Rotor Temperature Estimation of Squirrel-Cage ... - IEEE Xplore

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 40, NO. 4, JULY/AUGUST 2004

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Rotor Temperature Estimation of Squirrel-Cage Induction Motors by Means of a Combined Scheme of Parameter Estimation and a Thermal Equivalent Model Christian Kral, Member, IEEE, Thomas G. Habetler, Fellow, IEEE, Ronald G. Harley, Fellow, IEEE, Franz Pirker, Member, IEEE, Gert Pascoli, Member, IEEE, Helmut Oberguggenberger, and Claus-Jürgen M. Fenz

Abstract—This paper deals with a rotor temperature estimation scheme for fan-cooled mains-fed squirrel-cage induction motors. The proposed technique combines a rotor resistance estimation method with a thermal equivalent circuit. Usually, rotor resistance estimation works quite well under rated load conditions. By contrast, if the motor is slightly loaded, rotor resistance estimation becomes inaccurate due to the small slip. Therefore, rotor temperature estimation under low-load conditions may be estimated by a thermal equivalent model. In order to determine the rotor resistance and, thus, rotor temperature accurately, several machine parameters have to be obtained in advance. Load tests provide the leakage reactance and the iron losses of the induct machine. The stator resistance has to be measured separately. The parameters of the thermal equivalent model are a thermal resistance and a thermal capacitance. These parameters are derived from a heating test, where the reference temperature is provided from the parameter model in the time domain. This lumped thermal parameter model is based on the assumption that the total rotor temperature increase is caused by the total sum of the losses in the induction machine. Measuring results of a 1.5-kW and an 18.5-kW four-pole lowvoltage motor and a 210-kW four-pole high-voltage motor are presented and compared. Index Terms—Induction machine, rotor cage, temperature estimation, thermal model.

I. INTRODUCTION

T

HE knowledge of the rotor temperature of an induction motor enables motor surveillance, protection, and operation based on the thermal limits of the actual machine. However,

Paper IPCSD-04–023, presented at the 2003 IEEE International Electric Machines and Drives Conference, Madison, WI, June 1–4, and approved for publication in the IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS by the Electric Machines Committee of the IEEE Industry Applications Society. Manuscript submitted for review June 18, 2003 and released for publication April 1, 2004. This work was supported by Georgia Power and by Duke Power. C. Kral was with the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332–0250 USA, on leave from Arsenal Research, 1030 Vienna, Austria (e-mail: [email protected]). F. Pirker, G. Pascoli, H. Oberguggenberger, and C.-J. M. Fenz are with Arsenal Research, 1030 Vienna, Austria (e-mail: [email protected]; [email protected]; [email protected]; [email protected]; [email protected]). T. G. Habetler and R. G. Harley are with the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332–0250 USA (e-mail: [email protected]; [email protected]). Digital Object Identifier 10.1109/TIA.2004.830759

rotor temperature measurement is a difficult and expensive task [1], [2]. Therefore, the only appropriate way of temperature acquisition is the estimation based on machine models. Rotor temperature can be determined with the help of the estimated resistance of the rotor cage. This task can be performed by evaluating the equations of the induction machine accordingly [3]. Consequently, some of the machine parameters have to be determined in order to be able to compute the rotor resistance. The accuracy of the estimated rotor temperature is mainly determined by the accuracy of the employed model and the involved machine parameters. However, the method utilized in this paper does require speed measurement. Nevertheless, other applications may employ observer based schemes instead, which eliminate speed measurement [4]. Other rotor temperature estimation techniques rely on thermal equivalent circuits. A thermal model can be obtained by subdividing the machine into several parts. Each of these parts is analyzed with respect to its conduction and convection heat transfer coefficients and heat storage capabilities [5], [6]. The obtained coefficients can be interconnected to a thermal equivalent circuit. For the determination of the parameters, the exact machine design including all dimensions and material properties have to be known. The heat sources are basically the copper losses in the winding and iron losses. The calculations are often supported by a finite-element analysis [7]. However, for the determination of certain coefficients, measurement are required [8], [9]. The particular number of heat sources and energy storages determine the complexity and capability of the actual model [10], [11]. A comprehensive thermal model entails the difficulty of determining the required parameters. However, even a simple model which utilizes only one heat source and one energy storage can fulfill the task of practicable rotor temperature estimation [12]–[14]. The advantage of the presented scheme is that the utilized thermal equivalent circuit is extremly simple. The circuit consists only of a thermal resistor and a thermal capacitor. The parameters of this model can be identified by means of a single heat-up test. No further identifications are required. Therefore, the simplicity of the thermal model leads to a robust rotor temperature estimation technique. The drawback of the simple model is that the employed thermal equivalent circuit is not able to estimate stator temperature. However, since

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the correct temperature estimation of the employed thermal equivalent circuits could be verified, the utilization of such a simple thermal model is justified.

