ChapterPDF Available

Stator Winding Fault Diagnosis of Permanent Magnet Synchronous Motor-Based DTC-SVM Dedicated to Electric Vehicle

Authors:

Abstract and Figures

Abstract Electric vehicles (EVs) have several advantages such as energy efficiency, virtually lack of pollution, and the availability of electric energy through electric distribution systems. Among the major components of an EV is the block control of the AC motor. Many modern high-performance control technologies are being used in EV controllers, such as direct torque control-based space vector modulation (DTC-SVM). Given that permanent magnet synchronous motor (PMSM) has a small volume, a lightweight, and a high reliability, attracting the interest of EV industrial designers. The fault diagnosis about the PMSM problems is essential because the PMSM has a huge influence on the safety and economic efficiency with the vehicles. In this background, the chapter concerns the modeling and simulation of an electric vehicle drive system including a PMSM-based DTC-SVM without and with stator winding fault. The performance of the motor under both the normal and faulty conditions is simulated. For the diagnosis of the fault, the chapter proposes the technique based on the motor current spectrum analysis.
Content may be subject to copyright.
Selection of our books indexed in the Book Citation Index
in Web of Science™ Core Collection (BKCI)
Interested in publishing with us?
Contact book.department@intechopen.com
Numbers displayed above are based on latest data collected.
For more information visit www.intechopen.com
Open access books available
Countries delivered to Contributors from top 500 universities
International authors and editor s
Our authors are among the
most cited scientists
Downloads
We are IntechOpen,
the world’s leading publisher of
Open Access books
Built by scientists, for scientists
12.2%
124,000
140M
TOP 1%
154
5,000
Chapter
Stator Winding Fault Diagnosis of
Permanent Magnet Synchronous
Motor-Based DTC-SVM Dedicated
to Electric Vehicle Applications
Fatma Ben Salem
Abstract
Electric vehicles (EVs) have several advantages such as energy efficiency, virtu-
ally lack of pollution, and the availability of electric energy through electric distri-
bution systems. Among the major components of an EV is the block control of the
AC motor. Many modern high-performance control technologies are being used in
EV controllers, such as direct torque control-based space vector modulation (DTC-
SVM). Given that permanent magnet synchronous motor (PMSM) has a small
volume, a lightweight, and a high reliability, attracting the interest of EV industrial
designers. The fault diagnosis about the PMSM problems is essential because the
PMSM has a huge influence on the safety and economic efficiency with the vehicles.
In this background, the chapter concerns the modeling and simulation of an electric
vehicle drive system including a PMSM-based DTC-SVM without and with stator
winding fault. The performance of the motor under both the normal and faulty
conditions is simulated. For the diagnosis of the fault, the chapter proposes the
technique based on the motor current spectrum analysis.
Keywords: permanent magnet synchronous motor, electric vehicles, DTC-SVM,
stator winding fault, diagnosis, fault signature extraction
1. Introduction
Electric vehicles (EVs) have several advantages such as energy efficiency, virtu-
ally lack of pollution, and the availability of electric energy through electric distri-
bution systems [14]. Among the major components of an electric vehicle is the
controlling block of the AC motor. The controller should be designed to make the
drive system robust and efficient on both dynamic and steady-state performances.
Many modern high-performance control technologies are being used in EV
controllers, such as direct torque control-based space vector modulation (DTC-
SVM) [512]. The advantage of the DTC-SVM methodology is that it has all the
merits of DTC strategy [1316] by adding the fact of having a fixed switching
frequency, which guarantees low vibrations in order to provide passenger comfort.
Permanent magnet synchronous motor (PMSM) becomes more suitable in wide
speed range application due to their high efficiency and wide constant power speed
range. In fact, the absence of rotor windings and therefore the absence of external
1
excitation increase the efficiency of the PMSM and greatly reduce its maintenance
cost. In addition, the drop in the price of permanent magnets promotes a strong use
of this type of machine particularly in the field of electric vehicle applications
[1720]. In fact, such motor has a small volume, lightweight, and high reliability
attracting the interest of EV industrial designers.
So the fault diagnosis about the PMSM problems is essential because the PMSM
has a huge influence on the safety and economic efficiency of the vehicles. The
stator winding fault is a common fault that has interested researchers for many
years [2127]. It is resulting from the degradation of the inter-turn interphase and
main isolation of the motor winding. This internal fault will increase the torque
ripples that deteriorate machine performance. However such fault can be rapidly
propagated to motor stator turn since it makes a large circulating current in the
shorted path yielding excessive heat. The adverse condition of heat will occasionally
lead to the progressive deterioration and eventual breakdown of winding insulation
in the phase of the motor.
Several methods have been used to detect and estimate the type and the degree
of this fault in PMSM. The motor signature current analysis is one of the most
common online methods for fault detection. In fact, it is widely used in the field of
PMSM fault diagnosis, because it is fast and does not need any specific model. This
method uses spectral analysis techniques by comparing the healthy to the faulty
case; faults can be detected.
In this chapter, as for signal processing tools, fast Fourier transform (FFT) is
used to extract the frequency characteristics of the stator current signal. This
method will be proposed to detect whether a PMSM, under DTC-SVM traction
system, is healthy or faulty.
The chapter is organized as follows. Electric vehicle propulsion system scheme is
developed in Section 2. In Section 3, the mathematical model of PMSM in (a, b, c)
frame in healthy conditions is introduced and which is followed by Section 4, the
PMSM with inter-turn fault modeling. Then, the design of a conventional DTC-
SVM traction system is presented. Simulation result, considering steady-state oper-
ation, is discussed in Section 6.
