PMSM model with phase-to-phase short-circuit and diagnosis by ESA and EPVA

POWER ENGINEERING AND ELECTRICAL ENGINEERING VOLUME: 14 |NUMBER: 5 |2016 |DECEMBER PMSM Model with Phase-to-Phase Short-Circuit and Diagnosis by ESA and EPVA Chourouk BOUCHAREB, Mohamed Said NAIT SAID Electrical Engineering Department, Laboratory LSPIE Batna 2000, Batna University, Route de Biskra, 05078, Algeria [email protected], [email protected] DOI: 10.15598/aeee.v14i5.1928 Abstract. One of the most frequent faults in PMSM stator is the insulation failure due to the degradation of the main isolation in the motor winding. This paper is aimed at suggesting a dynamic model of PMSM with phase-to-phase fault based on an equivalent electric circuit model including the real form of back EMF. The faulty model is used for studying the machine behavior and extracting the fault signatures for diagnosis. Two diagnostic techniques the Spectral Analysis (ESA) and Extend Park’s Vectors Approach (EPVA) based on frequency analysis are applied to detect this kind of fault. Keywords EPVA, ESA, Inter-turn fault, phase-to-phase fault, PMSM model. 1. Introduction In recent years, Permanent Magnet Synchronous Motor (PMSM) has become one of most important electric machines because of the inherent advantages of high power density, high efficiency, small weight, high reliability and easy control of external torque of stator’s current control. Consequently, it is widely used in industry, e.g. in traction, automobiles, robotics and aerospace technology, as well as electric vehicles and ship propulsion systems [1], [2] and [3]. The fault diagnosis of electrical machines had been the target of an intense amount of interesting researches during the last 30 years. Reducing maintenance costs and preventing unscheduled down-times, which result in losses of production and financial incomes and benefitting from their utility in safetysensitive applications, are the priorities of electrical drives for manufacturers and operators [4], [5] and [6]. In fact, correct diagnosis and early detection of incipient faults require the development of an accurate model for electrical machine, able to simulate electrical faults and to apply an effective diagnostic technique. However, model accuracy and computation time represents two opposite criteria. Conventional model (equivalent electric circuit or equivalent magnetic circuit) obtained with Park transformation for instance is based on restrictive assumptions and does not require long computation time [7] and [8]. On the other hand, model obtained with the finite elements method is based on minimal assumption and requires long computation time [9] and [10]. There is a real need to establish an alternative model, which offers a good balance between accuracy and computation time. One of the most common faults, called insulation failure, is the inter-turn short circuit in one of the stator coils. Since the coil insulation material is under the high voltage and temperature stress, it degrades gradually and finally loses the insulating characteristic [6]. The inter-turn fault is mostly caused by mechanical stress, moisture and partial discharge, which is accelerated for inverter supplied electrical machines [11]. In this paper, a dynamic model of a stator surface mounted PMSM with inter-turn fault is presented. We focus on phase-to-phase fault of the stator winding. This model based on equivalent electric circuit exhibits a trade-off between simplicity and precision, and it is used for studying a machine behavior under fault conditions for different levels of fault severity using MATLAB Simulink software. Exploiting