Robot and Servo Drive Lab.
Online fault-detecting scheme of an inverterfed permanent magnet synchronous motor
under stator winding shorted turn and
inverter switch open
K. - H. Kim1 , B. - G. Gu 2 , I. - S. Jung 2 ,
Published in IET Electric Power Applications, Received on 29th November 2010, Revised on 1st March 2011,
doi: 10.1049/iet-epa.2010.0272, IET Electr. Power Appl., 2011, Vol. 5, Iss. 6, pp. 529–539.
Student: Pei-Lin, Tsai
Adviser: Ming-Shyan, Wang
Department of Electrical Engineering
Southern Taiwan University
2016/7/16
PPT製作率：100%
Outline









Abstract
Introduction
Fault characteristics due to the stator winding shorted turn
Fault characteristics due to the inverter switch open
Fault detecting algorithm
Configuration of the system
Simulation and experimental results
Conclusions
References
2016/7/16
2
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
Abstract

To detect faults in an inverter-fed permanent magnet
synchronous motor drive under the circumstance having faults
in a stator winding and inverter switch, an online basis faultdetecting scheme during motor operation is presented.

To verify the effectiveness of the proposed fault detecting
scheme, a test motor to allow inter-turn short in the stator
winding has been built.

The entire control system including harmonic analysis and
fault detecting algorithm is implemented using digital signal
processor TMS320F28335.
2016/7/16
3
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
Introduction

Under the fault due to winding shorted turn, a large circulating
shorted current flows in the faulty winding, and three-phase
currents cannot be maintained to balanced condition.

Even under the open fault in any of the inverter switch, threephase balanced condition does not hold and the third-order
harmonic component is observed in phase currents.

This is multiplied by the fundamental component in the
transformation into the synchronous reference frame, which
produces the second-order harmonic components in the q-axis
and d-axis currents.
2016/7/16
4
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
Introduction

The fault is detected by continuously comparing these
components with those in normal non-fault conditions during
operation.

The non-fault harmonic data in arbitrary operating conditions
are determined using the linear interpolation method with
several sample harmonic data pre-measured in the stage of the
initial drive setup.

Once the fault is detected, the operating mode is changed to
identify a fault type through the analysis using phase current
waveform.
2016/7/16
5
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
Fault characteristics due to the stator winding
shorted turn

To denote the degree of shorted turn in the stator winding, the
faulty turn ratio (FTR) and healthy turn ratio (HTR) are
defined as follows
σ denotes FTR, a denotes HTR, NT is the total stator winding and
Nf is the total stator winding in fault.
2016/7/16
6
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
Fault characteristics due to the stator winding
shorted turn
Fig. 1 Experimental results at no load 1000 rpm without fault
a Phase current responses
b q-axis current and harmonic characteristics
2016/7/16
7
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
Fault characteristics due to the stator winding
shorted turn
Fig. 2 Experimental results at no load 1000 rpm under the stator shorted turn of 1/24
a Phase current responses
b q-axis current and harmonic characteristics
2016/7/16
8
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
Fault characteristics due to the inverter switch
open
Fig. 3 PWM inverter
2016/7/16
9
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
Fault characteristics due to the inverter switch
open

For the voltage vectors V2 , V3 , V6 and V7 , b-phase pole voltage vbo is
determined according to the conduction state of the free-wheeling diode
Db and Db as follows
2016/7/16
10
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
Fault characteristics due to the inverter switch
open
Fig. 4 Experimental results at no load 1000 rpm under Tb  inverter switch open fault
a Phase current responses
b q-axis current and harmonic characteristics
2016/7/16
11
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
Fault characteristics due to the inverter switch
open
Fig. 5 Experimental results at no load 1000 rpm under the entire switch open fault in b-phase leg
a Phase current responses
b q-axis current and harmonic characteristics
2016/7/16
12
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
Fault detecting algorithm
2016/7/16
Fig. 6 Operating mode for the proposed fault detecting scheme
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
13
Fault detecting algorithm
2016/7/16
Fig. 7 Detection of steady-state condition
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
14
Fault detecting algorithm

Steady-state flag represents the number that (4) is satisfied.
2016/7/16
15
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
Fault detecting algorithm

To compute these data, the Fourier series for a periodic
waveform is used as follows
Where av is the average value, ωo is the fundamental frequency, and An and θn
represent the magnitude and phase of harmonics, respectively.
2016/7/16
16
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
Fault detecting algorithm

