A Novel Universal Sensor
Concept for Survivable
PMSM Drives
Yao Da, Student Member, IEEE, Xiaodong Shi, Member, IEEE, and Mahesh
Krishnamurthy, Senior Member, IEEE
IEEE TRANSACTIONS ON POWER ELECTRONICS, VOL. 28, NO. 12,
DECEMBER 2013，5630-5638
教授:王明賢
學生:王沼奇
目錄
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I. INTRODUCTION
II. FUNDAMENTALS OF SEARCH COIL IMPLEMENTATION
 A. Position Estimation
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B. Current Estimation
III. SIMULATION RESULTS
IV. EXPERIMENTAL VERIFICATION
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INTRODUCTION
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Furthermore, additional hardware is usually required in the process of
high-frequency signal injection and detection.
Another type of position sensorless technique is based on an electric
machine’s back electromotive force (EMF) [8], [9]. The position vector
can be estimated by integration of the back EMF.
However, phase back EMF is usually not achievable in a machine drive
system, because phase voltage is also affected by stator resistance drop
and armature current-induced flux.
Therefore, machine model-based algorithms are adopted to eliminate
those effects to get rotor’s flux vector.
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However, immeasurable areas exist in the space vector
hexagon of traditional space vector pulse width
modulation (SVPWM) technique, and the duration of an
effective switching state may be too short for reliable
measurements.
Therefore, special PWM techniques are required in the
implementation of vector control.
Another current reconstruction method uses three shunt
resistors in a series of three lower switches of an inverter
[19], [20]. Voltage across shunt resistors is sampled when
the lower switches are turned ON. However,
immeasurable areas still exist because of the existence of
a zero vector.
FUNDAMENTALS OF SEARCH
COIL IMPLEMENTATION
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This three-phase Y-connected
machine has a concentrated
armature winding and a
sinusoidal back EMF.
Fig. 1 presents the test machine
used in this study
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Twelve search coils are wound
around each tooth of this
machine, with four turns each,
for multi fault detection and
health monitoring.
Fig. 2. Among the 12 search coils
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A basic mathematical model for PMSM in rotating d–q axis is given
as
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whereRs is the stator resistance,Ld andLq the stator inductance in d and q axes,
respectively, ud and uq the stator windings’ terminal voltage in d and q axes,
respectively, id and iq are the phase currents in d and q axes, respectively, ωe
the electrical angular speed, and Ke is the back EMF constant
Due to the high input impedance of the analog to digital converter (ADC)
channels in a DAQ or DSP, current flowing through the search coils can be
neglected. Therefore, the terminal voltage of search coils can be expressed as

where ud s and uq s are the search coils’ terminal voltage in d and q
axes, respectively, Md s and Mq s the mutual inductances between
phase winding and search coils in d and q axes, respectively, andKe s is
the back EMF constant of search coils.
Position Estimation
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In the position estimator, an
estimated electrical angle θe is
assumed. Compared to the
actual electrical angle θa , the
angular difference is defined as
Their relationship is illustrated in Fig. 3.
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The relationship between estimated rotating coordinate system and
the actual rotating coordinate system is given as
Current Estimation
結論
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This paper proposes the implementation of a novel
universal sensor for position and current estimation in a
PMSM. Search coils are mounted around stator teeth of
each phase. Due to its ability to achieve position and
current estimation without being effected by stator
resistance variation, it improves the reliability of drive
system for propulsion applications and gives it a “+1”
fault tolerance. Co-simulations using FEA and Simulink
along with experimental implementation have been used
to verify the effectiveness of the proposed method. They
clearly show that the universal sensor concept can be a
highly effective tool in the development of survivable
drives.
REFERENCES
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[1] A. Piippo, M. Hinkkanen, and J. Luomi, “Adaptation of motor parameters in sensorless PMSM drives,” IEEE
Trans. Ind. Appl., vol. 45, no. 1,
pp. 203–212, Feb. 2009.
[2] M. Elbuluk, T. Liu, and I. Husain, “Neural network-basedmodel reference
adaptive systems for
high performancemotor drives andmotion controls,” in Conf. Rec. 2000 IEEE Ind. Appl. Conf., vol. 2, pp. 959–965.
[3] J. R. Frus and B. C. Kuo, “Closed-loop control of step motors using waveform detection,” in Proc. Int. Conf.
Stepping Motors Syst., Leeds, U.K., 1976, pp. 77–84.
[4] M. E. Haque, L. Zhong, andM. F. Rahman, “A sensorless initial rotor position estimation scheme for a direct
torque controlled interior permanent magnet synchronous motor drive,” IEEE Trans. Power Electron., vol. 18,no. 6,
pp. 1376–1383, Nov. 2003.
[5] S.Ogasawara andH.Akagi, “An approach to real-time position estimation at zero and low speed for a PM motor
based on saliency,” IEEE Trans.
Ind. Appl., vol. 34, no. 1, pp. 163–168, 1998.
[6] M. J. Corley and R. D. Lorenz, “Rotor position and velocity estimation for a salient-pole permanent magnet
synchronous machine at standstill and high speeds,” IEEE Trans. Ind. Appl., vol. 34, no. 4, pp. 784–789, 1998.
[7] J.-I. Ha, K Ide, T. Sawa, and S.-K. Sul, “Sensorless rotor position estimation of an interior permanent-magnet
motor from initial states,” IEEE Trans. Ind. Appl., vol. 39, no. 3, pp. 761–767, Jun. 2003.
[8] K. Iizuka,H.Uzuhashi, M. Kano, T. Endo, andK.Mohri, “Microcomputer control for sensorless brushless motor,”
IEEE Trans. Ind. Appl., vol. 21, no. 3, pp. 595–601, 1985.
[9] X. Shi, J. P. Serradilla, and M. Krishnamurthy, “A back EMF-based rotor position prediction in Permanent
Magnet machines for survivable wind generator systems,” in Proc. 36th Annu. Conf. IEEE Ind. Electron. Soc.
(IECON 2010), pp. 778–783.
[10] Z. Chen, M. Tomita, S. Doki, and S. Okuma, “New adaptive sliding observers for position- and velocitysensorless controls of brushless DC motors,” IEEE Trans. Ind. Electron., vol. 47, no. 3, pp. 582–591, 2000.