this faulty model to extract fault signatures in order to diagnose and to predict the insulation failure breakdown when the fault is not very severe in order to avoid the machine winding damages. To detect this fault, we chose two simple and useful techniques based on frequency analysis. These techniques are Electric Spectral Analysis (ESA) and Exc 2016 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING 522 POWER ENGINEERING AND ELECTRICAL ENGINEERING VOLUME: 14 |NUMBER: 5 |2016 |DECEMBER tend Park’s Vectors Approach (EPVA). The contribution of this work is the addition of the real waveform of back Electro Motive Force (EMF) of healthy machine which contains a harmonic at 3·fsof supply frequencies, because if the model does not take the uncertainties, like real back-EMF, the indicator will give a wrong diagnostic. 2. PMSM Fault Dynamic Model 2.1. Phase-to-Phase Fault Dynamic Model The phase-to-phase fault denotes insulation failures between two windings of two phases at the stator. The insulation failure is modeled by a resistance, where its value depends on the fault severity. The stator winding of a PMSM machine with phase-to-phase fault is represented by Fig. 1. In this figure, the fault occurs between ’a’ and ’b’ phases, rfdenotes the fault insulation resistance. The sub-windings (as1) and (as2) represent respectively, the healthy and faulty part of the phase winding a, and sub-windings (bs1) and (bs2) represent, the healthy and faulty part of the phase winding brespectively. When the fault resistance rfdecreases towards zero, the insulation fault evaluates towards an inter-turn full short-circuit. lf rf lbs1 lbs2 Rbs1, Lbs1 Rcs, Lcs Ras1, Las1 Ras2, Las2 las1 las2 las lbs lcs Rbs2, Lbs2 Fig. 1: Three-phases winding with phase-to-phase fault. 2.2. PMSM Healthy Model in abc-Coordinates The voltages equations from the circuit in Fig. 1 without fault(healthy machine), given by rfinfinite value, as in [2], [12] and [13] are: [Vs]=[Rs]·[Is]+[Ls]· d dt ·[Is]+[Es],(1)   vas vbs vcs  =  Rs0 0 0Rs0 0 0 Rs    Ias Ibs Ics  + +  L M M M L M M M L  · d dt ·  Ias Ibs Ics  +  eas ebs ecs  , (2) where the healthy machine variable and parameters are: •vas,bs,cs - three phase stator voltages, •Ias,bs,cs - three phase stator currents, •eas,bs,cs - three phase back EMF, •Rsstator resistance, •Lself inductance of the stator, •Mmutual inductance of the stator. 2.3. PMSM Faulty Model in abc-Coordinate Voltage equations, which describe the faulty circuit presented in Fig. 1, can be expressed as: [Vs] = vas1vas2vbs1vbs2vcs T,(3) where: •vas1the voltage of the healthy part phase a, •vas2the voltage of faulty part of phase a, •vbs1the voltage of the healthy part phase b, •vbs2the voltage of faulty part of phase b. The new resistances of healthy and faulty parts of phase ’a’ and ’b’ are calculated as follows: Ras1= (1 −σ)·Ras,(4) Ras2=σ·Ras,(5) Rbs1= (1 −σ)·Rbs,(6) Rbs2=σ·Rbs,(7) σ=Nf Ns .(8) The study of the elementary circuits of the phases has given the following relations: vas =vas2+vas1,(9) vbs =vbs2+vbs1,(10) c 2016 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING 523 POWER ENGINEERING AND ELECTRICAL ENGINEERING VOLUME: 14 |NUMBER: 5 |2016 |DECEMBER Ias1=Ias,(11) Ibs1=Ibs,(12) where Ras1is the stator phase resistance of healthy parts of ’a’ phase while Ras2is the faulty stator phase resistance. Rbs1it is the stator phase resistance of healthy parts of ’b’ phase while Rbs2is its faulty stator phase resistance, σis the ratioof number of the turns (Nf) over the phase winding number of the turns (Ns). The self-inductances of the faulty and healthy parts of winding (aas1,aas2), and winding (bbs1,bbs2) are