real-time detection of interturn faults in pm drives using back-emf estimation and residual analysis

2402 IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 47, NO. 6, NOVEMBER/DECEMBER 2011

Real-Time Detection of Interturn Faults in PM DrivesUsing Back-EMF Estimation and Residual Analysis

Nicolas Leboeuf, Thierry Boileau, Babak Nahid-Mobarakeh, Member, IEEE,Guy Clerc, Senior Member, IEEE, and Farid Meibody-Tabar

Abstract—Interturn stator winding fault is one of the mostfrequent faults in permanent-magnet synchronous machines(PMSMs). In this paper, we present a new technique for on-line detection of this fault in PMSMs. It is based on a residualanalysis improved by taking into account back-electromotive forcewaveform estimation, inverter model, and unbalanced inductancematrix. Then, a current residual monitoring block permits todetect the fault and its severity. The simulation and experimentalresults validate the proposed method and its efficiency.

Index Terms—Health monitoring, interturn fault, permanent-magnet synchronous machine (PMSM), real-time fault detection.

NOMENCLATURE

i Current.v Voltage.e Back EMF.Ω Rotor angular speed.Γ Torque.Rs Stator resistance.Ls Stator inductance.M Mutual inductance.Ψf Magnet flux linkage through the stator windings.p Number of pole pairs.J Inertia coefficient.f Friction coefficient.Rf Faulty insulation resistance.if Fault current through Rf .k Ratio between faulty turns and healthy turns.F fault indicator.

Subscriptsd, q Synchronous frame components.

Manuscript received January 5, 2011; revised June 12, 2011; acceptedJune 16, 2011. Date of publication September 22, 2011; date of currentversion November 18, 2011. Paper 2010-IACC-486.R1, presented at the 2010Industry Applications Society Annual Meeting, Houston, TX, October 3–7,and approved for publication in the IEEE TRANSACTIONS ON INDUSTRY

APPLICATIONS by the Industrial Automation and Control Committee of theIEEE Industry Applications Society.

The authors are with the Groupe de Recherche en Electrotechnique etElectronique de Nancy (GREEN), Institut National Polytechnique de Lorraine(INPL), Nancy University, 54510 Nancy, France, and also with the AMPERE,Université Claude Bernard, 69622 Lyon, France (e-mail: [email protected]; [email protected]; [email protected]; [email protected]; [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TIA.2011.2168929

I. INTRODUCTION

P ERMANENT-MAGNET (PM) synchronous machines(PMSMs) are increasingly used in transport applications

such as aircrafts because of their good torque/mass ratio. Par-ticularly in aerospace industry, they are preferred to hydraulicactuators for overcoming maintenance problems related to theselatter. Within the framework of the “more electric aircraft,”PM drives are considered as a more reliable device, thanks toonline diagnostic and fault-tolerant control techniques. Indeed,for such an application, PM drives should involve fault-tolerantcontrol techniques [1], reconfigurable systems [2], and health-monitoring methods to avoid severe failures.

Several methods have been already developed for the faultdetection in induction motors [3], [4] and are often based oncurrent and voltage monitoring, spectral, or temporal analysis[5]–[10]. However, these approaches are not easily transposableto PM machines. There are also other methods which use fluxsensors around the machine [11], [12], parameter identificationmethods [13]–[16], artificial intelligence, and neural networks[17]–[20].

To classify faults in electrical machines, two main categoriescan be distinguished: mechanical and electrical faults. In thispaper, we focus on interturn fault in the stator winding of aPMSM. This type of fault is often the first symptom beforea more important fault such as interphase short-circuit fault,phase-ground short-circuit fault [6], [7] or a demagnetizationproblem [21] appears. Here, the objective is to detect thisfault in real time as soon as possible. The intended applicationis an electromechanical aileron actuator. This real-time faultdetection has to detect the fault before it damages definitelythe actuator. Also, no additional sensors are permitted due tocertification problems. The only admitted measurements arethose used for the control: stator current sensors, rotor positionmeasurement, and dc-link voltage sensor. It is also desirablethat the fault detection requires the modification of neither thecurrent references nor the control output voltages. In summary,we have the following requirements:

1) real-time fault detection;2) no additional sensor;3) no signal injection.

