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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 I. INTRODUCTION II. FUNDAMENTALS OF SEARCH COIL IMPLEMENTATION A. Position Estimation B. Current Estimation III. SIMULATION RESULTS IV. EXPERIMENTAL VERIFICATION INTRODUCTION 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. 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 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 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 A basic mathematical model for PMSM in rotating d–q axis is given as 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 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. The relationship between estimated rotating coordinate system and the actual rotating coordinate system is given as Current Estimation 結論 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 [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. 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