proportional to the square of the fraction of shorted turns σ, and also the mutual inductance is proportional to this number of both parts. Therefore, we assume: Las1= (1 −σ)2Las,(13) Las2=σ2Las,(14) Mas2b=σM, (15) Mas1as2=σ(1 −σ)L, (16) where Las1is the stator phase inductance of healthy parts of a phase while Las2is the stator phase inductance of faulty parts of a phase, Ifis the additional current engendered by the short circuit, rfis the insulation faulty resistance and vfis the corresponded faulty voltage. The stator currents become: [Is]=[Ias (Ias −If)Ibs (Ibs +If)Ics]T.(17) The equation which describes the short circuit loop is in Eq. (18). From previous analysis, we obtain the global equations governing the behavior of the machine with the presence of this short-circuit fault as the Eq. (19). In the Eq. (19): R0=Ras +Rbs +Rcs,(20) Lf=−(−La2+Ma2b2−Lb2+Mb2a2),(21) Mbf =−Ma1a2+Ma1b2−La2+Ma2b2,(22) Mcf =−Mca2+Mcb2.(23) The expression of the electromagnetic torque can be written as follows: Te=eas ·Ias +ebs ·Ibs +ecs ·Ics −ef·If Ω,(24) where Ωis the mechanical angular speed. 2.4. PMSM Faulty Model in α, β-Coordinates The machine equations with inter-turn fault in stationary αand βaxis reference frame are in Eq. (25), where: Rr=r2 3−Ra2− Rb2 2,(26) rf=Ra2+Rb2+Rf,(27) rb2=1 2√2Rb2,(28) Mfα =r2 3Maf − Mbf 2− Mcf 2,(29) Mfβ =1 2√2 (Mbf −Mcf ),(30) Ls=L−M, (31) with, •Iα,β -αand βaxis components of stator currents, •eα,β -αand βcomponents of stator back EMF. Then the electromagnetic torque expression for the phase-to-phase fault model becomes: Te=eα·Iα+eβ·Iβ−ef·If Ω.(32) We consider for all the studies that the electromotive force of the healthy motor has a sinusoidal form as shown in Fig. 2(a) and contains a 3rd harmonic at 3·fs of supply frequencies as seen in Fig. 2(b). 00.01 0.02 0.03 0.04 0.05 0.06 0.07 0.0 8 −40 −20 0 20 40 Time (s) EMF (V) (a) Electromotive force. 050 100 150 200 250 300 0 10 20 30 40 Frequency (Hz) |EMF(V)| 3rd harmonic (b) Spectrum analysis. Fig. 2: Electromotive force and its spectrum analysis. c 2016 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING 524 POWER ENGINEERING AND ELECTRICAL ENGINEERING VOLUME: 14 |NUMBER: 5 |2016 |DECEMBER 0 = −Ra2·Ias +Rb2·Ibs −(La2+Ma1a2−Mb2a1−Mb2a2)·dIas dt −(Ma2b1+Ma2b2−Lb2−Mb2b1)·dIbs dt + −(Ma2c−Mb2c)·dIcs dt −ef+Ra2+Rb2+rf·If−(−La2+Ma2b2−Lb2+Mb2a2)·dIf dt .(18)     vas vbs vcs 0     =     Rs0 0 Ra2 0Rs0Rb2 0 0 Rs0 −Ra2Rb20R0         Ias Ibs Ics If     +     L M M Maf M L M Mbf M M L Mcf Maf Mbf Mcf Lf     d dt     Ias Ibs Ics If     +     eas ebs ecs −ef     .(19) 3. Dynamic Fault Model Simulation Results The study of the behavior of PMSM under fault conditions using the proposed fault dynamic model requires an accurate knowledge of circuit parameters. The PMSM parameters are given as shown in AppA [2]. 00.2 0.4 0.6 0.8 11.2 −40 −20 0 20 40 Time (s) Ia−Ib−Ic (A) f r= 100 Ωf r= 7 Ω f r= 0.5 Ω (a) Phase currents. 00.2 0.4 0.6 0.8 11.2 −60 −40 −20 0 20 40 Time (s) If (A) rf = 100 Ωrf = 7 Ω rf = 0.5 Ω (b) Faulty current. −15 −10 −5 0 5 10 00.2 0.4 0.6 0.8 1 1.2 Time (s) rf= 100 Ωrf= 7 Ω rf= 0.5 Ω Te(N m) (c) Electromagnetic torque. −2000 −1000 0 1000 2000 Pa (Watt) 00.2 0.4 0.6 0.8 1 1.2 Time (s) rf= 100 Ω rf= 7 Ω rf= 0.5 Ω (d) Absorbed power. Fig. 3: Phase currents, faulty current, electromagnetic torque and absorbed power versus time for three values of fault resistances: rf= 100 Ω,rf= 7 Ω and rf= 0.5 Ω. The machine is supposed to be supplied by 3-phases sinusoidal balanced voltage source with star connection and without neutral connection and operates at synchronous speed (speed and supply frequency are 1000 rpm and 66.67 Hz respectively). Simulation of the proposed model is realized using MATLAB environment. 