2. Electric vehicle propulsion system scheme
The major electric propulsion system of EV consists of motor, controller, power
source, charger, transmission device, and wheels (Figure 1) [28]. The proposed
motor drive includes the electric motor (PMSM), power converter (three-phase
voltage inverter), energy storage (battery), and electronic controller (DTC-SVM).
These components are the core of the EV propulsion system.
The tractive effort (Fte) is the force propelling the vehicle forward, transmitted
to the ground through the drive wheels. Consider a vehicle of mass Mv, proceeding
at a velocity Vv,asinFigure 2. The force propelling the vehicle forward, the
tractive effort, has to overcome the following forces:
The rolling resistance force: Frrol ¼μMvg
where μis the coefficient of rolling resistance (μ¼0:005 for tires developed
especially for electric vehicles), Mvis the vehicle mass, and gis the gravitational
constant
The aerodynamic drag: Fad ¼1
2ρACdV2
v
2
Autonomous Vehicle and Smart Traffic
where ρis the air density, Ais the frontal area, Cdis the drag coefficient, and Vv
is the vehicle speed
The hill climbing force: Fhc ¼Mvgsinα
where αis the angle of a slope for a vehicle climbing a hill (in the case of Figure 2
α¼0).
Fte is equal to the sum of the resistance forces [28, 29], as shown in the sequel
equation:
Fte ¼Frrol þFad þFhc (1)
2.1 Voltage source inverter
The DC voltage made constant by the rectifier is delivered by the battery to the
inverter input, which thanks to controlled transistor switches, converts this voltage
to three-phase AC voltage signal with wide-range variable voltage amplitude and
frequency.
The inverter one leg consists of two transistor switches. A simple transistor
switch consists of feedback diode connected in antiparallel with transistor.
Control
Transmission
PMSM
Software:
DTC-SVM
Energy
storage
Two level
inverter
Figure 1.
Components of electric vehicle propulsion system.
Frrol
Fad
Ft
Mvg
Figure 2.
The forces acting on a vehicle.
3
Stator Winding Fault Diagnosis of Permanent Magnet Synchronous Motor-Based DTC-SVM
DOI: http://dx.doi.org/10.5772/intechopen.88784
Feedback diode conducts current when the load current direction is opposite to the
voltage direction.
Assuming that the power devices are ideal, when they are conducting, the voltage
across them is zero and they present an open circuit in their blocking mode. Therefore,
each inverter leg can be represented as an ideal switch. It gives thepossibility to connect
each of the three motor phase coils to a positive or negative voltage of the DC link (E0).
Considering a two-level inverter, presented by Figure 3, the voltage vector of
the three-phase voltage inverter is represented as follows:
Vs
!¼ffiffi
2
3
rSaþSbej2π
3þScej4π
3
hi (2)
where Sa,Sb, and Scare three-phase inverter switching functions, which can
take a logical value of either 0 or 1.
3. Mathematical model of PMSM in (a, b, c) frame
An accurate dynamic model of the PMSM is necessary to study the dynamic
behavior of the machine. Indeed, the dynamic behavior of a PMSM is described in
terms of space variables as follows:
Vabc ¼RsIabc þLs
d
dt Iabc þd
dt Φmabc (3)
considering Vabc ¼vavbvc
½
T,Iabc ¼iaibic
½
T, and
dΦmabc
dt ¼Φmωrcos θr
ðÞcos θr2π
3

cos θrþ2π
3

T
where Vabc,Iabc , and Φmdenote the stator voltages, the stator currents, and the
permanent magnetic flux amplitude, respectively. Rsand Lsdenote the stator resis-
tance and inductance matrix, respectively.
Φmabc ¼Φm
sin θr
ðÞ
sin θr2π
3

sin θrþ2π
3

2
6
6
6
6
6
4
3
7
7
7
7
7
5
(4)
SaSbSc
SaSbSc
E0
2
E0
2
Figure 3.
Two-level three-phase voltage inverter.
4
Autonomous Vehicle and Smart Traffic
since ωr=Npωm,ωrand ωmtorque pulsation and mechanical pulsation, respec-
tively. θris the angular position of the rotor with respect to the magnetic axis of the
phase (a) of the stator, θr=Npθm,θmdenotes the mechanical angular position of the
rotor, and Npis the pole pair number.
The electromagnetic torque can be expressed as follows:
Tem ¼eaiaþebibþecic
Ωm
(5)
where ea,eb, and ecare the emf of the phases a,b, and cand Φmabcf ¼Φmeaebec
½.
Then referring to Eq. (4), the developed equation of electromagnetic torque becomes
Tem ¼
Φmsin θr
ðÞiaþsin θr2π
3

ibþsin θrþ2π
3

ic

Ωm
(6)
The mechanical part of the machine is described by
Jd
dt Ωm¼Tem Tl(7)
where Jis the motor inertia, Tem is the electromagnetic torque, and Tlis the load
torque.
4. Modeling of the PMSM with inter-turn fault
An inter-turn fault represents an insulation failure between two windings in the
same phase of the stator [23].
The representation of a PMSM with stator winding turn fault at phase (a) is
shown in Figure 4;a2and a1represent the shorted turns and the healthy turns,
respectively. The fault is modeled by a small resistance Rfconnected across the
shorted turns. In fact, Rsdenotes the inter-turn short circuit resistance.
To represent the impact of the defect, a new parameter σis introduced. Param-
eter σis defined as the ratio between the number of shorted turns Ncc and the total
number of turns Ns.