The actual losses in watts can be computed by (4) where

II. CONCEPT The proposed scheme is based on both a rotor resistance estimation technique and a lumped thermal model of an induction machine. Temperature estimation based on a parameter model that determines the rotor resistance is quite accurate under rated load conditions. Nevertheless, if the motor is slightly loaded, slip diminishes and rotor resistance estimation becomes inaccurate. Therefore, rotor temperature of a slightly loaded induction machine may be better approximated by a thermal model which evaluates the losses of the induction machine. The required parameters of such a thermal circuit can be determined from a full-load heat test of the induction machine by means of an approximation of the estimated temperature of the parameter model. The advantage of a parameter based temperature estimation technique is that a significant change of the thermal conditions can be sensed, for example, the malfunction of the cooling fan. The drawback of the low-load accuracy of the parameter model can be compensated by a thermal parameter scheme. For these reasons, the proposed technique utilizes a combination of a parameter and a thermal model in order to improve the accuracy throughout the power range. Yet, it must be noted that the accuracy of the thermal model cannot exceed the systematic accuracy of the parameter model, since the parameter model serves as reference for the determination of the thermal parameters. At any rate, the accuracy of the parameter model in the low load range can still be improved. This is performed by an appropriate consideration of the iron losses which have to estimated from a load test. A. Parameter Model The parameter model is based on the estimation of the rotor resistance. Therefore, voltages, currents, and rotor speed have to be obtained. The stator voltage and current space phasors are composed of the instantaneous values of the phase voltages and currents, respectively. The per unit (p.u.) stator (index ) space phasors are

(5) is the reference (apparent) power of the induction machine. This quantity also equals the rated voltamps of the motor. The stator current space phasor corresponding to the stator voltage equation has to be reduced by the resistive component that represents the iron losses (6) Then, the stator voltage equation is (7) where

is the normalized time (8)

is the p.u. stator flux and is the p.u. stator resistance and linkage. However, the stator flux linkage can be determined from (7) by numeric integration. For steady-state operation (9) where is the p.u. angular stator (supply) frequency. Once we obtain we can also determine the rotor flux linkage (index ) which is the stator flux linkage decreased by the leakage flux (10) is This approach assumes that the total leakage reactance due to the stator. Consequently, there is no rate assigned to the rotor, which simplifies the rotor voltage equation [15]. However, this assumption does not affect the validity of the machine can be defined equations. Thus, the complex rotor reactance to the stator current . as the ratio of the rotor flux leakage This complex rotor reactance also equals the (real) rotor (magdivided by a rotor frequency dependent netizing) reactance term, which can be derived from the rotor voltage equation

(1) (11) (2) Rotor frequency In this equation , , and are the instantaneous phase voltages and , , and are the instantaneous phase currents. The references and are the peak values of the respective rated phase voltage and the rated phase current. The modeled iron losses are proportional to the square of the supply voltage. The p.u. equation is (3)

(12) is the difference between the angular supply frequency and the mechanical angular speed. Since this is a p.u. value, too, it equals the slip of the induction machine (if operated at rated frequency). For the computation of the complex rotor reactance (11) we have to determine the rotor flux linkage with the help of (10) and (9). However, they can be calculated by means of the measured

KRAL et al.: ROTOR TEMPERATURE ESTIMATION OF SQUIRREL-CAGE INDUCTION MOTORS

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voltage and current space phasor and the stator resistance and the leakage reactance . In addition, the equivalent resistor (which represents the iron losses) has to be known in order to determine the rectified current (6). Each operating point is characterized by a complex rotor reactance value (11), which is determined by the rotor (magnetizing) reactance and the rotor time constant and the angular rotor frequency . The involved rotor reactance is mainly dependent on the actual level of saturation, whereas the rotor resistance Fig. 1.

Considered power flow of a squirrel-cage induction machine.