00.2 0.4 0.6 0.8 11.2 −60 −40 −20 0 20 40 Time (s) Ia−Ib−Ic (A) 0.5 0.1 0.7 (a) Phase currents. 00.2 0.4 0.6 0.8 1 1.2 −100 −50 0 50 Time (s) If (A) 0.1 0.7 0.5 (b) Faulty current. −30 −20 −10 0 10 00.2 0.4 0.6 0.8 1 1.2 Time (s) 0.1 0.7 0.5 Te (N m) (c) Electromagnetic torque. 0.8 1.2 −3 −2 −1 0 1 2 Pa (kWatt) 0 0.2 0.4 0.6 1 Time (s) 0.1 0.7 0.5 (d) Absorbed power. Fig. 4: Phase currents, faulty current, electromagnetic torque and absorbed power versus time at three values of the fraction of shorted turns: (σ= 0.1,σ= 0.5and σ= 0.7) and rf= 0.5 Ω. For this model, Fig. 3 shows the characteristics phase currents (a, b, c), faulty current (If), electromagnetic torque and absorbed power for different values of fault insulation resistance such as rf= 100 Ω,0.5 Ω and 7 Ω. The fraction of shorted turns is fixed at 50 %. Figure 4 shows the characteristics (phase currents (a, b, c), faulty current (If), electromagnetic torque and absorbed power for different values of the fraction of shorted turns (σ= 10 %, σ= 50 % and σ= 70 %), where the fault insulation resistance is fixed to rf= 0.5 Ω. As it can be seen from Fig. 3, for three different values of fault resistances (healthy case: rf= 100 Ω and c 2016 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING 525 POWER ENGINEERING AND ELECTRICAL ENGINEERING VOLUME: 14 |NUMBER: 5 |2016 |DECEMBER   vα vβ 0  =  Rs0Rr 0Rsrb2 Rrrb2rf  ·  Iα Iβ If  +  Ls0Mfα 0LsMfβ Mfα Mf β Lf  ·d dt ·     Ias Ibs Ics If     +  eα eβ −ef  .(25) faulty case: rf= 7 Ω and rf= 0.5 Ω) when the fault resistance decreases, the three phases currents increase to compensate the negative effects of the short-circuit fault. It can cause a current unbalance in the power supply, and the increase of the absorbed power. We can observe a torque ripple when the faulty case is applied. Changing the fraction of short-turns means changing the severity of applying fault. From Fig. 4, it is clear that the magnitude of the torque ripple is mainly determined by the severity of the fault. The magnitude of the phase currents and absorbed powerchange proportionally with the severity of the fault and become unbalanced. It would be very helpful to predict the insulationfailure, breakdown when the fault is not high developed inorder to avoid the machine winding damages [14]. 4. Diagnostic of Stator Fault by ESA and EPVA Techniques Two techniques based on frequency analysis are applied to detect faults in stator, consecutively defined in [15], [16] and [17]. First is ESA, based on the Fast Fourier decomposition of the phase currents winding, the electromagnetic torque and the absorbed power. The second is EPVA, which is based on the frequency analysis of the module of the Park’s Vector’s of currents as shown below. 4.1. Electric Spectral Analysis (ESA) We applied this technique on the phase stator currents, the instantaneously absorbed power and the electromagnetic torque. The instantaneous absorbed power is illustrated by the following equation [18]: p(t) = vas(t)ias(t) + vbs(t)ibs(t) + vcs(t)ics(t).(33) The phase stator currents, the instantaneous absorbed power and electromagnetic torque spectrum analysis results of both healthy and faulty conditions with different values of faulty resistance (rf= 100 Ω, rf= 7 Ω and rf= 0.5 Ω) of simulation machine are presented in Fig. 5, Fig. 6, and Fig. 7 respectively. 