The resistances of the sub-winding a1and the shorted sub-winding a2are noted
by Ra1and Ra2, respectively; they are proportional to the number of turns of the
involved parties. Therefore, we can express them according to the resistance Raand
the coefficient σ. So we have
Ra1¼1σðÞRa,R
a2¼σRa(8)
By defining the electrical quantities of the new circuit with the index f,and
refering to Figure 5 the new equations of the motor voltages are reformulated as follows:
Va¼Ra1þRa2
ðÞiaþLa1þLa2þ2Ma1a2
ðÞ
dia
dt þMa1bþMa2b
ðÞ
dib
dt
þMa1cþMa2c
ðÞ
dic
dt þeaþef

Ra2ifLa2þMa1a2
ðÞ
dif
dt
Vb¼RsibþLdib
dt þMa1bþMa2b
ðÞ
dia
dt þMdic
dt þebMa2b
dif
dt
Vc¼RsicþLdic
dt þMa1cþMa2c
ðÞ
dia
dt þMdib
dt þecMa2c
dif
dt
(9)
5
Stator Winding Fault Diagnosis of Permanent Magnet Synchronous Motor-Based DTC-SVM
DOI: http://dx.doi.org/10.5772/intechopen.88784
where L and M denote the phase self-inductance and M is the mutual inductance
between phase windings of healthy PMSM, respectively. And considering
La1¼1σðÞ
2La,La2¼σ2Laand Ma1a2¼σ1σðÞLa.
ib
i
c
ia
Rf
a1a2
b
c
Figure 4.
Equivalent circuit of PMSM under inter-turn fault in phase (a).
if
rf
ea2
eb
ec
Lc
Lb
La1La2
ea1
Ma2b
Ma1b
Ma2c
Ma1c
Va2
Va1
ia
ib
ic
Figure 5.
Schematic representation of an insulation fault between turns on a phase of the stator.
6
Autonomous Vehicle and Smart Traffic
5. Design of a conventional DTC-SVM traction system
The disadvantages of basic DTC is obvious: torque and flux ripples, deteriorated
performance at low speed, and uncontrolled switching frequency of the inverter.
For the defects of basic DTC, much works have been made over the past few
decades. DTC combined with space vector modulation (DTC-SVM) for PMSM is to
accomplish constant switching frequency of the inverter in addition to obtain the
desired torque and stator flux with little ripples by synthesizing an appropriate
voltage space vector through SVM, which is more accurate than that of basic DTC
to compensate the error of desired and actual stator flux.
5.1 Flux reference coordinate computing
The relationship between the voltage, current, and stator flux vectors of a PMSM
is given by
d
dt Φs¼VsRsIs(10)
where Rsis the stator resistance.
The reference of stator flux amplitude and phase is stated using Concordia
quantities as
Φ
s¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ϕ2
αsþϕ2
βs
q,θ
s¼arctan ϕ
βs
ϕ
αs
 (11)
5.2 Voltage reference coordinate computing
The coordinates of references of voltage vectors v
αsand v
βsin (α,β) frame are
determined by the following equations:
v
αs¼ϕ
αsϕαs
Tem þRsiαs
v
βs¼ϕ
βsϕβs
Tem þRsiβs
8
>
>
>
<
>
>
>
:
(12)
These vectors are introduced to the SVM block, which use them to control the
inverter switches (Sa,S
b,S
c).
5.3 SVM block design
The SVM technique refers to a special switching scheme of the six power tran-
sistors of a two-level three-phase voltage inverter. In fact, it uses eight sorts of
different switch modes of the inverter to control the stator flux to advance the
reference flux circle. Eight types of switch modes stand for eight space voltage
vectors that contain six active voltage vectors and two zero voltage vectors as
shown in Figure 6a [30, 31].
Voltage vectors, created by a three-phase PWM inverter, divide the space vector
plane into six sectors: i1ðÞ
π
3<Siiπ
3,i¼1,,6. The determination of the sector,
where the reference voltage vector is located, is done according to
7
Stator Winding Fault Diagnosis of Permanent Magnet Synchronous Motor-Based DTC-SVM
DOI: http://dx.doi.org/10.5772/intechopen.88784
θ
s¼arctan v
βs
v
αs
 (13)
The SVMs principle is to project the desired stator voltage vector V
!sref on the
two adjacent voltage vectors Vi
!and V
!iþ1corresponding to two switching states of
the inverter. Values of these projections provide the determination of desired com-
mutation times Tiand Tiþ1and correspond to two nonzero switching states of the
inverter. To maintain the constant commutation frequency, in the case where
TiþTiþ1Tmod, a zero state of the inverter is applied during the rest of the period
Tmod, i.e., T0¼Tmod TiþTiþ1
ðÞ. In what follows, the study will be limited to
sector S1, as presented in Figure 6b. As the reference voltage vector V
!sref is in sector
S1, it can be compounded by the active voltage vectors V1and V2. The projection on
these adjacent vectors gives the following expression:
V
!sref ¼V
sαþjV
sβ
¼T1
Tmod
V1
!þT2
Tmod
V2
!(14)
where Tmod ¼T1þT2þT0.D1and D2are duties relative to voltages V1and V2.
In sector S1, expressions of the voltage vectors are
V
!1¼ffiffi
2
3
rE0cos 0 þjsin 0ðÞ¼
ffiffi
2
3
rE0
V
!2¼ffiffi
2
3
rE0cos π
3þjsin π
3

8
>
>
>
>
<
>
>
>
>
:
(15)
Expressions of T1and T2are detailed in the sequel:
T1¼ffiffi
3
2
rV
sα1
ffiffi
2
pV
sβ
!
Tmod
E0
T2¼ffiffi
2
pV
sβ
Tmod
E0
8
>
>
>
>
>
<
>
>
>
>
>
:
(16)
β
α
V
2
(110)
V
3
(010)
V
4
(011)
V
5
(001)
V
6
(101)
V
1
(100)
V
0
(000)
V
7
(111)
2
3E0
E0
2
V
V
θ
vs
S1
S2
S3
S4
S5
S6
Vsref
(a)
β
α
V2
V1
2
3E0
E0
2
Vsref
D1
V1
D2
V2
S1
D1=T1
Tmod
D2=T2
Tmod
(b)
Figure 6.