Moreover, we are looking for an indicator allowing determin-ing the fault severity. This information may be useful for defin-ing the corresponding degraded mode. It is of course desirableto have an indicator insensitive to the inverter irregularities andto the mechanical load variations.

0093-9994/$26.00 © 2011 IEEE

LEBOEUF et al.: REAL-TIME DETECTION OF INTERTURN FAULTS IN PM DRIVES 2403

The proposed method in this paper allows a real-time im-plementation, which can be integrated in the control systemof the PMSM and may be associated to a higher level onlinefault detection system. The PMSM considered in this study aresupplied by pulse-width modulation (PWM)-controlled voltagesource inverters (VSI).

To better understand the behavior of the drive under faultconditions, a model of a faulty PMSM is described brieflyin the next section [6]–[8]. This model takes into accountan interturn fault represented by a faulty insulation resistance(Rf ). There are many studies based on this type of model,which depend on various hypotheses on the machine designsuch as winding distribution or saliency [22], [23]. However, itallows us simulating the actuator under a stator fault, to predictthe performances of the whole drive regarding the fault and totest fault detection methods.

The method proposed in this paper consists in obtainingthe machine current with a healthy model and comparing itwith the real current of the machine. The model has to beaccurate because the difference between the two currents shouldonly reflect the presence of an interturn short-circuit fault infaulty cases. In fact, if the model does not take into accountuncertainties like real back EMF or losses in the inverter, theindicator level may exceed the alert threshold even for thehealthy machine (Rf � 100 kΩ). As the machine is currentcontrolled in our application, the method exploits the unbal-anced voltages and can detect a fault if the model is adequate.However, this method is not appropriate for detecting emergingfaults (Rf > 1 kΩ) [24], [25] because of the original unbalanceeffects of the healthy machines. In this case, the interturnfault does not significantly affect the control signals and themeasurements, and so, it cannot be clearly detected underthe above requirements. Actually, the fault has little impacton the behavior of the system in this case so that the alertthreshold is not exceeded. Whatever the fault nature is, a safeoperating requires switching to a proper degraded mode oncethe fault is detected [26]–[28]. This is not the subject of thispaper.

The paper is organized into five sections. The model of thePMSM is detailed in Section II. In Section III, the detectiontechnique is described. Simulation results and parametric stud-ies are also presented and discussed in Section IV. Experimen-tal tests and conclusions are given in Section V.

Fig. 1. Electrical model of a faulty machine.

II. PMSM MODEL UNDER FAULT CONDITIONS

After understanding the physical effects of an interturn short-circuit fault in a PMSM, we should develop an electrical modelof the faulty machine. This has already been validated usingfinite-element analysis and considering some hypothesis in[22]. We consider a one-slot per pole and per phase PMSM witha Y-connected stator. The saturation effects are neglected andthe rotor is considered as nonsalient. As it is shown in Fig. 1, anadditional resistance Rf is placed in phase a and divides it intotwo parts a1 (healthy part) and a2 (faulty part). Without loss ofthe generality, the number of pole pairs is fixed to 1 to use theone pole pair equivalent machine. The fault current through Rf

is called if .Consider the following vectors:⎧⎨⎩ iabcf = [ia ib ic if ]T

vabcf = [va vb vc vf ]T

eabcf = [ea eb ec ef ]T

where iabcf , vabcf , and eabcf are, respectively, the phase cur-rent, the phase voltage, and the back EMF in the three phasesand in the faulty part [8]. It is obvious that the faulty partsupplying voltage is zero (vf = 0).

Moreover, we have the resistance and inductance matri-ces shown in the equation at the bottom of page. Rj(j =a1, a2, b, c) is the resistance of the winding j, Lj(j =a1, a2, b, c) is the self-inductance of the winding j, Mjk (j and

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

[Rabcf ] =

⎡⎢⎢⎢⎢⎣Ra1 + Ra2 0 0 −Ra2

0 Rb 0 0

0 0 Rc 0

−Ra2 0 0 Ra2 + Rf

⎤⎥⎥⎥⎥⎦

[Labcf ] =

⎡⎢⎢⎢⎢⎣La1 + La2 + 2.Ma1a2 Ma1b + Ma2b Ma1c + Ma2c −(La2 + Ma1a2)