(13) is a function of the actual rotor temperature. Once we either measure or estimate the mechanical rotor speed we are able to determine the remaining machine parameters and in (11) accordingly.

model that is based on the assumption, that the total rotor resistance (temperature) increase is approximately caused by the , stator copper losses , and rotor sum of iron losses copper losses (20) (21) (22)

(14) (15)

B. Rotor Temperature Estimation The p.u. rotor resistance (13) can be determined through (14) and (15). This quantity appears in the rotor voltage equation of the induction machine. However, the real (physical) rotor resistance

Iron losses have already been derived in (3). Stator copper losses are proportional to the stator resistance and the square of the stator current (reduced by the component referring to the iron losses). In the real machine, stator resistance is also a function of the actual stator temperature. If the stator winding is equipped with a temperature sensor, this information can be used to track the stator resistance. Usually, such a sensor is not available. In such a case we assume that the actual temperature of the stator winding equals the temperature of the rotor cage

(16) can be derived from a comprehensive model of a squirrel cage which consists of rotor bars and two end-rings. Consequently, the rotor resistance is composed of a weighting of the and the total rotor end-ring resistance rotor bar resistance

(17) Rotor temperature increase is, therefore, a combined rotor bar and end-ring temperature increase with respect to thermal stress and thermal properties of the actual cage. The used temwith reperature coefficient of aluminum is spect to the investigated die-cast cage rotors of the low-voltage induction machines (18) (19) The index of the temperature increase indicates that this estimated temperature refers to the parameter ( ) estimation. C. Thermal Model The derived model of the induction machine considers iron losses (3) as well as the stator and rotor copper losses, which and the rotor resisare represented by the stator resistance tance . Fig. 1 shows the power flow of the machine model. We can now introduce a simplified lumped parameter thermal

(23) According to the design of the machine, the actual stator temperature may either be smaller or larger than the rotor temperature. Even if the proposed approach is not absolutely correct, the effect on the total accuracy is partially compensated by the finally determined parameters of the thermal model which represent a best fit approximation. The thermal equivalent circuit with respect to the equation of rotor temperature increase, (24) is shown in Fig. 2. This equivalent circuit is composed of a thermal resistance and a thermal capacitance . The index of the temperature increase (25) indicates that this temperature estimation technique refers to the thermal ( ) model. III. PARAMETER DETERMINATION The application of the introduced rotor temperature estimation technique requires certain machine parameters. These pa, the leakage reacrameters are the (cold) stator resistance , and the thermal tance , the iron losses (3) represented by parameters of (24). All these parameters have to be known a priori in order to apply the proposed scheme. Therefore, it is

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axis. The parameter of the depicted locus diagram is the p.u. rotor frequency . The lower semicircle represents motor operation and the upper semicircle represents generating. The limits of the locus are (30) (31) The leakage reactance can be determined by performing a load test. For this test, the motor is loaded from no load up to rated load. The respective voltages and currents are used to compute the stator flux phasor (9) and the complex stator reactance (28). Once we obtain the measuring points in the complex plane we determine the best approximation of the circular locus. Such a best fit algorithm can be obtained by means of a least-squares fit. The distance from the origin of the complex plane to the outmost left point of the circle is the leakage reactance . At the beginning, the described algorithm has to be performed without consideration of iron losses. Once the iron losses are ascertained, the algorithm for the determination of the leakage reactance can be repeated in order to refine and track the result.

Fig. 2. Simplified thermal equivalent model of the rotor.

C. Iron Losses Fig. 3. Complex stator reactance locus of the induction machine as a function of parameter ! (p.u. rotor frequency).

necessary to do some initial measurements and identifications with respect to the actual machine before the algorithm can be evaluated. A. Stator Resistance The (cold) stator resistance can be measured by means of dc voltage and current measurement. For that purpose the machine has to be disconnected from the supply. Usually the is measured. If the machine is line-to-line resistance wye connected the p.u. phase resistance is (26) if the machine is delta connected, the p.u. phase resistance can be determined by (27)

The determined rotor resistance (13) is not a function of the actual load with respect to a certain rotor temperature. This fact is used in order to estimate the total iron losses of the induction machine without disconnecting the load. Iron losses are represented by the p.u. resistance (3), (4). The iron losses which are determined in this way, therefore, do not represent the exact real iron losses. They rather represent the equivalent iron losses, such that the condition of constant rotor resistance is fulfilled. However, this approach is an adequate way to achieve the desired parameter response. Measuring results have been acquired for a 1.5-kW, an 18.5-kW, and a 210-kW four pole induction machine and the variation of the estimated rotor resistance versus p.u. real power (proportional to load) is shown in Figs. 4–6. The best fit of iron losses of both figures is represented by a horizontal curve. The results are summarized in Table I. The equivalent iron losses of the 1.5-kW machine are approximately 2.3% of the reference power whereas the equivalent iron losses of the 18.5-kW machine are 1.5% of the reference power. The iron losses of the 210-kW high-voltage machine are 1.8% of the reference power.