(a) Basic switching vectors and sectors. (b) Projection of the reference voltage vector on two adjacent vectors.
8
Autonomous Vehicle and Smart Traffic
Consequently, the expressions of the duties are given as follows:
D1¼ffiffi
3
2
rV
sα1
ffiffi
2
pV
sβ
!
1
E0
D2¼ffiffi
2
pV
sβ
1
E0
8
>
>
>
>
<
>
>
>
>
:
(17)
The time duration of each nonzero vector is divided equally into two parts, the
time duration of zero vectors is distributed equally to V
!0and V
!7, and thus the
switching sequence of space vector is V
!0,V
!1,V
!2,V
!7,V
!7,V
!2,V
!1,V
!0during the
modulation period.
The duties of each phase of the inverter are calculated as follows:
Da¼D1þD2þ1
2D0
Db¼D2þ1
2D0
Dc¼1
2D0
8
>
>
>
>
>
<
>
>
>
>
>
:
(18)
As D1þD2þD0¼1, duties, given by the system of Eq. (18), turn to be
Da¼1
21þffiffi
3
2
rV
sα
E0þ1
ffiffi
2
pV
sβ
E0
!
Db¼1
21ffiffi
3
2
rV
sα
E0þ1
ffiffi
2
pV
sβ
E0
!
Dc¼1
21ffiffi
3
2
rV
sα
E01
ffiffi
2
pV
sβ
E0
!
8
>
>
>
>
>
>
>
>
>
>
>
<
>
>
>
>
>
>
>
>
>
>
>
:
(19)
5.4 Block diagram of the DTC-SVM
The DTC-SVM block diagram retains all the advantages of the conventional
DTC, such as no coordinate transformation, robust to motor parameters, etc.
Moreover, a SVM block is used to generate the pulses for the inverter in order to
promise a fixed commutation frequency.
Figure 7 illustrates the block diagram of the proposed traction control system,
and the overall control scheme is set as follows:
Including two major loops: the torque control loop and the flux control loop. In
fact, the flux and torque are directly controlled individually. The basic principle
of the direct torque control is to destine the torque error and the flux error in
hysteresis bands by properly choosing the switching states of the inverter.
The speed command is compared with the estimating speed to compute the
speed error. Then, the speed error is processed by the PI speed controller to
obtain the torque command. On the other hand, the flux command is
compared to the estimated flux.
The errors ΔTem and ΔΦsgo through the hysteresis controllers and the vector
selection table to generate the required switching states and therefore the
desired voltage vector.
9
Stator Winding Fault Diagnosis of Permanent Magnet Synchronous Motor-Based DTC-SVM
DOI: http://dx.doi.org/10.5772/intechopen.88784
The controllers produce the voltage command vector; an appropriate space
voltage vector can be generated with SVM.
6. Simulation end discussion
Parameters of the PMSM are listed in Table 1. It has the following ratings: 220 V,
10 kW, and 1470 rpm at 50 Hz.
The parameters of the electric vehicle model are given in Table 2.
6.1 Case study
We consider a vehicle traveling along a straight horizontal road. For Fhc ¼0, the
tractive effort is reduced to Fte ¼Frrol þFad, and the load torque can be expressed as
Tr¼RgrF
rrol þFad
ðÞ¼, where Rg¼1
Gand G denotes the gear ratio.
After development the load torque is
Tl¼K1þK2V2
v(20)
Concordia
transform
Stator flux
estimator
Torque φαs
φβs
Φ
s
Φs
T
em +
-
-
+
θs
cφcτ
iαs iβs
Tem
SVM
V
αs V
βs
estimator
SaSbSc
SaSbSc
Induction
E0
Motor
PI speed
controller
Hysteresis
stator flux
controller
Hysteresis
torque
controller
Vector
selection table
ias ibs ics vas vbsvcs
-
+
m
m
m
Wheel + Gear
Accelerator
Figure 7.
Block diagram of SVM-DTC PMSM drive.
Rs¼0:29ΩLs¼Lr¼50 mH Np¼2
Rr¼0:38ΩM¼47:3mH J¼0:5 Kg.m2
Table 1.
PMSM parameters.
10
Autonomous Vehicle and Smart Traffic
where K1¼RgrμMvgand K2¼1
2RgrρACdV2
v.
The relationship between vehicle speed and motor speed is given by
Vv¼RgrΩm(21)
Simulation tests were performed in healthy and faulty conditions, with the
motor driving a load torque, referring to Eqs. (20) and (21), and can be expressed as
follows:
Tl¼K1þK2Rgr

2Ω2
m(22)
The magnitude of the torque and flux hysteresis bands is 0.01 N.m and
0.001 Wb, respectively.
6.2 Steady-state operation analysis
Figure 8 shows several features of the PMSM drive at steady-state operation
considering a speed Ωm¼750 rpm. Waveforms presented in the left side (subscript
1)ofFigure 8 represent results yielded by the PMSM under DTC-SVM without
fault. The middle side (subscript 2)ofFigure 8 represents the results yielded by
the PMSM under DTC-SVM considering 5% turns are shorted. The right side
r¼0:3mA¼1m2
Mv¼400 Kg Cd¼0:19
G= 0.9 ρ¼1:2 Kg/m3
Table 2.
The electric vehicle model parameters.