Ma1b + Ma2b Lb Mbc −Ma2b

Ma1c + Ma2c Mbc Lc −Ma2c

−(La2 + Ma1a2 −Ma2b −Ma2c La2

⎤⎥⎥⎥⎥⎦


k = a1, a2, b, c) is the mutual inductance between windings jand k. If the healthy machine is balanced, we have

Ra1 + Ra2 =Rb = Rc = Rs (1)

La1 + La2 + 2.Ma1a2 =Lb = Lc (2)

Ma1b + Ma2b =Ma1c + Ma2c = Mbc (3)

ea = ea1 + ea2 = eb = ec (4a)

ef = −ea2. (4b)

Knowing that the machine is star connected, the followingequation holds:

ia + ib + ic = 0. (5)

Then, taking into account (2) and (3), the cyclical inductance isgiven as follow:

Lcy =La1 + La2 + 2.Ma1a2 − (Ma1b + Ma2b)

=La − Mab = Lb − Mbc = Lc − Mac. (6)

Moreover, from (5), the inductance matrix can be simplified asshown in (7) at the bottom of the page, where M3×3 is a 3 ×3 matrix whose elements are all equal to Mbc. Now, we candeduce the faulty model

vabcf = [Rabcf ] · iabcf +[L′

abcf

]· diabcf

dt+ eabcf . (8)

This model can be rewritten as follows for separating itshealthy and faulty parts:⎡⎣ va

vb

vc

⎤⎦ = Rs ·

⎡⎣ iaibic

⎤⎦ + Lcy · d

dt

⎡⎣ iaibic

⎤⎦ +

⎡⎣ ea

eb

ec

⎤⎦︸︷︷︸

healthy components

+

⎡⎣αβγ

⎤⎦︸︷︷︸

faulty components

(9)

with ⎧⎪⎨⎪⎩α = −Ra2 · if − (La2 + Ma1a2) · dif

dt

β = −Ma2b · dif

dt

γ = −Ma2c · dif

dt .

(10)

Then, considering Ma2b ≈ Ma2c, it yields⎧⎨⎩ α = −Ra2 · if − (La2 + Ma1a2) · dif

dt

β = −Ma2b · dif

dtγ ≈ β.

(11)

TABLE IEXPERIMENTAL PARAMETERS

This shows that the fault creates residual voltages in the(abc) frame. Assuming that Ma2b ≈ Ma2c (saturation effectsare neglected, one slot per pole and per phase machine), theresidual voltage is quiet identical to the phases b and c whereasit is different on the faulty phase. This property can be exploitedfor locating the fault (phase a, b, or c).

To verify the evolution of the fault severity with respect tothe fault resistance, the fault current rms value is calculated asa function of Rf using the above model (parameters are givenin Table I). We define k as the ratio of the faulty part of thephase a fixed to 0.5 for this test (half of the winding of thephase a is faulty). Fig. 2 shows the result. It can be seen onthis figure that the fault current is negligible when Rf > 50 Ωand the fault severity grows up rapidly for Rf < 10 Ω. Similarresults are obtained for different values of k. Therefore, it isreally interesting to detect the fault as soon as possible when itis not yet dangerous.

Finally, the above model may be useful for studying thedynamical behavior of the PMSM under stator fault conditionsand for testing fault detection methods before experimental val-idation. The residual voltage vector [α β β]T can be consideredas a fault feature. In the next section, we will use this residualterm to create an indicator for detecting an interturn fault in thestator winding of a PMSM.

III. DETECTION OF PMSM INTERTURN SHORT CIRCUITS

A. Principle

The detection technique consists in using a healthy modelof a PMSM to estimate the phase current of the machine.Then, this estimation is compared to the measured current anda residual current term is obtained. This latter will be used tocreate our indicator. We prefer to develop a method based onthe current residual instead of the voltage residual for accuracyreasons. In fact, working with integrated variables (currents) isalways more stable and accurate than working with derivative

[L′

abcf

]= [Labcf ] −

[M3×3 0

0 0

]=

⎡⎢⎣Lcy 0 0 −(La2 + Ma1a2)0 Lcy 0 −Ma2b

0 0 Lcy −Ma2c

−(La2 + Ma1a2) −Ma2b −Ma2c La2

⎤⎥⎦ (7)


Fig. 2. Fault severity as a function of Rf .

ones (voltages). Moreover, for our application, it is required thatthe fault should be detected in real time without any additionalsensors (requirements 1 to 3 in Section I).