B. Leakage Reactance We assumed in the previous section, that the total leakage reactance is assigned to the stator—the rotor is strayless (10). The complex stator reactance (28) (29) can be defined in analogy to the preceding defined complex rotor reactance (11). The locus diagram of that quantity is depicted in Fig. 3. The complex stator reactance values are represented by a circle, whose center is located on the positive real

D. Thermal Parameters As soon as the leakage reactance and the iron losses repare determined, the thermal resistance and resented by can be identified. This procedure is the thermal capacitance performed during a heating test where the motor is loaded with (approximately) rated torque. The rotor parameter determination provides an adequate rotor temperature estimation for rated is used to operating conditions. Therefore, the temperature estimate the thermal parameters. Even several heat up tests may be used in order to refine and smooth the obtained result. From (13) and (19) the rotor temperature curve can be determined. However, the very first value estimated with the cold


Fig. 4. Estimated rotor resistance for various iron loss components as a function of real power. The actual iron losses are represented by the best : for the investigated 1.5-kW approximation of a horizontal curve (p four-pole induction machine).

= 0 0227

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Fig. 6. Estimated rotor resistance for various iron loss components as a function of real power. The actual iron losses are represented by the best : for the investigated approximation of a horizontal curve (p 210-kW four-pole induction machine).

= 0 0179

TABLE I DATA OF THE INVESTIGATED INDUCTION MACHINES

Fig. 5. Estimated rotor resistance for various iron loss components as a function of real power. The actual iron losses are represented by the best : for the investigated approximation of a horizontal curve (p 18.5-kW four-pole induction machine).

= 0 0154

motor is used to define the reference temperature rise . Besides temperature, the total losses (20) are required in order to ascertain the thermal parameters in (24). A two-dimensional and enables the minimization iteration of the parameters of the least square of the error of the estimated temperature difand ference of (32) The index main

refers to obtained measuring points in the time do-

Fig. 7. Estimated rotor temperature with respect to the parameter model ( # ) and with respect to the thermal model ( # ) for the investigated 1.5-kW induction machine.

1

1

The approximation of rotor temperature with respect to the thermal model is depicted in Figs. 7–9 for the investigated moand as well as the tors. The estimated thermal parameters thermal time constant

rameter model from the temperature estimated by the thermal model is less than 3 C for all three motors. The units of the thermal resistor and the thermal capacitor are mixed p.u. and SI units. Usually, the physical unit of the thermal . In this case the motor losses are derived as resistor is p.u. quantities. Therefore, the unit of the thermal resistor in this paper is only . The physical meaning of the thermal resistor can be explained with the help of the steady-state temperature of (33)

(34)

(35)

of the investigated machines are summarized in Table II. The maximum difference of the temperature estimated by the pa-

If the p.u. total losses are, e.g., 5% of the reference power (5), the steady-state rotor temperature is 5% of . The physical unit of a

(33)

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Fig. 10. Infrared sensor, mounted at a hole through one of the bearing plates of the 18.5-kW induction machine. Fig. 8. Estimated rotor temperature with respect to the parameter model ( # ) and with respect to the thermal model ( # ) for the investigated 18.5-kW induction machine.

1

1

Fig. 11.

Fig. 9. Estimated rotor temperature with respect to the parameter model ( # ) and with respect to the thermal model ( # ) for the investigated 210-kW induction machine.