0 1 2
0
500
1000 Ωm(rpm)
t (s)
(a1)
0 1 2
0
50
100
150 Tem(Nm)
t (s)
(b1)
0 1 2
0
0.5
1
1.5 Φs(Wb)
t (s)
(c1)
0 1 2
0
500
1000 Ωm(rpm)
t (s)
(a2)
0 1 2
0
50
100
150 Tem(Nm)
t (s)
(b2)
0 1 2
0
0.5
1
1.5 Φs(Wb)
t (s)
(c2)
0
1 2
0
500
1000 Ωm(rpm)
t (s)
(a3)
0
1 2
0
50
100
150 Tem(Nm)
t (s)
(b3)
0
1 2
0
0.5
1
1.5 Φs(Wb)
t (s)
(c3)
0 1 2
0
500
1000 Ωm(rpm)
t (s)
(a1)
0 1 2
0
50
100
150 Tem(Nm)
t (s)
(b1)
0 1 2
0
0.5
1
1.5 Φs(Wb)
t (s)
(c1)
0 1 2
0
500
1000 Ωm(rpm)
t (s)
(a2)
0 1 2
0
50
100
150 Tem(Nm)
t (s)
(b2)
0 1 2
0
0.5
1
1.5 Φs(Wb)
t (s)
(c2)
0
1 2
0
500
1000 Ωm(rpm)
t (s)
(a3)
0
1 2
0
50
100
150 Tem(Nm)
t (s)
(b3)
0
1 2
0
0.5
1
1.5 Φs(Wb)
t (s)
(c3)
Figure 8.
(subscript 1) Considering the PMSM under DTC-SVM without fault, (subscript 2) considering the PMSM
under DTC-SVM allowing for 5% shorted turns, and (subscript 3) considering the PMSM under DTC-SVM
allowing for 25% shorted turns. Legend: (a) speed and its reference, (b) electromagnetic torque, and (c) stator
phase current.
11
Stator Winding Fault Diagnosis of Permanent Magnet Synchronous Motor-Based DTC-SVM
DOI: http://dx.doi.org/10.5772/intechopen.88784
(subscript 3)ofFigure 8 represents the results yielded by PMSM under
DTC-SVM considering 25% turns are shorted.
From the analysis of the previous steady-state simulation results, one can remark
the following:
The speed follows its reference despite the presence of defect. It is clear that in
the case of machine with short circuit fault, the speed oscillates around its
reference with a ripple amplitude that increases when the percentage of turns
shorted increases.
It is seen in Figure 8 when 5 and 25% turns are shorted, respectively, in one of
the phase (a) windings, the increase of torque ripples is notable.
The stator flux shapes, in healthy and faulty conditions, are quite similar.
6.3 Signature extraction based on the analysis of the fault effect
From the analysis of Figure 9, one can notice that a short circuit fault between
turns on phase (a) increases the amplitude of the second harmonic which was too
low in the spectrum of the healthy machine. This harmonic introduced by the defect
presents an amplitude that evolves the rate of defect in the sense that this harmonic
sees its amplitude increases in a proportional way with the increase of the number
of turns short circuited on the phase (a). When we increase the rate of the short
circuit, the amplitude of this harmonic has increased in value.
0 0.01 0.02
−50
0
50 one period of ia
(Ωm=750 rpm)
t (s)
(a1)
0 10 20
0
0.5
1
N
(b1)
0 10 20
0
0.05
0.1
N
(c1)
0 0.01 0.02
−50
0
50 one periode of ia
(Ωm=750 rpm)
t (s)
(a2)
0 10 20
0
0.5
1
N
(b2)
0 10 20
0
0.05
0.1
N
(c2)
0 0
.
0
1
0
.
0
2
−50
0
50 one period of ia
(Ωm=750 rpm)
t (s)
(a3)
0
1
0
2
0
0
0.5
1
N
(b3)
0
1
0
2
0
0
0.05
0.1
N
(c3)
Figure 9.
(a) One period of ias, (b) reduced spectrum of ias with respect to fundamental, and (c) harmonic of ias
(fundamental = 1). (subscript 1) considering the PMSM under DTC-SVM without fault, (subscript 2)
considering the PMSM under DTC-SVM allowing for 5% shorted turns, and (subscript 3) considering the
PMSM under DTC-SVM allowing for 25% shorted turns.
12
Autonomous Vehicle and Smart Traffic
In light of this analysis, it clearly appears that, in general, the harmonic ampli-
tude increases in an apparent way when there is a short circuit fault.
7. Conclusion
The chapter focused on a study and diagnosis of the PMSM under DTC-SVM
integrated in EV propulsion system without and with stator winding fault. Simula-
tion results have been presented in order to demonstrate that in a DTC-SVM PMSM
drive, with stator inter-turn short circuits, the considered defect affects the
dynamics of the motor and introduces a strong second harmonic in the motor
supply currents, which can be used to detect this type of fault. This ascertainment
can be used in the industry for fault detection and diagnosis of stator winding faults
of PMSM.
Author details
Fatma Ben Salem
Control and Energy Management Laboratory (CEMLab), University of Sfax, Sfax
Engineering School, Sfax, Tunisia
*Address all correspondence to: fatma.bensalem@isgis.usf.tn; fatma_bs@yahoo.fr
© 2020 The Author(s). Licensee IntechOpen. Distributed under the terms of the Creative
Commons Attribution - NonCommercial 4.0 License (https://creativecommons.org/
licenses/by-nc/4.0/), which permits use, distribution and reproduction for
non-commercial purposes, provided the original is properly cited. NC
13
Stator Winding Fault Diagnosis of Permanent Magnet Synchronous Motor-Based DTC-SVM
DOI: http://dx.doi.org/10.5772/intechopen.88784
References
[1] Erdogan S, Miller-Hooks E. A green
vehicle routing problem. Transportation
Research Part E: Logistics and
Transportation Review. 2012;48(1):
100-114
[2] Serra JVF. Electric Vehicles
Technology, Policy and Commercial
Development. London, UK: Earthscan;
2013
[3] Felipe A, Ortuno MT, Righini G,
Tirado G. A heuristic approach for the
green vehicle routing problem with
multiple technologies and partial
recharges. Transportation Research Part
E: Logistics and Transportation Review.
