An enhanced fault tolerant control against current sensor failures in induction motor drive by applying space vector

f
measured and the estimated current space vec-
tor, as follows:
IF :
∣∣∣∣∣∣iSS_m∣∣∣− ∣∣∣ˆiSS∣∣∣∣∣∣ > Tthreshold
THEN : Fcurrent = 1;ELSE : Fcurrent = 0.
(24)
T
threshold
: is a maximum difference between the
amplitude of measured current space vector and
magnitude of the estimated current space vector.
In this paper, T
threshold
is proposed as a 10(%)
value of rated current.
Reconfiguration step: after the total-current-
failure is detected, the FTC controller isolates
the wrong feedback signals of current sensors,
and replace them by the estimated current signs.
As a result, the IMD operates stably with the
estimated current signals, which are corrected
by the remain healthy current sensor.
Finally, the FTC unit determines the fault
state and provides the feedback current to the
FOC controller as the flowchart in Fig. 5:
Fig. 5: Flowchart of FTC unit's function.
3. Simulation results
In this part, simulations in Matlab/Simulink
have been implemented and compared between
the axes transformation method and space vec-
tor method. Here, the model induction motor
with machine parameters listed in Tab. 1.
The simulations have been implemented at
the normal reference speed zone. The IMD is
controlled by the FOC technique in both healthy
sensor conditions and false conditions. The con-
c© 2020 Journal of Advanced Engineering and Computation (JAEC) 57
VOLUME: 4 | ISSUE: 1 | 2020 | March
Tab. 1: The main parameters of IM
Description:
Symbol
Value Unit
Rated Power: Pn 2.2 kW
Rated Voltage: Un 400 V
Rated Torque: Tn 14.8 N.m
Rated speed: nn 1420 rpm
Rated stator current: In 4.85 A
Stator resistance: RS 3.179 Ω
Rotor resistance: RR 2.118 Ω
Mutual inductance: Lm 0.192 H
Pole pair number: p 2 -
Stator inductance: LS 0.209 H
Rotor inductance: LR 0.209 H
Rated Rotor flux: Ψ
Sn
0.757 Wb
(a)
(b)
Fig. 6: Nominal speed range: (a) reference speed and
measured speed, (b) two-phase measured cur-
rent signals in healthy current sensors condition.
trol structure of the IMD is the same in both
two FTC methods.
The IMD has been operated according to the
reference rotor speed in two ranges: the nomi-
nal speed and low-speed, as shown in Fig. 6(a)
and Fig. 7(a). A constant load torque equal to
5N.m was applied at 0.5s time during the oper-
ation process. Reference speed set up to a value
70(%) of rated speed t = 0.5 sec, and then, it
(a)
(b)
Fig. 7: Low-speed range: (a) reference speed and mea-
sured speed, (b) two-phase measured current
signals in healthy current sensors condition.
(a)
(b)
Fig. 8: Nominal speed range: (a) reference speed and
measured speed, (b) two-phase measured cur-
rent signals with A phase current sensor fault.
set down to 35% of the rated value following
the slant line (2.5 sec3.0 sec). The two mea-
sured stator currents in a healthy condition are
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c© 2020 Journal of Advanced Engineering and Computation (JAEC)
VOLUME: 4 | ISSUE: 1 | 2020 | March
(a)
(b)
Fig. 9: Low-speed range: (a) reference speed and mea-
sured speed, (b) two-phase measured current
signals with A phase current sensor fault.
also shown in Fig. 6(b). Similarly, the refer-
ence speed is set at 10% and 5% of the rated
speed in the low-speed area as Fig. 7(b). The
IMD applying the FOC controller operates sta-
bly during the steady-state and transient state
in both cases above.
When a total failure of the current sensor oc-
curs at t = 2.0 sec, the value of feedback A
phase current back to zero that leads the FOC
controller operates wrong. As a result, the IMD
becomes unstable and collapses, as shown in Fig.
8 and Fig. 9. Therefore, the purpose of FTC
methods is to maintain the stability of the drive
system, even under fault operating conditions.
In the first case, the FTC function of the axes
transformation method against a current sen-
sor fault is simulated by Matlab/Simulink. Fig.
10(a) and Fig. 11(b) depicted a failure of A-
phase current occurrence, and feedback current
signal equals to zero. The fault-location func-
tion set to a high level to detect the false as in
Fig. 10(b) and Fig. 11(b). The rotor speed
slightly fluctuates in a short time, and after the
incorrect current signals are replaced by the es-
(a)
(b)
(c)
Fig. 10: Nominal speed range: IMD applying axes
transformation-FTC method against a current
sensor fault: (a) two-phase measured current,
(b) fault indication function, (c) rotor speed.
timated value, the IMD works stably again, as
in Fig. 10(c) and Fig. 11(c).
Next step, the simulations have been imple-
mented to demonstrate the effectiveness of the
proposed FTC method. Similar to the above
case, Fig. 12(a) and Fig. 13(a) shows a cur-
rent sensor fault with A-phase. After the cur-
rent fault occurrence, the fault diagnosis of the
proposed method works immediately, as in Fig.
12(b) and Fig. 13(b). The wrong signals are
isolated and replaced by the estimated current,
which is corrected by the healthy current sen-
sor. Fig. 12(c) and Fig. 13(c) demonstrated
that the IMD still operates reliably, even under
the sensor fault condition.
By comparison between two methods, we can
recognize that the detection time of the proposed
c© 2020 Journal of Advanced Engineering and Computation (JAEC) 59
VOLUME: 4 | ISSUE: 1 | 2020 | March
(a)
(b)
(c)
Fig. 11: Low-speed range: IMD applying axes
transformation-FTC method against a current
sensor fault: (a) two-phase measured current,
(b) fault indication function, (c) rotor speed.