1

1

Infrared sensor, mounted inside the 210-kW induction machine.

thermal model will be assigned to The initial value for the thermal model for the actual decay curve will be provided from the parameter model, though. V. MEASURING RESULTS

TABLE II THERMAL PARAMETERS OF THE INVESTIGATED INDUCTION MACHINES

thermal capacitor is usually . In this paper the unit of the , since the p.u. losses have no physical thermal capacitor is unit. The mixed units of the thermal resistor and capacitor do not affect the unit of thermal time constant , though (Table II). IV. ROTOR TEMPERATURE ESTIMATION The proposed rotor temperature estimation scheme is based on a simple algorithm that combines the parameter model and represents the temperthe thermal model. In the following, ature increase with respect to the combined (index ) scheme. If the p.u. real power (36) is larger than 0.5, then the combined temperature increase equals the temperature increase, determined by the parameter . Otherwise, the temperature obtained from the model,

In order to provide a reference for the obtained rotor temperature estimation technique, rotor temperature has been measured, too. For that purpose, an infrared sensor was mounted at a hole through one of the bearing plates of the 1.5-kW and the 18.5-kW motor (Fig. 10). The sensor of the 210-kW motor was mounted inside (Fig. 11). It must be emphasized that measured rotor temperature represents the temperature of the end-ring at the driving end (opposite side of cooling fan). This measured temperature does not necessarilymatchtheestimatedrotortemperatureexactly,sincetheestimated temperature is composed of the rotor end-ring and the rotor bar temperature. However, it is an adequate value to compare. The measured and the combined rotor temperature increase are depicted in Figs. 12–14 for the 1.5-kW, the 18.5-kW, and the 210-kW induction machine. A. 1.5-kW Induction Machine Altogether, four stages are carried out. They are designated as A, B, C, and D in Fig. 12. A temperature rise, p.u. real power ; ; B cooling down, p.u. real power ; C temperature rise, p.u. real power . D cooling down, p.u. real power


1

Fig. 12. Measured temperature increase # and estimated temperature increase # for four stages of the investigated 1.5-kW induction machine. : ); B: cooling (p A: temperature rise (p : ); C: temperature rise (p : ); D: cooling (p : ).

1 08

08 0 15

05

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1 01

Fig. 14. Measured temperature increase # and estimated temperature increase # for two stages of the investigated 210-kW induction machine. A: temperature rise (p : ); B: cooling (p : ).

1

07

B. 18.5-kW Induction Machine Altogether, five stages are investigated which are designated as A, B, C, D, and E in Fig. 13. ; A temperature rise, p.u. real power ; B cooling down, p.u. real power ; C temperature rise, p.u. real power ; D cooling down, p.u. real power E cooling down, motor disconnected, . During the temperature rise stages A and C, the estimated deviates from the measured rotor temperature increase by approximately 10 C. rotor temperature increase The cooling stages B and D show similar deviations, and stage E shows even smaller deviations. Without the thermal model the deviation of the parameter model in stage D was about 20 C. Therefore, the combined scheme permits an improvement of accuracy during temperature decay. However, it should be noted that rotor temperature increase during stage E can only be determined by the thermal model, since the motor is disconnected from the mains.

1

Fig. 13. Measured temperature increase # and estimated temperature increase # for five stages of the investigated 18.5-kW induction machine. A: temperature rise (p : ); B: cooling (p : ); C: temperature rise (p : ); D: cooling (p : ); E: cooling, motor disconnected from ). supply (p

1 0 85 =0

0 85 0 05

0 45

During the temperature rise stages A and C, the measured matches the estimated rotor rotor temperature increase quite well. The maximum deviation temperature increase of measured and estimated temperature increase is 5 C. The temperature deviation during the cooling stages B and D does not exceed 5 C, either. The resemblance between the measured and estimated curves is evident.

C. 210-kW Induction Machine Altogether, two stages are carried out. They are designated as A and B in Fig. 14. The 6300-V motor has been operated at 5000 V due to the restrictions of the laboratory. Therefore, maximum mechanical power was less than 210 kW. The motor was designed in such a way that the maximum rotor temperature is less than 60 C, which is extremely little. A temperature rise, p.u. real power ; B cooling down, p.u. real power . The maximum deviation of measured and estimated temperature increase is less than 2 C.

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VI. CONCLUSION A parameter and a thermal model have been introduced, both capable of estimating the rotor temperature increase of an induction machine. The determination of the required machine parameters has been presented and discussed. A combined scheme of the estimated temperature with respect to the parameter and the thermal model has been defined. Since the parameter model fails for low load, temperature estimation of the low-load region is provided by the thermal model. For the full-load region the temperature of the parameter model serves as the temperature estimate. Measuring results for a 1.5-kW, an 18.5-kW low-voltage, and a 210-kW high-voltage induction machine have been presented and compared. The estimated rotor temperature matches the measured temperature within an accuracy of 5 C (for the 1.5-kW motor), 10 C (for the 18.5-kW motor), and 2 C (for the 210-kW motor). Nevertheless, it should be noted that the measured temperature refers to the end-ring at the driving end, which does not necessarily exactly equal the rotor temperature derived by the lumped machine models.