2014;71(1):111-128
[4] Longo M, Foiadelli F, Yaïci W. Electric
vehicles integrated with renewable energy
sources for sustainable mobility. In: New
Trends in Electrical Vehicle Powertrains.
IntechOpen; 2018
[5] Habetler TG, Profumo F,
Pastorelli M, Tolbert LM. Direct torque
control of induction machines using
space vector modulation. IEEE
Transactions on Industry Applications.
1996;28(5):1045-1053
[6] Bounadja M, Belarbi A, Belmadani B.
A high performance space vector
modulationDirect torque controlled
induction machine drive based on stator
flux orientation technique. Advances in
Electrical and Computer Engineering.
2009;9(2):28-33
[7] Joseline Metilda A, Arunadevi R,
Ramesh N, Sharmeela C. Analysis of
direct torque control using space vector
modulation for three phase induction
motor. Recent Research in Science and
Technology. 2011;3(7):37-40
[8] Nasri A, Gasbaoui B. A novel electric
vehicle drive studies based on space
vector modulation technique and direct
torque. Journal of Asian Electric
Vehicles. 2011;9(2):1529-1535
[9] Rashag HF, Koh SP, Chong KH,
Tiong SK, Tan NML, Abdalla AN. High
performance of space vector modulation
direct torque control SVM-DTC based
on amplitude voltage and stator flux
angle. Research Journal of Applied
Sciences, Engineering and Technology.
2013;5(15):3934-3940
[10] Ahammad N, Khan SA, Reddy RK.
Novel DTC-SVM for an adjustable speed
Sensorless induction motor drive.
International Journal of Science
Engineering and Advance Technology
(IJSEAT). 2014;2(1):31-36
[11] Rashag HF, Tan NML, Koh SP,
Abdalla AN, Chong KH, Tiong SK. DTC-
SVM based on PI torque and PI flux
controllers to achieve high performance
of induction motor. Research Journal of
Applied Sciences, Engineering and
Technology. 2014;7(4):875-891
[12] Ben Salem F, Derbel N. Second-
order sliding-mode control approaches
to improve low-speed operation of
induction machine under direct torque
control. International Journal of Electric
Power Components and Systems. 2016;
44(17):1969-1980
[13] Ben Salem F, Yangui A,
Masmoudi A. On the reduction of
the commutation frequency in dtc:
A comparative study. European
Transactions on Electrical Power
Engineering. 2005;15(6):571-584
[14] Chlebis P, Brandstetter P, Palacky P.
Direct torque control of induction motor
with direct calculation of voltage vector.
Advances in Electrical and Computer
Engineering. 2010;4:17-22
[15] Chaikhy H, Khafallah M, Saad A.
Evaluation of two control strategies for
induction machine. International
Journal of Computers and Applications.
2011;35(5):571-584
14
Autonomous Vehicle and Smart Traffic
[16] Allirani S, Jagannathan V. Direct
torque control technique in induction
motor drivesA review. Journal of
Theoretical and Applied Information
Technology. 2014;60(3):454-475
[17] Qi H, Li J, Chen Y. Control of
electric vehicle. In: Soylu S, editor.
Urban Transport and Hybrid Vehicles.
IntechOpen; 2010. p. 192. ISBN 978-953-
307-100-8
[18] Gieras JF, Wing M. Permanent
Magnet Motor Technology, Design and
Application. Electrical and Computer
Engineering. 3rd edition. Boca Raton,
London, New York: CRC Press Taylor
and Francis Group; 26 August 2009
[19] Singh R, Sengar KP, Mishra A,
Thakur C. A direct torque control of
interior permanent magnet synchronous
motor for an electric vehicle-design
analysis total harmonic distortion of
stator current. International Journal of
Engineering Research & Technology
(IJERT). 2016;5(11):57-65
[20] Hadef M, Mekideche MR, Djerdir A.
Vector controlled permanent magnet
synchronous motor (PMSM) drive with
stator turn fault. In: Proceeding of XIX
International Conference; Rome: ICEM;
6-8 September 2010
[21] Jeong Y, Sul S, Schulz SE, Patel NR.
Fault detection and fault-tolerant
control of interior permanent-magnet
motor drive system for electric vehicle.
IEEE Transactions on Industry
Applications. 2005;41(1):46-51
[22] Ebrahimi B-M, Faiz J. Feature
extraction for short-circuit fault
detection in permanent-magnet
synchronous motors using stator-
current monitoring. IEEE Transactions
on Power Electronics. 2010;25(10):
2673-2682
[23] Hadef M, Djerdir A, Mekideche MR,
NDiaye AO. Diagnosis of stator
winding short circuit faults in a direct
torque controlled interior permanent
magnet synchronous motor. In: IEEE
Vehicle Power and Propulsion
Conference; Chicago, IL, USA; 6-9
September 2011; DOI: 10.1109/
VPPC.2011.6043166
[24] Haddad RZ, Strangas EG. Fault
detection and classification in
permanent magnet synchronous
machines using fast Fourier transform
and linear discriminant analysis. 2013.
pp. 99-104
[25] Kamdi PD, Vaidya UB, Kamdi SY,
Asutakar P. Diagnosis of stator winding
short circuit fault in permanent magnet
synchronous motor. International
Research Journal of Engineering and
Technology (IRJET). 2015;2(4):325-328
[26] Rohan A, Rabah M, Kim SH. An
integrated fault detection and
identification system for permanent
magnet synchronous motor in electric
vehicles. International Journal of Fuzzy
Logic and Intelligent Systems. 2018;
18(1):20-28. DOI: 10.5391/
IJFIS.2018.18.1.20
[27] Liang H, Chen Y, Liang S, Wang C.