1) Currents Spectral Analysis The ESA signatures reveal the existence of a spectral component in phase ’a’ and ’b’, with a small amplitude at the frequency with value three times higher than the supply due the existence of an inter-turn short circuit in the stator winding and its amplitude increase with the increase of severity of faultas seen in Fig. 5(b), Fig. 5(c) and Fig. 5(d), where rf= 0.5 Ω and in Fig. 5(e), where rf= 7 Ω, the existence of this harmonic is due to the presence of the third harmonic of the electromotive force presented in Fig. 2(b). We can observe no existence of this harmonic in phase ’c’ because the short circuit occurs between phase ’a’ and ’b’. Note that at healthy conditions the current does not have this component (third harmonic), as seen in Fig. 5(a). 2) Electromagnetic Torque Spectral Analysis It is noticeable from Fig. 7, that in case of fault, we notice the appearance of high harmonic at double value of supply frequency, especially if rf= 0.5 Ω. The increase of the harmonic amplitude is inversely proportional to the values of fault resistance. 3) Absorbed Power Spectral Analysis Figure 6 shows the absorbed power spectrum with and without fault. We can observe only a zero frequency component at healthy conditions. In faulty conditions the same analysis as that of the electromagnetic torque is noted. From the comparative analysis of results under healthy and faulty conditions, it is clear that the fault appears in the ESA signaturedue to the presence of harmonic of even rows on the spectrum analysis of electromagnetic torque and absorbed power and by the appearance of the harmonic of odd rows on the spectrum analysis of phase currents. The appearances of these harmonics are directly related to the existence of asymmetries caused by the short-circuit in the stator winding. With the consumption that we have a balanced voltage source, the appearance of harmonics in phase ’a’ and ’b’ indicates the short-circuit between these two phases. c 2016 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING 526 POWER ENGINEERING AND ELECTRICAL ENGINEERING VOLUME: 14 |NUMBER: 5 |2016 |DECEMBER 0 50 100 150 200 250 300 0 5 10 15 Frequency (Hz) |Ia (A)| (a) Healthy case for rf= 100 Ω. 0 10 20 30 |Ia (A)| 150 200 250 0 1 2 3rd harmonic 0 50 100 150 200 250 300 Frequency (Hz) (b) Faulty case for rf= 0.5 Ω (Phase a). 0 5 10 15 20 25 |Ib (A)| 3rd harmonic 150 200 250 0 1 2 0 50 100 150 200 250 300 Frequency (Hz) (c) Faulty case for rf= 0.5 Ω (Phase b). 0 50 100 150 200 250 300 0 5 10 15 Frequency (Hz) |Ic (A)| (d) Faulty case for rf= 0.5 Ω (Phase c). 0 50 100 150 200 250 300 0 5 10 15 Frequency (Hz) |Ia (A)| 3rd harmonic (e) Faulty case for rf= 7 Ω Fig. 5: Spectrum of phase currents. 4.2. Extend Park’s Vector Approach (EPVA) This technique is based on the two equivalent currents in reference frame obtained by Park’s transformation [18]: Id=r2 3·Ias −1 √6·Ibs −1 √6·Ics,(34) Iq=1 √2·Ibs −1 √2·Ics,(35) where Idand Iqare the instantaneous values of electric currents in direct and quadrature axis. Idis always a sine wave and Iqhas a cosine wave in healthy conditions. These two components have the same values and their locus is a circle as seen in Fig. 8(a). In case of the inter-turn short circuit, the current becomes unbal050 100 150 200 250 300 0 200 400 600 800 1000 Frequency (Hz) |Pa (Watt)| (a) Healthy case for rf= 100 Ω. 0 20 40 60 80 100 120 140 160 180 20 0 0 200 400 600 800 1000 Frequency (Hz) |Pa (Watt)| 2nd harmonic (b) Faulty case for rf= 0.5 Ω. 0 500 1000 1500 |Pa (Watt)| 2nd harmonic 0 20 40 60 80 100 120 140 160 180 200 Frequency (Hz) (c) Faulty case for rf= 7 Ω. Fig. 6: Spectrum of absorbed power. 