We use the following healthy model of the PMSM:

vabc = Rs · iabc + [Labc] ·diabc

dt+ eabc (12)

with ⎧⎨⎩ iabc = [ia ib ic]T

vabc = [va vb vc]T

eabc = [ea eb ec]T

and [Labc] = Lcy · I3 for a balanced inductance matrix (I3 asthe identity matrix).

It is obvious that this model is not complete. Actually,we have to define the back-EMF term eabc and to take intoaccount the inverter model and inductance unbalanced effects.This permits us to improve the healthy model by minimizingthe gap with the real healthy machine. It is described in thefollowing.

1) Back EMF: eabc = [ea eb ec]T is the real back-EMFvector of the healthy machine. As this vector is not measured,we have to estimate it. This estimation can be done in anyworking frame; we can do it in the synchronous (dq) frame asin [8] or during an offline test using the machine in generatormode. However, the contribution here is to taking into accountthe other back-EMF frequency components by estimating themin higher frequency synchronous frames. This permits us toget more precise back-EMF waveforms leading to decreasingthe current residual term in the healthy case. Fig. 3 shows theestimated back-EMF waveforms for our PMSM at 1000 r/min.

It should be noted that this estimation is only realized in thefirst operating cycles and the obtained back-EMF waveformsare progressively frozen. It is of course supposed that themachine and its supply are initially healthy. The freeze rate canbe adjusted. It is obvious that the estimated back-EMF vectorwill contain not only the real back-EMF components, but alsoother deviations from the ideal drive model (12).

Fig. 3. No-load back-EMF of the studied PMSM at 1000 r/min.

Note-It should be noted that the Park transformation that weuse for the control is the following:

P (θ) =[

cos(θ + δ) − sin(θ + δ)sin(θ + δ) cos(θ + δ)

](13)

where θ is the rotor position and δ is a proper correction to thisposition defined often by trial and error. Actually, it is shown in[13] that when the back-EMF waveforms are not sinusoidal, wecan use a modified Park transformation matrix for improvingthe control of the machine.

2) Inverter Model: If we use the control reference voltages,the resistance Rs is the equivalent resistance seen by the controlwhich is a nonlinear variable parameter because of the wholesystem losses. We have two choices: either closing the eyesto this phenomenon and putting it with the estimated backEMF, or taking it into account and compensating it. To do thelatter, an experimental law is required to describe the equivalentresistance evolution as a function of the system state variables.This can be easily done by performing offline or online testsfrom stand-still to Ωmax by sweeping the machine d-currentfrom zero to the rated value and evaluating the equivalent


Fig. 4. Model of inverter switches.

resistance from vd and id. Supposing that the IGBT devices andthe diodes have the same dynamical resistance Rd and the samevoltage drop Vd, we consider the following model for powerswitches.

Then, we have [29]

vabc = vabcref − Rd.iabc − Vd.sign(iabc) (14)

where

sign(iabc) =

⎛⎝ sign(ia)sign(ib)sign(ic)

⎞⎠ .

Fig. 4 shows the model used for the inverter switches.On the other hand, the dead time (Tm) has also an influence

on the accuracy of our results. Particularly at zero controlvoltage, its impact is more important than losses in the in-verter. To compensate it, we modify the control voltage asfollows [30]:

v∗abcref = vabcref + 2.

Tm

Tp.pm.sign(iabc) (15)

where pm and Tp are, respectively, the amplitude and the periodof the PWM carrier signal.

3) Unbalanced Inductances: Design or maintenance as-pects have a lot of consequences on the machine behavior,particularly on the stator windings. In some cases for specialmachines (it is the case of our machine), the inductance matrixis unbalanced

[Labc] =

⎡⎣ La Mab Mac

−Mab Lb Mbc

−Mac −Mbc Lc

⎤⎦ . (16)

Then, taking into account (5), and by using the cyclical induc-tance Lcy = La − Mab like as in (7), we obtain the followingmatrix:

[L′abc] =

⎡⎣ Lcy 0 ΔM2

0 Lcy + ΔL2 ΔM3

−ΔM2 −ΔM3 Lcy + ΔL3

⎤⎦ . (17)

The elements of [L′abc] are almost known and constant if the

machine is not saturated.By the way, we can also do the same operation on the

resistance matrix [Rabc]. However, considering the temperatureeffects, unbalance effects are negligible for resistances. Hence,we will take the same value (Rs) for all three phases.