A fault index is defined as follows
where hq2 represents the magnitude of the second-order harmonic in the q-axis current
obtained from Mode 1, and h2n represents that at non-fault condition with the same
speed and current.
2016/7/16
17
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
Fault detecting algorithm
2016/7/16
Fig. 8 Determination of second-order harmonic at arbitrary normal
operating condition using linear interpolation
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
18
Fault detecting algorithm

Using the data at points A and B, the second-order harmonic
value is modified again according to the average q-axis current
using the linear interpolation as follows
2016/7/16
19
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
Fault detecting algorithm
2016/7/16
Fig. 9 Operating mode transition under the shorted turn of 1/12 at 2000 rpm
20
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
Fault detecting algorithm
2016/7/16
21
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
Configuration of the system
2016/7/16
Fig. 10 Configuration of the test motor
a Winding configuration
b Photograph of the test motor
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
22
Configuration of the system
Fig. 11 Configuration of the experimental system
a Configuration
b Experimental test setup
2016/7/16
23
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
Configuration of the system
2016/7/16
24
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
Simulation and experimental results
2016/7/16
Fig. 12 Comparative simulation results at no load 1000 rpm
a Under the normal condition without fault
b Under the stator shorted turn of 1/24
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
25
Simulation and experimental results
Fig. 13 Experimental results for fault detection under the stator
shorted turn of 1/24 at no load 1000 rpm
a Fault detection mode
b Fault type identification mode
2016/7/16
26
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
Simulation and experimental results
Fig. 14 Experimental results for fault detection under the stator
shorted turn of 1/24 at 1000 rpm with load
a Fault detection mode
b Fault type identification mode
2016/7/16
27
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
Simulation and experimental results
Fig. 15 Experimental results at no load 1000 rpm under Tb  inverter switch open fault
a Fault detection mode
b Fault type identification mode
2016/7/16
28
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
Simulation and experimental results
Fig. 16 Experimental results at no load 1000 rpm under the entire
switch open fault in b-phase leg
a Fault detection mode
b Fault type identification mode
2016/7/16
29
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
Simulation and experimental results
Fig. 17 Experimental results for fault detection and fault type identification at various
operating conditions under the stator turn fault
a Under the shorted turn of 1/24 at no load
b Under the shorted turn of 1/12 at no load
c Under the shorted turn of 1/24 at load
d Under the shorted turn of 1/12 at load
2016/7/16
30
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
Simulation and experimental results
2016/7/16
31
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
Simulation and experimental results
Fig. 18 Experimental results for fault detection and fault type
identification at various operating conditions under the switch fault
a Under Tb+ inverter switch open fault
b Under the entire switch open fault in b-phase leg
2016/7/16
32
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
Conclusions

An online basis fault detecting scheme of an inverter-fed PM
synchronous motor drive system has been presented to detect
faults during motor operation under the circumstance having
several faults. The proposed scheme can detect the fault as
well as identify its source.

The proposed fault detection and fault type identification
algorithms are valid under various fault conditions. In addition,
without requiring any additional hardware or measuring
apparatus, the proposed diagnostic algorithm can effectively
detect a fault by online basis during operation so long as the
steady-state condition is satisfied.
2016/7/16
33
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
References












