constant Healthy Fig. 11
5 Full-load Constant Faulty Fig. 12
6 Full-load Variable Faulty Fig. 13
Himani, R. Dahiya / ISA Transactions 65 (2016) 537–546 541

6. conditions as shown in Fig. 15(b). Under variable wind speed
conditions, the slip varies; the magnitudes of the above compo-
nents of the fault are spread in a bandwidth proportional to the
variation of speed as shown in Fig. 16(a). There are no clear side-
bands available in case of no load under variable wind conditions.
The same phenomena also exist under full load conditions as
shown in Fig. 16(b). The results are summarized in Table 3.
As a result of varying slip under transit conditions, the use of
Fourier analysis may result in an erroneous diagnosis. For pro-
viding a reliable diagnosis procedure, a more appropriate techni-
que is required.
4.3. Stator winding fault diagnosis using discrete wavelet
transformation
An effective fault diagnosis requires the measurements of a
quantity-sensitive to the faults and a suitable method to obtain a
diagnostic index and a threshold stating the edge between faulty and
healthy condition. Under variable-speed conditions, the fault fre-
quency components, whose amplitude is usually monitored for fault
detection, are spread in frequency with width which is related to the
load, speed and slip variations. The wavelet analysis allows the pro-
cessing of the stator terminal voltage in such a manner that the fault
Fig. 9. At constant wind speed, no load, healthy conditions- (a) terminal voltage (b) shaft speed (c) line current.
Fig. 10. Terminal voltage with short circuit fault-at no-load (a) constant wind speed (b) variable wind speed.
Fig. 11. Terminal voltage in healthy conditions- on load and constant wind speed (a) terminal voltage (b) line current.
Himani, R. Dahiya / ISA Transactions 65 (2016) 537–546542

7. Fig. 12. Terminal voltage with short circuit fault on load and constant wind speed (a) terminal voltage (b) line current .
Fig. 13. Terminal voltage with short circuit fault on load and variable wind speed (a) terminal voltage (b) line current.
Fig. 14. Power spectrum of generated voltage in healthy conditions at constant wind speed (a) at no load (b) at full load.
Fig. 15. Power spectrum of voltage in faulty conditions at constant wind speed (a) at no load (b) at full load.
Himani, R. Dahiya / ISA Transactions 65 (2016) 537–546 543

8. frequency components can be shifted to a dedicated frequency band.
In this way, the information related to the fault can be isolated and
confined to obtaining a diagnostic index and a threshold stating the
edge between faulty and healthy condition. As illustrated by Fig. 17,
the logarithmically spaced frequency bands are obtained by dividing
the frequency content of the original signal using DWT.
A high-order mother wavelet is suitable for carrying out the DWT.
If a low-order wavelet is used, there is an increase in the overlap
between adjacent frequency bands and the frequency response gets
worse.In this paper, a 10th order mother wavelet of Daubechies family
has been chosen [24,25]. In order to cover the frequency band with a
sampling frequency of fs¼2000 Hz, an eight level decomposition
(J¼8) is chosen [5]. In this range, we can track the characteristics of
default frequency components. For fault analysis, a diagnosis index
based on multiresolution mean power indicator is introduced. The
signal is decomposed at different frequency levels by MRA analysis. To
reduce the computational time, only detail level dj is considered. In
case any fault occurs, the distribution of the energy in signal changes
at the resolution levels associated with the characteristic frequency
bands of the default. Therefore, the increase in the energy constrained
to certain details is measured as the appearance of an irregularity. The
mean power concentrated in each detail dj generated by the MRA of
the generated voltage signal is defined as below [24,25]:
mPdj ¼
1
N
XN
n ¼ 1
djðnÞ 2
ð22Þ
Here
N¼number of samples
j¼level decomposition.
The data flow of the MRA feature vector extraction is shown
Fig. 18.
The mean power of the details (d1 Àd8Þ resulting from wavelet
decomposition at no-load (Case 1, 2, 3 Table 2) is shown in Fig. 19
(a) and under load conditions (Case 4, 5, 6 Table 2) is shown in
Fig. 19(b).
Fig. 16. Power spectrum of voltage in faulty conditions at variable wind speed (a) at no load (b) at full load.
Table 3
Power spectrum analysis for short circuited winding fault.
Case no. Condition Lower Side Band Upper Side Band Observation
FF Mag. FF Mag.
Case 1 Fig. 14(a) – No Visible side bands in healthy conditions
Case 2 Fig. 14(a) 20.75 8 79.25 8 Visible
Case 3 Fig. 16(a) – – – – No Visible side bands because of variable wind speed.
Case 4 Fig. 14(b) – No Visible side bands in healthy conditions
Case 5 Fig. 15(b) 22.5 15 77.5 15 Visible
Case 6 Fig. 16(b) – – – – No Visible side bands because of variable wind speed.
Fig. 17. Wavelet tree decomposition with three details levels.
Fig. 18. Multiresolution feature vector extraction.
Himani, R. Dahiya / ISA Transactions 65 (2016) 537–546544