4) Indicator Definition: Now, we put together (12), (14),(15) and (17) to obtain the estimated currents using the controlvoltages vabcref

d

dt

⎡⎣ι̂aι̂bι̂c

⎤⎦= [L′abc]

−1.

⎡⎣⎡⎣varef

vbref

vcref

⎤⎦ − Rd.

⎡⎣ι̂aι̂bι̂c

⎤⎦− Vd.sign(iabc) + 2.

Tm

Tp.pm.sign(iabc)

−Rs.

⎡⎣ ι̂aι̂bι̂c

⎤⎦ −

⎡⎣ ea

eb

ec

⎤⎦⎤⎦ (18)

with

ι̂abc = [ι̂a ι̂b ι̂c]T

as the estimated current vector. It should be noted that the sameestimation can be done in the stationary frame (αβ)

d

dt

[ι̂αι̂β

]=

[L′

αβ

]−1

.

[[vαref

vβref

]− Rd.

[ι̂αι̂β

]− Vd.T

T32.sign(iabc)

+ 2.Tm

Tp.pm.TT

32.sign(iabc) − Rs

.

[ι̂αι̂β

]−

[eα

eβ

]](19)

with [L′

αβ

]−1 =[TT

32.L′abc.T32

]−1; ι̂αβ = [ι̂α ι̂β ]T

T32 =

√23

⎡⎣ 1 0−12

√3

2−12

−√

32

⎤⎦ .

In both cases, the residual currents are defined by⎡⎣ ι̃aι̃bι̃c

⎤⎦ =

⎡⎣ iaibic

⎤⎦ −

⎡⎣ ι̂aι̂bι̂c

⎤⎦ . (20)

Under fault conditions, the control voltages are unbalancedfor regulating correctly the phase currents iabc. This voltagevector comes actually from the current controllers which aresupposed to be efficient. Hence, the estimated currents ı̂abc,obtained from these unbalanced voltages, contain informationon the fault. It can be deduced that the real currents are almostsinusoidal, but not ı̂abc because of the residual voltage vector[α β β]T [see (10)]. In this case, if the healthy model is“accurate” in terms of its back-EMF vector, Rs, ψf , [Labc],and the inverter model, a fault in the drive can be concluded.In addition, as if grows up rapidly when Rf decreases, theresidual voltage vector [α β β]T and so the residual currentterms depend on the fault severity. This will be verified bysimulation and then by experimentation.


Fig. 5. Block diagram of the fault indicator.

The block diagram of the proposed fault detection methodis given in Fig. 5. The phase currents are estimated using(18). Then, the residual current vector [ı̃a ı̃b ı̃c]T is calculatedaccording to (20). Finally, a simple signal processing is appliedto this residual current for determining the fault indicator. Itconsists in shifting the second and the third residual currents,respectively, by T/3 and 2T/3 (T is the electrical period)

F =∣∣∣ι̃a − [ι̃b]t−T/3

∣∣∣ +∣∣∣[ι̃b]t−T/3 − [ι̃c]t−2T/3

∣∣∣+

∣∣∣ι̃a − [ι̃c]t−2T/3

∣∣∣ (21)

T = 2π/p.Ω. (22)

Then, F is low-pass filtered. These shifts allow localizing theunbalanced phase.

B. Simulation Results

The simulations are performed with MATLAB/Simulink.First, we use the PMSM faulty model coupled with a VSImodel including losses and voltage drops. The DC sourceis represented by a perfect source followed by a LC filter.Switching and sampling frequencies are fixed to 10 kHz.

The drive works in a torque control scheme. Fig. 6 showsthe simulation results in the healthy case and under fault condi-tions. The fault signal (F ), shown at the bottom, is calculatedaccording to (21). The current references are{

idref = 0 Aiqref = 15 A.