Fault detection of stator inter-turn
short-circuit in PMSM on stator current
and vibration signal. Applied Sciences.
2018;8:1677. DOI: 10.3390/app8091677
[28] Larminie J, Lowry J. Electric Vehicle
Technology Explained. 2nd ed. Vol. 23.
Chichester, England: John Wiley &
Sons, Ltd; 2012
[29] Wang Z, Qu C, Zhang L, Zhang J,
Yu W. Integrated sizing and energy
management for four-
wheel-independently-actuated electric
vehicles considering realistic
constructed driving cycles. Energies.
2018;11:1768. DOI: 10.3390/en11071768.
[30] Ben Salem F, Derbel N. VSC-based
DTC-SVM with adaptive parameter
estimation. In: IEEE 11th Int. Conf. on
Systems, Signals and Devices (SSD14),
15
Stator Winding Fault Diagnosis of Permanent Magnet Synchronous Motor-Based DTC-SVM
DOI: http://dx.doi.org/10.5772/intechopen.88784
Castelldefels-Barcelona; 11-14 February
2014; Spain
[31] Ben Salem F, Derbel N. Direct
torque control of induction motors
based on discrete space vector
modulation using adaptive sliding mode
control. International Journal of Electric
Power Components and Systems. 2014;
42(14):1598-1610
16
Autonomous Vehicle and Smart Traffic
... The equivalent circuit of PEMFC (proton exchange membrane fuel cell) considering an electrochemical model is shown in Figure 9 as: The output voltage Vf c of the PEMFC stack is expressed as follows [23], [30]: (13) where: ...
... The following equations specify the references of voltage vectors v * αs and v * βs in the (α, β) frame (30) With iαs and iβs are the stator current vectors in the (α, β) frame. ...
Article
Full-text available
Maximum Power Point Tracking (MPPT) is a tool to optimize the use of the produced power in renewable energy systems, particularly in photovoltaic (PV) systems. Many algorithms have been developed and reviewed in the literature. In this work, a Sliding Mode-Artificial Neural Network-based MPPT (SM-ANN-MPPT) technique is proved. A comparison is made with one of the simplest MPPTs in the literature Perturb and Observe (P&O). It has been found that the minimization of the PV power ripples and the dissipated energies have been gained using the SM-ANN-MPPT controller. The proposed controller is applied for a standalone PV system with a Proton Exchange Membrane Fuel Cell (PVPEMFC) considering variable irradiation and temperature. The application of the system is dedicated to a solar pumping system using a three-phase Permanent Magnet Synchronous Motor (PMSM). The control of the PMSM is ensured by a Direct Torque Control-Space Vector Modulation (DTC-SVM) approach. Two cases are tested: with imposed speed profile, and with a solar daily profile. Two cases of load profiles are also treated: constant and variable loads. The control behaviour is simulated and the results are revealed justifying the feats of the DTC-SVM PMSM drive. Results show improvement in robustness and stability under real daily climatic conditions.
Article
Full-text available
This paper proposes two intelligent torque distribution strategies based on particle swarm optimization (PSO) and fuzzy logic control (FLC) to provide convenient torque allocation that maximizes hybrid electric vehicle (HEV) propulsion power. PSO torque distribution strategy uses torque transfer ratio (TTR) as a fitness function to select the best torque candidates and differential arrangements that maximize HEV propulsion torque. A proposed FLC controller with adequate membership functions is designed to ensure convenient torque vectoring across vehicle wheels. A new coordinated switching strategy is proposed in this paper to address the undesired transient ripples occurring during drivetrain commutations and power source switchings. The proposed coordinated switching strategy controls the switching period duration through transition functions fitting the transient dynamics of power sources. In non-uniform surfaces, intelligent torque allocation strategies converted $84\sim 86$ % of the generated torque into propulsion torque whereas the equal torque distribution strategy yielded a torque transfer ratio of 50%. Thanks to the proposed coordinated switching strategy, DC bus voltage ripples were reduced to a narrow band of $\pm ~5\text{V}$ , transient power ripples were limited to a narrow band of 600 W and torque jerks were almost suppressed. Real-time simulation using the RT LAB platform confirms that the proposed coordinated switching strategy has reduced transient torque overshoot from 69% to almost zero and this is expected to improve HEV driving comfort.
Chapter
Full-text available
Across the globe, governments have been tackling the concerning problem of air-polluting emissions by committing significant resources to improving air quality. Achieving the goal of air purification will require that both the private and public sectors invest in clean energy technology. It will also need a transition from conventional houses to smart houses and from conventional vehicles to electric vehicles (EVs). It will be necessary to integrate renewable energy sources (RESs) such as solar photovoltaics, wind energy systems and diverse varieties of bioenergies. In addition, there are opportunities for decarbonisation within the transportation sector itself. Paradoxically, it appears that the same transportation sector might also present an opportunity for a speedy decarbonisation. Statistics indicate that transportation is responsible for 14% of global greenhouse gas (GHG) emissions. However, there are numerous options for viable clean technology, including the plug-in electric vehicles (PEVs). There are indeed many technologies and strategies, which reduce transportation emissions such as public transportation, vehicle light weighing, start-stop trains, improved engine technology, fuel substitution and production improvement , hydrogen, power-togas , and natural gas heavy fleets. This work concentrates on EV adoption integrated with RES. Specifically, this chapter examines the feasibility of significantly reducing GHG emissions by integrating EVs with RESs for sustainable mobility.