0 1 2 3 4 5 |Te (N m)| 050 100 150 200 250 300 Frequency (Hz) (a) Healthy case for rf= 100 Ω. 0 2 4 6 2nd harmonic |Te (N m)| 0 20 40 60 80 100 120 140 160 180 200 Frequency (Hz) (b) Faulty case for rf= 0.5 Ω. 0 1 2 3 4 5 2nd harmonic 0 20 40 60 80 100 120 140 160 180 200 Frequency (Hz) |Te (N m)| (c) Faulty case for rf= 7 Ω. Fig. 7: Spectrum of electromagnetic torque. anced and it can be expressed as the sum of a positive sequence and a negative sequence component. As a result of this fault, the Concordia’s vector locus shape deviates and becomes elliptic as shown in Fig. 8(b). If the motor operates under healthy conditions (i.e. under symmetrical conditions), the three currents form a balanced system and constitute a positive sequence c 2016 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING 527 POWER ENGINEERING AND ELECTRICAL ENGINEERING VOLUME: 14 |NUMBER: 5 |2016 |DECEMBER system. Hence, idand iqcan be written as below [18]: ip=qi2 d+i2 q,(36) id=√6 2·imax ·sin(ωt),(37) iq=√6 2·imax ·sin ωt − π 2,(38) where imax is a maximum value of the current positive sequence, ωis the angular supply frequency, and ip is the Park’s equivalent current module. When the system is balanced, the current Park’s vector modulus is constant as illustrated in Fig. 9(a). Under faulty condition the currents will contain other components besides the positive sequence component and in this case the Park’s Vector modulus will contain a dominant DC and AC level of the motor current supply [15] and their existence is directly related to the asymmetries, as we can see in Fig. 9(b). (a) Healthy case. (b) Faulty case. Fig. 8: Concordia’s currents vector locus. The aim of EPVA technique is to apply the frequency analysis to the Park’s vector modulus in order to obtain the EPVA signature when the system is unbalanced. After simulation and analysis, we obtain the results for healthy condition (rf= 100 Ω) and faulty conditions (rf= 7 Ω and rf= 0.5 Ω) as shown in Fig. 10. From these results, the EPVA signature reveals the existence of a aspectral component at a frequency of 00.2 0.4 0.6 0.8 11.2 0 5 10 15 Time (s) Ip (A) (a) Healthy case. 0 0.2 0.4 0.6 0.8 11.2 0 5 10 15 20 25 Time (s) Ip (A) (b) Faulty case. Fig. 9: Park’s vector modulus. 66.67 Hz-twice the fundamental supply frequency and it is so clear from results when the fault resistance decreases (the severity of fault increases) the amplitude of the spectral component makes it a good indicator of the occurred fault. 050 100 150 200 250 300 0 10 20 30 Frequency (Hz) |Ip (A)| (a) Healthy case for rf= 100 Ω. 0 20 40 60 80 100 120 140 160 180 200 0 20 40 60 Frequency (Hz) |Ip (A)| 2nd harmonic (b) Faulty case for rf= 0.5 Ω. 020 40 60 80 100 120 140 160 180 200 0 10 20 30 40 Frequency (Hz) |Ip (A)| 2nd harmonic (c) Faulty case for rf= 7 Ω. Fig. 10: Spectrum of Park’s vector modulus. 5. Conclusion This paperproposed a dynamic model for surface mounted PMSM machine under phase-to-phase shortcircuit in the stator winding. The real form of back EMF is presented and included in the model. This faulty model is used to study the behavior of the mac 2016 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING 528 POWER ENGINEERING AND ELECTRICAL ENGINEERING VOLUME: 14 |NUMBER: 5 |2016 |DECEMBER chine under various fault conditions and severity. From the analysis of the simulation results, phase-to-phase short-circuit fault causes high torque ripples and currentunbalance in the system. Higher circulating currents could be generated by the motor winding shortcircuit. More importantly, the detection of these kinds of faultsis crucial in the design and development procedure of the motor drive and its diagnosis. Two simple and effective diagnosis method as ESA and EPVA based on frequency analysis are used to analyze and to indicate the presence of the short-circuit fault