Fig. 7 shows, respectively the control voltages, the faultcurrent, and the fault indicator for Ω = 1000 r/min. When Rf

is fixed to 50 Ω, the fault current amplitude is about 0.5 A, andno significant changes can be noticed in measured signals. Inthis case, the fault indicator level is low but different from zeroand the fault can be detected. For Rf = 20 Ω, we can make

Fig. 6. From top to bottom: machine currents and their estimation, residualcurrent terms, and synchronized residual current terms for the healthy machine.

a clear decision on the fault detection because the indicatorlevel increased to about 20 (almost 50 times more than thatfor the healthy case). For Rf = 10 Ω, the indicator is around40 and finally for the worst case studied here (Rf = 5 Ω),the fault current is about 5 A and the indicator is about 75(35 points more than the previous case). It can be noticedthat the indicator is close to 0.4 for the healthy machine; thesensitivity is conserved that is why we can detect a change evenfor a high value of Rf in simulations.

In the following, the robustness of the proposed method withrespect to the operating point, the parameter uncertainties, andtransient periods is evaluated by simulation.

1) Influence of the Operating Point: The objective here is toobserve the influence of the operating point (iq,Ω) on the faultindicator. We suppose that the evolution of the voltage drop inthe inverter is almost constant (see Fig. 4) and that the directcomponent of the current is fixed to zero (id ∼= idref = 0 A).


Fig. 7. From top to bottom: compensated control voltage on phase a, faultcurrent, and fault indicator for different values of Rf (simulation).

Fig. 8. Evolution of the fault indicator with respect to the operating point.

Fig. 8 shows the evolution of the indicator for different faultconfigurations regarding the operating point. Three surfacesare shown in this figure. They correspond, respectively, to thefollowing fault configurations: Rf = 1 Ω, Rf = 10 Ω andthe healthy machine. k = 0.5 for both faulty cases. Thesesimulation results show that the fault indicator level dependson the speed, but it is almost insensitive to the current. Onthe other hand, the indicator is different from zero even forthe healthy machine. In fact, the presence of non-ideal powerswitches, digital sampling with zero order hold, and numericalerrors create a threshold and the surface is between −2 and 2.Nevertheless, this threshold does not prevent us to detect thefault because the fault indicator level is higher than 20 for alltested operating points.

2) Sensitivity to Parameters: In this part, we show theimpact of various parameters uncertainties on the proposedindicator. Consider the following parametric errors:{

Lcy = Lcy ± ΔLcy

ψm = ψm ± Δψm

Rs = Rs ± ΔRs

(23)

Fig. 9. Normalized deviations on the nominal value of the fault indicator F(see Fig. 8) with respect to parameter uncertainties (Lcy , ψm, Rs).

where ΔLcy , Δψm, and ΔRs are fixed, respectively to 20%,10%, and 20%. Fig. 9 shows the impact of these uncertaintieson the fault indicator for several operating points. In all cases,k = 0.5 and Rf = 10 Ω.

According to this figure, uncertainties on ψm have a minorimpact on the fault indicator. Meanwhile, Rs and Lcy uncer-tainties affect the indicator, respectively, at low and high speeds.The same tests have been performed when Vd and Rd vary±50% around their nominal values, and it was concluded thatthe indicator is not sensitive to these parameters.

These results show that the parameter uncertainties may biasthe fault indicator. Two phenomena can be distinguished: at lowspeeds, a rather good knowledge on Rs is required, whereas athigh speed, Lcy should be relatively well known.

3) Transient Conditions: The fault detection in transientmodes is often unachievable with frequently used methodsbased on spectral analysis because of the nature of frequency


Fig. 10. Four ramps used for transient mode tests.

Fig. 11. Simulation results in transient mode. From top to bottom: indicatorresponse to reference4, reference3, reference2, and reference1.

analysis tools requiring steady-state operation. However, ac-cording to the definition of the proposed fault indicator in (21),the proposed method in this paper seems to be applicable intransitions. To verify it, we consider four acceleration rampsas shown in Fig. 10. For each ramp, we test the indicator forthe healthy machine and for k = 0.5 and Rf = 10 Ω. It shouldbe noted that the drive is controlled using a speed controlscheme.

Fig. 11 shows that the proposed method indicates the pres-ence of the fault even in transient modes. For each reference, theindicator level does not exceed ten in the healthy case. Underfault conditions, it is always greater than 20 whatever the rampslope. We can also notice that the indicator has its transientresponse due to the low-pass filtering of the fault signal (F ).However, obviously, this transient response does not prevent usdetecting the fault. Finally, it is obvious that the fault detectionis more efficient if the speed is higher.