REFERENCES [1] H. Yahoui and G. Grellet, “Measurement of physical signals in rotating part of electrical machine by means of optical fiber transmission,” in Proc. IEEE Instrumentation and Measurement Technol. Conf. (IMTC-96) Quality Measurements: The Indispensable Bridge between Theory and Reality., vol. 1, 1996, pp. 591–596. [2] J. Dymond, R. Ong, and N. Stranges, “Instrumentation, testing and analysis of electric machine rotor steady-state heating,” in Proc. IEEE-IAS 48th Annu. Petroleum and Chemical Industry Conf., 2001, pp. 297–303. [3] R. Beguenane and M. Benbouzid, “Induction motors thermal monitoring by means of rotor resistance identification,” IEEE Trans. Energy Conversion, vol. 14, pp. 566–570, Sept. 1999. [4] K. Hurst and T. Habetler, “A thermal monitoring and parameter tuning scheme for induction machines,” in Conf. Rec. 32nd IEEE-IAS Annu. Meeting, vol. 1, 1997, pp. 136–142. [5] J. Bates and A. Tustin, “Temperature rise in electrical machines as related to the properties of thermal networks,” Proc. Inst. Elect. Eng., pt. A, vol. 103, pp. 471–492, 1956. [6] P. Mellor, D. Roberts, and D. Turner, “Lumped parameter thermal model for electrical machines of tefc design,” Proc. IEE—Elect. Power Applicat., vol. 138, no. 5, pp. 205–218, 1991. [7] R. Ibtiouen, S. Mezani, O. Touhami, N. Nouali, and M. Benhaddadi, “Application of lumped parameters and finite element methods to the thermal modeling of an induction motor,” in Proc. IEEE Int. Electric Machines and Drives Conf., 2001, pp. 505–507. [8] A. Bousbaine, M. McCormick, and W. Low, “In-situ determination of thermal coefficients for electrical machines,” IEEE Trans. Energy Conversion, vol. 10, pp. 385–391, Sept. 1995. [9] A. Shenkman and M. Chertkov, “Experimental method for synthesis of generalized thermal circuit of polyphase induction motors,” IEEE Trans. Energy Conversion, vol. 15, pp. 264–268, Sept. 2000. [10] J. Moreno, F. Hidalgo, and M. Martinez, “Realization of tests to determine the parameters of the thermal model of an induction machine,” Proc. IEE—Elect. Power Applicat., vol. 148, no. 5, pp. 393–397, 2001. [11] A. Boglietti, A. Cavagnino, M. Lazzari, and M. Pastorelli, “A simplified thermal model for variable-speed self-cooled industrial induction motor,” IEEE Trans. Ind. Applicat., vol. 39, pp. 945–952, July/Aug. 2003. [12] J. Boys and M. Miles, “Empirical thermal model for inverter-driven cage induction machines,” Proc. IEE—Elect. Power Applicat., vol. 141, no. 6, pp. 360–372, 1994. [13] A. Eltom and N. Moharari, “Motor temperature estimation incorporating dynamic rotor impedance,” IEEE Trans. Energy Conversion, vol. 6, pp. 107–113, Mar. 1991. [14] S. Zocholl, “Motor analysis and thermal protection,” IEEE Trans. Power Delivery, vol. 5, pp. 1275–1280, July 1990. [15] H. Kleinrath, “Equivalent circuits for transfomers and induction machines” (in German), E&i, vol. 110, pp. 68–74, 1993.

Christian Kral (M’00) received the Dipl.-Ing. and Ph.D. degrees from Vienna University of Technology, Vienna, Austria, in 1997 and 1999, respectively. From 1997 to 2000, he was a Scientific Assistant in the Institute of Electrical Drives and Machines, Vienna University of Technology. Since 2001, he has been with Arsenal Research (Österreichisches Forschungs- und Prüfzentrum Arsenal Ges.m.b.H.),Vienna, Austria. From January 2002 until April 2003 he was on sabbatical as a Visiting Professor at the Georgia Institute of Technology, Atlanta. His research activities are focused on diagnostics and monitoring techniques, machine models and the simulation of faulty machine behavior.