between two phases in the stator. The appearance of the 2nd and 3rd harmonic indicates the presence of this fault andthe amplitude of the harmonics is proportional to the severity of this fault. The shape of Concordia’s currents vector locus is a good indicator of the presence of the fault when its form changes from the circle trajectory to an elliptical one. References [1] HADEF, M., M. R. MEKIDECHE and A. O. N’DIAYE. Diagnosis of stator winding short circuit faults in a direct torque controlled interior permanent magnet synchronous motor. In: IEEE Vehicle Power and Propulsion. Chicago: IEEE, 2011, pp. 1–8. ISBN 978-61284247-9. DOI: 10.1109/VPPC.2011.6043166. [2] VASEGHI, B., B. NAHID-MOBAREKEH, N. TAKORABET and F. MEIBODY-TABAR. 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DOI: 10.1109/28.952496. [16] CRUZ, S. M. A. and A. J. M. CARDOSO. Multiple reference frames theory: a new methode for the diagnosis of stator fault inthree phases induction motors, by the extended Park’s vector approach. IEEE Transaction On Energy Conversion. 2005, vol. 20, iss. 3, pp. 611–619. ISSN 1558-0059. DOI: 10.1109/TEC.2005.847975. [17] DYONOSIOS, V. S. and D. M. EPAMINONDAS. Induction Motor Stator Fault Diagnosis Technique Using Park Vector Approach and Complex Wavelets. In: IEEE International Conference on Electric Machinery (ICEM). Marseille: IEEE, 2012, pp. 1730–1734. ISBN 978-1-4673-0142-8. DOI: 10.1109/ICEIMach.2012.6350114. [18] PARRA, A. P., M. C. A. ENCICO, J. O. OCHOA and J. A. P. PENAR. Stator fault diagnosis on squirrel cage induction motor by ESA and EPVA. In: Power Electronics and Power Quality Applications. Bogota: IEEE, 2014, pp. 1–6. ISBN 978-1-4799-10076. DOI: 10.1109/PEPQA.2013.6614937. About Authors Chourouk BOUCHAREB was born in 1975, in Algiers, Algeria. She received an Engineer Diploma in Electrical Engineering in 1999 and an M.Sc. degree in Control engineeringin 2005, both from Electrical Engineering Department of Batna University. Her research interests include the electric machines and their control drives and diagnosis. She is a member at the Laboratory University, named Electromagnetic Induction and Propulsion Systems (LSPIE) of Batna University. Mohamed-Said NAIT-SAID was bornin 1958, in Batna, Algeria, He received an Engineer Diploma in Electrical Engineering from the National Polytechnic High School of Algiers, Algeria (February 1983), and the M.Sc. degree in Electronics and Control Engineering from Electronics Department at Constantine University in 1992. He received the Ph.D. degree in Electrical Engineering from University of Batna after he accomplished his free scientific research accomplished in Automatic Laboratory of Amiens University in French from 1996 to 1999. Currently he is a full professor at the Electrical Engineering Department of Batna University II and is responsible for the Master course of Control and Diagnosis of the Electrical Systems. From 2000–2005, Dr. Nait-Said was the head of the first created research laboratory in Batna University, named Electromagnetic Induction and Propulsion Systems (LSPIE) of Batna and also in 2006 he has been appointed the head of the scientific committee of the same department. LSPIE has been evaluated by the Algerian ministry of the universities as the best laboratory in Batna University (100 percent satisfied. Dr. Nait-Said has supervised twenty five Masters and ten Ph.D. theses. His research interests include the electric machines and their control drives and diagnosis. Appendix A - AC Driver Parameters •PN= 5 kW, •P= 4, •EMF at 1000 rpm = 34 V, •IN= 19 A. c 2016 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING 530