4) Conclusion: The performed tests show that the proposedmethod is well adapted for detecting the interturn faults inPMSMs. Nevertheless, a detection threshold appears due tonumerical errors, and it may be higher in experimental tests

Fig. 12. Experimental test bench.

because of neglected irregularities in the practical system. Insteady-state operation, the obtained results show that we candetect the fault whatever the operating point is, but the faultindicator level evolves with the operating point even if thesevariations are less important than those of some other tech-niques [8]. In transient mode, the simulation results confirm thatthe fault can be detected if the motor speed is sufficiently high.These advantages come from the use of model (18) for derivingthe fault signal. The counterpart of these advantages lies in thefact that the proposed method is relatively sensitive to someof the system parameters. In addition, an initializing phase forestimating important parameters is necessary. The authors workon this topic.

IV. EXPERIMENTAL TESTS

A. Experimental Setup

To verify our theoretical investigations on the interturn faultdetection, a test bench with a nonsalient pole PMSM suppliedby a PWM controlled VSI is implemented. It is shown inFig. 12, and its parameters are given in Table I. The machineis a 1.5 kW doubly fed PMSM. The two stator windings areconnected in series to get a simple three-phase drive. Half-winding short circuits can be realized in this manner. The driveis controlled by a TI-DSP digital control card whose samplingperiod is 100 μs. It sends PWM command signals to a three-leg VSI supplied by a 200 V DC source. The load is a PMsynchronous generator connected to a diode rectifier. Then,the rectified DC voltage supplies a variable resistance througha buck converter in such a way that the load torque can becontrolled. This allows achieving different operating points.

B. Results

1) Fault Detection: At first, let us test the efficiency of theproposed method in interturn fault detection. We begin by acurrent control scheme with the following operating point:⎧⎨⎩

idref = 0 A

iqref = 15 A

Ω = 1000 r/min.


Fig. 13. From top to bottom: compensated control voltage, fault current, andfault indicator for different values of Rf (experimental).

The rotor position correction is δ = 8◦. The dead time is fixed toTm = 1 μs. The inverter switches are represented by a voltagedrop of Vd = 1.2 V and a dynamic resistance of Rd = 0.9 Ω.

Fig. 13 shows the experimental results obtained for the sametests performed by the simulation (see Fig. 7). Obviously, thestator phase currents are less sinusoidal than those in simula-tions. It is why the fault indicator level is greater than that inFig. 7 for the healthy machine (Fhealthy

∼= 42). Meanwhile,the variations of the fault indicator for low values of Rf arealmost the same as in the simulation. Indeed, for Rf = 10 Ωand Rf = 5 Ω, the increase of F with respect to Fhealthy isrespectively about 15 and 55. Hence, such faulty cases canbe easily detected. However, due to the healthy case level ofthe indicator (Fhealthy), the detection of less severe faults isstrongly affected compared to the simulation.

This difference between the simulation and experimentalresults can be explained by the fact that the healthy model ofthe machine is not yet accurate enough to reflect the reality.Even if the compensation terms (back EMF, inverter model,and unbalanced inductances) bring some improvements, we canconsider that some other phenomena are not taken into account.Actually, mechanical losses, iron losses, and end windingsconfiguration are ignored. The authors analyze these parameteruncertainties and measurement errors in the same manner as in[31], [32] and prepare another paper for presenting their results.

2) Influence of the Operating Point: Now, we study the im-pact of the operating point on the indicator level. To do that, theload torque is varied in such a way that q-current sweeps from0 A to 5 A while the angular speed varies between 1000 r/minto 2000 r/min. For each point, the fault indicator level is evalu-ated and saved. The results are plotted in Fig. 14. We can noticein this figure that the indicator level is highly dependent to theangular speed, but it is almost insensitive to the q-current and soto the load torque. Also, the detection threshold (indicator levelfor the healthy machine) is higher than that in the simulation(see Fig. 8) as it was explained in the previous paragraph. Thegeneral shape of the surfaces matches well with Fig. 8. It isobvious from Fig. 14 that the proposed approach allows keepinga good distance between the faulty and healthy cases even at

Fig. 14. Test results for several operating points (experimental).