Article
Full-text available
The stator inter-turn short circuit fault is one of the most common and key faults in permanent magnet synchronous motor (PMSM). This paper introduces a time–frequency method for inter-turn fault detection in stator winding of PMSM using improved wavelet packet transform. Both stator current signal and vibration signal are used for the detection of short circuit faults. Two different experimental data from a three-phase PMSM were processed and analyzed by this time–frequency method in LabVIEW. The feasibility of this approach is shown by the experimental test.
Article
Full-text available
This paper presents an integrated optimization framework of sizing and energy management for four-wheel-independently-actuated electric vehicles. The optimization framework consists of an inner and an outer layer that are responsible for energy management, i.e., torque allocation, and powertrain parameter optimizations. The optimal torque allocation in the inner layer is achieved via the dynamic programming (DP) method while the desirable powertrain parameters in the outer layer are pursued based on the exhaustive method. In order to verify the proposed optimization framework, two driving cycles are constructed to represent the comprehensive and realistic driving conditions. One cycle is built by combining six typical driving cycles, which cover urban, high-way and rural driving styles to enhance representativeness. The other one is synthesized using the Markov chain method based on a vast quantity of real-time operating data of electric vehicles in Beijing. Simulation results demonstrate that the proposed strategy decreases the power consumption by 15.1% and 13.3%, respectively, in the two driving cycles, compared to the non-optimal, even-torque-allocation strategy.
Article
Full-text available
This paper proposes an efficient and integrated fault detection and identification system for power converters and permanent magnet synchronous motor in electric vehicles. Switching faults of power converters (single, double and triple switching faults), electrical and mechanical faults of the permanent magnet synchronous motor (bearing fault, stator electrical faults) are considered. Fault detection is done using Clarke transformed (α-β) three-phase current analysis. Features are extracted from the current signals and artificial neural network (ANN) is used for the fault identification. Using motor current signature analysis and by selecting simple and suitable features, the system can detect and distinguish between overall faults of power converters and permanent magnet synchronous motor in an electric vehicle; it requires no complex calculations. The proposed system is designed in MATLAB/Simulink. The system is tested under different fault scenarios and performance is evaluated. The simulation results have proved that the proposed system can detect and identify overall faults of power converters and permanent magnet synchronous motor easily and effectively with no need for complex calculations and techniques.
Article
Full-text available
Direct torque control is a powerful approach widely used for the control of electrical machines. However, this approach is sensitive to several problems, such as the stator flux demagnetization phenomenon and the appearance of undesirable torque ripples, especially at low-speed operation. To overcome these problems, a variable structure control approach for a direct torque control–space vector modulation system, with a constant switching frequency, is proposed in this article. The proposed sliding-mode direct torque control–space vector modulation method can (i) enhance the system robustness, (ii) reduce the torque ripples, and (iii) prevent the demagnetization problem, which penalizes the stator flux regulation. To reduce the chattering phenomenon that appeared on the control law, a second-order sliding-mode approach, which is a new second-order procedure, has been proposed. Simulation results dealing with low-speed operation of the induction motor under variable structure control direct torque control–space vector modulation strategy is presented and compared to those yielded by the direct torque control–space vector modulation strategy using hysteresis controllers.
Article
Full-text available
Various aspects related to controlling induction motor are investigated. Direct torque control is an original high performance control strategy in the field of AC drive. In this proposed method, the control system is based on Space Vector Modulation (SVM), amplitude of voltage in direct- quadrature reference frame (d-q reference) and angle of stator flux. Amplitude of stator voltage is controlled by PI torque and PI flux controller. The stator flux angle is adjusted by rotor angular frequency and slip angular frequency. Then, the reference torque and the estimated torque is applied to the input of PI torque controller and the control quadrature axis voltage is determined. The control d-axis voltage is determined from the flux calculator. These q and d axis voltage are converted into amplitude voltage. By applying polar to Cartesian on amplitude voltage and stator flux angle, direct voltage and quadratures voltage are generated. The reference stator voltages in d-q are calculated based on forcing the stator voltage error to zero at next sampling period. By applying inverse park transformation on d-q voltages, the stator voltages in α and β frame are generated and apply to SVM. From the output of SVM, the motor control signal is generated and the speed of the induction motor regulated toward the rated speed. The simulation Results have demonstrated exceptional performance in steadand transient states and shows that decrease of torque and flux ripples is achieved in a complete speed range.
Article
Full-text available
The fundamental idea of direct torque control of induction machines is investigated in order to emphasize the property produced by a given voltage vector on stator flux and torque variations. The proposed control system is based on Space Vector Modulation (SVM) of electrical machines, Improvement model reference adaptive system, real time of stator resistance and estimation of stator flux. The purpose of this control is to minimize electromagnetic torque and flux ripple and minimizing distortion of stator current. In this proposed method, PI torque and PI flux controller are designed to achieve estimated torque and flux with good tracking and fast response with reference torque and there is no steady state error. In addition, design of PI torque and PI flux controller are used to optimize voltages in d-q reference frame that applied to SVM. The simulation Results of proposed DTC-SVM have complete excellent performance in steady and transient states as compared with classical DTC-SVM.
Article
Comparative studies on several direct torque control (DTC) strategies of interior permanent magnet synchronous motor (IPMSM) for electric vehicles (EVs) are discussed in details, namely basic DTC, DTC combined with space vector modulation (DTC-SVM), and dead beat DTC (DB-DTC). These DTC strategies are reviewed; meanwhile dynamics and steady- state performance are analyzed and compared. Simulations of a 20kW IPMSM for EVs are carried out for comparison studies including: ripple of torque and stator flux, sensitivity to machine's parameter, computational complexity, and total harmonic distortion