Thomas G. Habetler (M’83–SM’92–F’02) received the B.S.E.E. and M.S. degrees in electrical engineering from Marquette University, Milwaukee, WI, in 1981 and 1984, respectively, and the Ph.D. degree from the University of Wisconsin, Madison, in 1989. From 1983 to 1985, he was with the Electro-Motive Division, General Motors, as a Project Engineer. While there, he was involved in the design of switching power supplies and voltage regulators for locomotive applications. He is currently a Professor of Electrical Engineering at Georgia Institute of Technology, Atlanta. His research interests are in electric machine protection and condition monitoring, switching converter technology, and drives, and he has published over 100 papers in the field. He is a regular consultant to industry in the field of condition-based diagnostics for electrical systems. Dr. Habetler has received four conference prize paper awards from the IEEE Industry Applications Society (IAS). He currently serves as Past President of the IEEE Power Electronics Society, and Past Chair of the Industrial Power Converter Committee of the IAS.

Ronald G. Harley (M’77–SM’86–F’92) was born in South Africa. He received the B.Sc.Eng. (cum laude) and M.Sc.Eng. (cum laude) degrees from the University of Pretoria, Pretoria, South Africa, in 1960 and 1965, respectively, and the Ph.D. degree from London University, London, U.K., in 1969. In 1971, he was appointed to the Chair of Electrical Machines and Power Systems at the University of Natal, Durban, South Africa. He was a Visiting Professor at Iowa State University, Ames, in 1977, at Clemson University, Clemson, SC, in 1987, and at Georgia Institute of Technology, Atlanta, in 1994. He is currently the Duke Power Company Distinguished Professor at Georgia Institute of Technology. His research interests include the dynamic behavior and condition monitoring of electric machines, motor drives, and power systems, and controlling them by the use of power electronics and intelligent control algorithms. He has coauthored some 280 papers published in refereed journals and international conference proceedings, of which nine have received prizes. Dr. Harley was elected as a Distinguished Lecturer by the IEEE Industry Applications Society (IAS) for the years 2000 and 2001. He is currently the Vice-President of Operations of the IEEE Power Electronics Society, and the Chair of the Distinguished Lecturers and Regional Speakers Program of the IAS. He is a Fellow of the Institution of Electrical Engineers, U.K., and the Royal Society in South Africa, and a Founder Member of the Academy of Science in South Africa formed in 1994.


Franz Pirker (M’03) was born in 1968. He received the Dipl.-Ing. degree in electrical engineering from Vienna University of Technology, Vienna, Austria, in 1997. Since 1999, he has been the head of the business area Monitoring, Energy and Drive Technologies, Arsenal Research (Österreichisches Forschungsund Prüfzentrum Arsenal Ges.m.b.H.), Vienna, Austria. In this area, the main research topics are online monitoring of machines and online diagnoses of high-voltage generators. In these fields, Arsenal Research is developing new online monitoring methods and products. His main research interests are inverter drives, especially in combination with faulty induction machines. As a member of the Rotor Fault Detection Group, he worked on the realization of an online monitoring system in an industrial voltage-source inverter drive.

Gert Pascoli (M’03) received the Dipl.-Ing. degree from Vienna University of Technology, Vienna Austria, in 1995. In 1991, he joined the Institute of Switchgear and High Voltage Technology, Vienna University of Technology. Since 1996, he has been with Arsenal Research (Österreichisches Forschungs- und Prüfzentrum Arsenal Ges.m.b.H.), Vienna Austria. His main research topics are the online monitoring of machines and the partial discharge diagnosis of electric insulations of high- and low-voltage machines.

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Helmut Oberguggenberger received the Dipl.-Ing. degree from Vienna University of Technology, Vienna, Austria, in 2001. Since 2001, he has been with Arsenal Research (Österreichisches Forschungs- und Prüfzentrum Arsenal Ges.m.b.H.), Vienna, Austria. His research activities are focused on modeling and computer simulation of power trains, power electronics, control systems, and hardware programming.

Claus-Jürgen Fenz received the Dipl.-Ing. (FH) degree from Technikum Wien, Vienna, Austria, in 2002. Since 2002, he has been with Arsenal Research (Österreichisches Forschungs- und Prüfzentrum Arsenal Ges.m.b.H.), Vienna, Austria. His research activities are focused on power electronics, electric machines, and signal processing.