Fig. 15. Test results in transient conditions-from top to bottom: reference1,reference2, reference3, and reference4 (experimental).

low speeds and low loads, whereas many other methods needhigh load and speed to be really efficient.

3) Transient Conditions: Finally, we verify our simulationresults in transient mode exposed in Section III. The samefour ramps in Fig. 10 are used to create a mechanical transientresponse. Experimental results shown in Fig. 15 reveal that theproposed approach can detect the fault for all cases. Once again,the fault indicator goes through a transition before settling to itsfinal level. The gap between the indicator levels in healthy andfaulty cases is sufficiently high for detecting the fault even ifthe indicator level for the healthy machine is much higher thanthat in Fig. 11.

V. CONCLUSION

A method for detecting interturn faults in PMSMs is pre-sented in this paper. It is based on a current residual analy-sis. The difference between estimated currents obtained by ahealthy model and the real currents of the machine is usedto detect the fault. The healthy model is improved by severalcompensation terms such as inverter losses, real back-EMFwaveforms, and unbalanced inductances.


The residual current is used to define the fault indicator. Thesimulation and experimental results confirmed the efficiencyof this indicator. These tests show that the proposed methodcan detect an interturn fault without additional sensors in bothsteady-state and transient conditions. The method is tested tak-ing into account the operating point variations and parametersuncertainties. The method does not require high CPU time andcan be easily implemented in an experimental drive to give real-time results. However, other ignored irregularities should betaken into account to improve the sensitivity of the indicatorto the fault. Anyway, because of the negligible impact of thefault on the system (Fig. 2), it seems to be impossible to detectthe fault when Rf > 1 kΩ without violating the requirementsgiven in Section I.

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Nicolas Leboeuf received the Engineer degree fromthe Ecole Nationale d’Electricité et de Mécanique,Nancy, France, in 2009. Currently, he is workingtoward the Ph.D. degree in the Groupe de Rechercheen Electrotechnique et Electronique de Nancy at theInstitut National Polytechnique de Lorraine, Nancy.

His main research interests are fault detection,modeling, and control of electric machines.


Thierry Boileau received the Master’s degree(DEA PROTEE) and the Ph.D. degree in electricalengineering from the Institut National Polytechniquede Lorraine, Nancy, France, in 2004 and 2010,respectively.

Currently, he is with the Groupe de Rechercheen Electrotechnique et Electronique de Nancy. Hismain research interests are diagnostics and controlof electrical machines supplied by static converters.

Babak Nahid-Mobarakeh (M’05) received theM.Sc. degree in electrical engineering from theUniversity of Tehran, Tehran, Iran, in 1995, andthe Ph.D. degree in electrical engineering from theInstitut National Polytechnique de Lorraine (INPL),Nancy, France, in 2001.

From 2001 to 2006, he was with the Centre de Ro-botique, Electrotechnique et Automatique, Amiens,France, as an Assistant Professor at the Universityof Picardie. Currently, he is with the Groupe deRecherche en Electrotechnique et Electronique de

Nancy at the INPL. His main research interests are in nonlinear and robustcontrol techniques applied to power systems.

Guy Clerc (M’90–SM’10) was born in Libourne,France, on November 30, 1960. He received the En-gineer’s and Ph.D. degrees in electrical engineeringfrom the Ecole Centrale de Lyon, Ecully, France, in1984 and 1989, respectively.

He is a Professor at the Université ClaudeBernard-Lyon 1, Villeurbanne, France, where heteaches electrical engineering. He carries out re-search on control and diagnosis of induction ma-chines at the Centre de Génie Électrique de Lyon/Université Claude Bernard-Lyon 1.

Farid Meibody-Tabar received the Engineer degreefrom the Ecole Nationale d’Electricité et de Mé-canique of Nancy, Nancy, France, in 1982, the Ph.D.and “Habilitation à diriger des recherches” degrees,both from the Institut National Polytechnique deLorraine (INPL), Nancy, France, in 1986 and 2000,respectively.

Since 2000, he has been a Professor at the NationalPolytechnique de Lorraine (INPL), Nancy, France.His research activities in the Groupe de Rechercheen Electrotechnique et Electronique de Nancy deal

with electric machines, their supply, and their control.

real-time detection of interturn faults in pm drives using back-emf estimation and residual analysis

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