FTC space vector method is shorter than the
axes transformation method, as in Fig. 14. At
the time of the current fault, the estimated sta-
tor current is corrected by the healthy-current
sensor, so the estimated signals have high accu-
racy and small deviation as shown in Fig. 15.
The result as, the fluctuation of rotor speed in
the proposed method at fault occurrence time
is smaller than the axes transformation method,
and the IMD operates smoother in the transient
period.
(a)
(b)
(c)
Fig. 12: Nominal speed range: IMD applying space
vector-FTC method against a current sensor
fault: (a) two-phase measured current, (b)
fault indication function, (c) rotor speed.
4. Conclusions
This paper has proposed the upgrading of the
FTC method by applying space vectors. This
proposed FTC method only uses one observer
for fault sensor diagnosis and reconfiguration
function. Due to the diagnosis algorithm's
simplicity, so the fault detection time of the
proposed method is shorter than the axes-
transformation method, and the IMD system
stably operates again quickly. The estimated
current has a high precision due to the correction
with the signal of the healthy current sensor.
Therefore, the proposed method contributes ef-
fectively to the development of the FTC tech-
nique against the total failure of the current sen-
sor.
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c© 2020 Journal of Advanced Engineering and Computation (JAEC)
VOLUME: 4 | ISSUE: 1 | 2020 | March
(a)
(b)
(c)
Fig. 13: Low-speed range: IMD applying space vector-
FTC method against a current sensor fault: (a)
two-phase measured current, (b) fault indica-
tion function, (c) rotor speed.
Acknowledgement
This research is funded by Graduate Schol-
arship for Master and Doctoral Programs
of Ton Duc Thang University, website:
 This work was
supported by Project reg. no. SP2020/128 -
Research and development of sophisticated con-
trol methods for the area of electric controlled
drives of VSB-Technical University of Ostrava,
2020.
(a)
(b)
Fig. 14: Comparison of detection time between the
axes transformation-FTC and space vector-
FTC method in (a) nominal speed range and
(b) low-speed range.
(a)
(b)
Fig. 15: The deviation between the estimated current
and measured current of the healthy sensor
in (a) nominal speed range and (b) low-speed
range.
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About Authors
Cuong Dinh TRAN was born in Ho Chi
Minh, Vietnam. He graduated from the Viet-
nam National University - Ho Chi Minh City
- University of Technology. He received his
B.E. and M.E. degrees in electrical  electronics
power engineering in 2005 and 2008, respec-
tively. Now, he is teaching at the department of
electrical and electronics engineering, Ton Duc
Thang University, Ho Chi Minh city, Vietnam.
His research interests include modern control
methods of electrical drives, automatic control
system, intelligent control system, operation
and control power system.
62
c© 2020 Journal of Advanced Engineering and Computation (JAEC)
VOLUME: 4 | ISSUE: 1 | 2020 | March
Pavel BRANDSTETTER was born in
Ostrava, Czech Republic, 1955, 1 June. He
received the M.Sc. and Ph.D. degrees in
Electrical Engineering from Brno University of
Technology, Czech Republic, in 1979 and 1987,
respectively. He is currently full professor in
Electrical Machines, Apparatus and Drives and
dean of Faculty of Electrical Engineering and
Computer Science at VSBTechnical University
of Ostrava. His current research interests are
applied electronics and modern control methods
of electrical drives. Since 2000 he has performed
research in field of modern control methods
for AC motors, for example sensorless control,
applications of artificial intelligence in control
of AC drives.
Minh Chau Huu NGUYEN was born
in Binh Duong, Vietnam. He graduated from
the Military Technical Academy, Vietnam. He
received his M.E. degrees in Automation engi-
neering in 2012. Now, he is researching at the
Faculty of Electrical Engineering & Computer
Science, VSB  Technical University of Ostrava,
Czech Republic. His research interests include
an automatic control system, intelligent control
system and electrical machine, apparatus and
drives.
Sang Dang HO received his B.Eng.
and M.Eng degrees in Electrical Engineering
from Ho Chi Minh University of Technology,
Ho Chi Minh city, Vietnam in 2001 and 2008,
respectively. Now, he is teaching at department
of electrical and electronics engineering, Ton
Duc Thang university, Ho Chi Minh city, Viet-
nam. His research interests include optimization
of power system and electric machines control.
Phuong Nhat PHAM was born in Tien
Giang, Vietnam. He graduated from the
University of Technical Education Ho Chi Minh
City, Viet Nam. He received MSc. degrees
in Electrical Equipment, Network and Power
Station in 2006. Now, he is teaching at the
department of electrical and electronics engi-
neering, Ton Duc Thang University, Ho Chi
Minh City, Vietnam. His research interests
include Evolutionary algorithm, intelligent con-
trol system, operation and control power system.
Bach Hoang DINH received the Ph.D
degree in Electrical Engineering from Heriot-
Watt University, Edinburgh, United Kingdom
in 2009. He received the B.E. and the M.E.
degrees in Electrical Engineering from Vietnam
National University - Hochiminh City in 1995
and 1998, respectively. Bach Dinh is currently
the head of Electrical Engineering Department,
Faculty of Electrical-Electronic Engineering
at Ton Duc Thang University. His research
interests are intelligent and optimal control,
computer vision, robotics, power electronics,
SCADA and industrial communication net-
works. He is a member of the IEEE Industrial
Electronics Society.
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