Rotor flux estimation method of bearingless induction motor based on stator current vector orientation

Existing research studies on torque ripple suppression mostly ignore the electrical loss of PMSM. However, the electrical loss will not only decrease the operating efficiency but also adversely influence the suppression of torque ripple. This paper attempts to construct a unified framework to suppress torque ripple with consideration of electrical loss. Firstly, a dynamic mathematical model of PMSM under current vector orientation is established with a combination of electrical loss. The constraints that can achieve the control of both torque ripple and electrical loss for PMSM are derived. Then, on the basis of the backstepping control principle, a closed-loop I/f integrative control method under stator current vector orientation is proposed. Meanwhile, this paper also proposes a speed estimation algorithm of PMSM based on the least-squares method to realize wide-range speed identification and an online prediction algorithm for control parameters of backstepping control to enhance the stability of the motor in operation. Both simulations and experiments have been performed to verify the effectiveness of the proposed control method, and the results indicate that torque ripple is suppressed effectively, operating efficiency is significantly improved, and all variables are regulated to track their reference signals correctly and rapidly.

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Model of Vector Controlled Induction Drive

Solid State Phenomena ◽

10.4028/www.scientific.net/ssp.147-149.149 ◽

2009 ◽

Vol 147-149 ◽

pp. 149-154

Author(s):

Roma Rinkeviciene ◽

Andrius Petrovas

Keyword(s):

Induction Motor ◽

Reference Frame ◽

Real Variable ◽

Flux Linkage ◽

Current Channel ◽

Stator Current ◽

Synchronous Reference Frame ◽

Rotor Flux ◽

Drive Model ◽

Motor Model

The paper presents the model of vector controlled induction motor. Induction motor model is carried out in reference frame, fixed on stator. Elaborated model of the drive is suitable for investigation dynamics because output signals appear as real currents, flux linkages and other variables. Vector control law model is implemented for variables in synchronous reference frame; therefore controller operates with transformed variables. Structure of designed model reflects the real variable speed drive. Model of vector controlled induction drive comprise model of induction motor, feedback signals and blocks as well as speed and flux reference blocks and controllers for producing motor supply signals. Feedback blocks are used for calculation stator current vector in the rotor flux linkage reference frame. For flux and speed control are designed lag controllers. Between advantages of that model is additional simulation of current channel law.

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Proportional Integral Estimator of the Stator Resistance for Direct Torque Control Induction Motor Drive

International Journal of Power Electronics and Drive Systems (IJPEDS) ◽

10.11591/ijpeds.v8.i4.pp1631-1641 ◽

2017 ◽

Vol 8 (4) ◽

pp. 1631

Author(s):

Jagdish Gangadharrao Chaudhari ◽

Sanjay Bhauraoji Bodkhe ◽

Mohan V. Aware

Keyword(s):

Induction Motor ◽

Estimation Error ◽

Direct Torque Control ◽

Estimation Method ◽

Operating Conditions ◽

Torque Control ◽

Proportional Integral ◽

Stator Current ◽

Current Estimation ◽

Stator Resistance

In this paper, an improved proportional integral stator resistance estimation for a direct torque controlled induction motor is proposed. This estimation method is based on an on-line stator resistance correction regarding the variations of the stator current estimation error. In fact, the input variable of the PI estimator is the stator current estimation error. The main idea is to tune accurately the stator resistance value relatively to the evolution of the stator current estimation error gradient to avoid the drive instability and ensure the tracking of the actual value of the stator resistance. But there is an unavoidable steady state error between the filtered stator current modulus and its estimated value from the dq model of the machine which is due to pseudo random commutations of the inverter switches. An offset has been introduced in order to overcome this problem, for different speed command values and load torques. Simulation results show that the proposed estimator was able to successfully track the actual value of the stator resistance for different operating conditions

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A speed and flux estimation method of induction motor using fuzzy extended kalman filter

2014 International Power Electronics and Application Conference and Exposition ◽

10.1109/peac.2014.7037941 ◽

2014 ◽

Cited By ~ 3

Author(s):

Zhonggang Yin ◽

Lu Xiao ◽

Xiangdong Sun ◽

Jing Liu ◽

Yanru Zhong

Keyword(s):

Kalman Filter ◽

Induction Motor ◽

Extended Kalman Filter ◽

Estimation Method ◽

Flux Estimation

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Speed Sensorless Control of Six-Phase Asynchronous Motor Drive

Recent Innovations in Mechatronics ◽

10.17667/riim.2017.1/8. ◽

2017 ◽

Vol 4 (1.) ◽

Author(s):

Z.M.S. Elbarbary

Keyword(s):

Induction Motor ◽

High Performance ◽

Asynchronous Motor ◽

Estimation Method ◽

Motor Drives ◽

Operating Conditions ◽

Speed Estimation ◽

Speed Sensorless ◽

Stator Current ◽

Current Error

Multi -phase ac motor drives are nowadays considered for various applications, due to many advantages that they offer when compared to three-phase motors. Cancellation of mechanical position or speed sensors at the motor shaft have the attractions for adjustable speed drives of induction motor to reduce the cost and increase the reliability. To replace the sensor, information of the rotor speed is extracted from measured stator currents and voltages at motor terminals. This paper investigates speed estimation method using model reference adaptive system (MRAS) to improve the performance of a sensorless vector controller of six-phase induction motor (IM). In the proposed method, the stator current is used as the state variable to estimate the speed. Since the stator current error is represented as a function of the first degree for the error value in the speed estimation, the proposed method provides fast speed estimation and is also, more robust to variations in the stator resistance, compared with other MRAS methods. Consequently, this method can improve the performance of a sensorless vector controller in a low speed region and at zero-speed. The proposed method is verified by simulation using the Matlab/Simulink package. The performance of the proposed system is investigated at different operating conditions. The proposed controller is robust and suitable for high performance six-phase induction motor drives. Simulation results validate the proposed approaches.

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