1. Bellini, A., Filippetti, F., Tassoni, C., Capolino, G.A.: ‘Advances in diagnostic techniques for induction machines’,
IEEE Trans., 2008, IE-55, (12), pp. 4109–4126
2. Grubic, S., Aller, J.M., Bin, L., Habetler, T.G.: ‘A survey on testing and monitoring methods for stator insulation systems
of low-voltage induction machines focusing on turn insulation problems’, IEEE Trans., 2008, IE-55, (12), pp. 4127–4136
3. Campos-Delgado, D.U., Espinoza-Trejo, D.R., Palacios, E.: ‘Faulttolerant control in variable speed drives: a survey’,
IET, 2008, EPA-2, (2), pp. 121–134
4. Nandi, S., Toliyat, H.A.: ‘Novel frequency-domain-based technique to detect stator inter turn faults in induction
machines using stator induced voltages after switch-off’, IEEE Trans., 2002, IA-38, (1), pp. 101–109
5. Awadallah, M.A., Morcos, M.M., Gopalakrishnan, S., Nehl, T.W.: ‘Detection of stator short circuits in VSI-fed brushless
DC motors using wavelet transform’, IEEE Trans., 2006, EC-21, (1), pp. 1–8
6. Arkan, M., Perovic, D.K., Unsworth, P.: ‘Online stator fault diagnosis in induction motors’, IEE, 2001, EPA-148, (6), pp.
537–547
7. Wu, Q., Nandi, S.: ‘Fast single-turn sensitive stator interturn fault detection of induction machines based on positive- and
negativesequence third harmonic components of line currents’, IEEE Trans., 2010, IA-46, (3), pp. 974–983
8. Ebrahimi, B.M., Faiz, J.: ‘Feature extraction for short-circuit fault detection in permanent-magnet synchronous motors
using stator-current monitoring’, IEEE Trans., 2010, PE-25, (10), pp. 2673–2682
9. Stavrou, A., Sedding, H.G., Penman, J.: ‘Current monitoring for detecting inter-turn short circuits in induction motors’,
IEEE Trans., 2001, EC-16, (1), pp. 32–37
10. Li, L., David, A., Wenxin, L.: ‘Application of particle swarm optimization to PMSM stator fault diagnosis’. Proc. Int.
Conf. Neural Networks, Vancouver, 2006, pp. 1969–1974
11. Chetwani, S.H., Shah, M.K., Ramamoorty, M.: ‘Online condition monitoring of induction motors through signal
processing’. Proc. 8th ICEMS, 2005, (3), pp. 2175–2179
12. Filippetti, F., Franceschini, G., Tassoni, C., Vas, P.: ‘Recent developments of induction motor drives fault diagnosis
using AI techniques’, IEEE Trans., 2000, IE-47, (5), pp. 994–1004
2016/7/16
34
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
References












13. Tallam, R.M., Habetler, T.G., Harley, R.G.: ‘Stator winding turn-fault detection for closed-loop induction motor
drives’, IEEE Trans., 2003, IA-39, (3), pp. 720–724
14. Awadallah, M.A., Morcos, M.M., Gopalakrishnan, S., Nehl, T.W.: ‘A neuro-fuzzy approach to automatic diagnosis and
location of stator inter-turn faults in CSI-fed PM brushless DC motors’, IEEE Trans., 2005, EC-20, (2), pp. 253–259
15. Martins, J.F., Pires, V.F., Pires, A.J.: ‘Unsupervised neural-networkbased algorithm for an on-line diagnosis of threephase induction motor stator fault’, IEEE Trans., 2007, IE-54, (1), pp. 259–264
16. Kattha, D., Bose, B.K.: ‘Investigation of fault modes of voltage-fed inverter system for induction motor drive’, IEEE
Trans., 1994, IE-30, (4), pp. 1028–1038
17. Bolognani, S., Zordan, M., Zigliotto, M.: ‘Experimental fault-tolerant control of a PMSM drive’, IEEE Trans., 2000,
IE-47, (5), pp. 1134–1141
18. Welchko, B.A., Lipo, T.A., Jahns, T.M., Schulz, S.E.: ‘Fault tolerant three-phase AC motor drive topologies: a
comparison of features, cost, and limitations’, IEEE Trans., 2004, PE-19, (4), pp. 1108–1116
19. de Araujo Ribeiro, R.L., Jacobina, C.B., da Silva, E.R.C., Lima, A.M.N.: ‘Fault-tolerant voltage-fed PWM inverter AC
motor drive systems’, IEEE Trans., 2004, IE-51, (2), pp. 439–446
20. Rodriguez, J., Hammond, P.W., Pontt, J., Musalem, R., Lezana, P., Escobar, M.J.: ‘Operation of a medium-voltage drive
under faulty conditions’, IEEE Trans., 2005, IE-52, (4), pp. 1080–1085
21. Karimi, S., Poure, P., Saadate, S.: ‘Fast power switch failure detection for fault tolerant voltage source inverters using
FPGA’, IET, 2009, PEA-2, (4), pp. 346–354
22. TMS320F28335: ‘Digital Signal Controller (DSC) – Data Manual’. Texas Instrument, 2008
23. Krause, P.C.: ‘Analysis of electric machinery’ (McGraw-Hill, New York, 1986)
24. Kim, K.H., Choi, D.U., Gu, B.G., Jung, I.S.: ‘Fault model and performance evaluation of an inverter-fed permanent
magnet synchronous motor under winding shorted turn and inverter switch open’, IET, 2010, EPA-4, (4), pp. 214–225
2016/7/16
35
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.
Thanks for your listening！
2016/7/16
36
Department of Electrical Engineering
Southern Taiwan University
Robot and Servo Drive Lab.