9. There is a significant discrimination between healthy and faulty
conditions. The fault discrimination is significantly increased at
load in varying wind conditions especially at d5 (around funda-
mental frequencies). By comparing the detail signal d5 calculated
in fault conditions (Fig. 19(a) and (b)), it is clearly visible that there
is an amplitude evolution of the faulty component for loaded
conditions.
5. Conclusion
The paper presents the development of a low-cost wind tur-
bine test rig for detection and diagnosis of the stator winding fault.
Various tests have been carried out on the test rig and the results
are benchmarked with already proven test rigs developed at the
Durham University and the University of Manchester. The
measured current spectra are in good agreement and shows
comparable results despite different operating conditions. The
findings of research presented in this paper can be summarized
as follows.
The designed wind turbine test rig follows the power curves of
wind turbine characteristics. The result shows a very good
operation in the whole operating range of the SCIG and due to
its simplicity, real-time interfacing, and control for monitoring –
it is suitable for practical use.
Stator winding fault can be successfully detected in normal IM
using FFT analysis whereas in the case of WTG, due to variations
in wind speed and hence slip variations, fault frequency com-
ponents are not visible in FFT spectrum.
Quantitative analysis using wavelet transformation has been
performed to discriminate the healthy from faulty. The analysis
shows the occurrence of characteristic patterns through the
energy of the involved wavelet signals and oscillations appear-
ing in the wavelet signals. The experimental results show that
the proposed approach offers good diagnostic capability com-
pared to the existing techniques.
This work further can be extended to the following topics could
be studied:
Detection of the fault subtypes.
There is the need to study the effect of electric drives, use dif-
ferent generator types-DFIG, WRIG as these may change the
electrical signature's.
The influence of gear box components needs to be investigated.
Appendix A
See Tables A1–A4.
Fig. 19. Mean power of the details d1–d8 resulting from the wavelet decomposition (a) at no load (b) at full load.
Table A1
Wind turbine parameters.
Rated power 500 W
Rated wind speed 7.5 m/s
Radius of WT 1.4 m
Power coefficient 0.48
Table A2
DC motor parameters.
Rated power 2.5 kW
Nominal speed 1750 rpm
Armature resistance (Ra) 1.8711 Ω
Filed resistance (Rf) 470 Ω
Armature inductance (La) 98 mH
Filed inductance (Lf) 14.160 H
Table A3
Induction generator parameters.
Rated power 1.5 kW
Nominal speed 1500 rpm
Table A4
Comparison of test rigs.
Manchester Durham NITK
Generator Type DFIG or WRIG
30 KW
WRIG
30 KW
SCIG 1.5 KW
No. of poles 4 4 4
Converter Back-to-back,
8 kHz switching
None None
DC motor Driving motor
(DC)
40 kW constant
speed
54 kW
variable
speed
2.5 KW
Gearbox None 5:1 Helical None
Data acquisition Hardware Precision
oscilloscope
NI LabVIEW Advantech
Sampling fre-
quency, kHz
2 5 2
MATLAB FFT analysis 0–500 Hz 0–500 Hz
Himani, R. Dahiya / ISA Transactions 65 (2016) 537–546 545

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