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A Novel Speed Estimation Method of Induction Motors Using Real-Time Adaptive Extended Kalman Filter

  • Zhang, Yanqing (Dept. of Electrical Engineering, Xi'an University of Technology) ;
  • Yin, Zhonggang (Dept. of Electrical Engineering, Xi'an University of Technology) ;
  • Li, Guoyin (CRRC SRI Chongqing S&T Co., Ltd) ;
  • Liu, Jing (Dept. of Electrical Engineering, Xi'an University of Technology) ;
  • Tong, Xiangqian (Dept. of Electrical Engineering, Xi'an University of Technology)
  • Received : 2017.05.29
  • Accepted : 2017.10.01
  • Published : 2018.01.01

Abstract

To improve the performance of sensorless induction motor (IM) drives, a novel speed estimation method based on the real-time adaptive extended Kalman filter (RAEKF) is proposed in this paper. In this algorithm, the fuzzy factor is introduced to tune the measurement covariance matrix online by the degree of mismatch between the actual innovation and the theoretical. Simultaneously, the fuzzy factor can be continuously self-tuned tuned by the fuzzy logic reasoning system based on Takagi-Sugeno (T-S) model. Therefore, the proposed method improves the model adaptability to the actual systems and the environmental variations, and reduces the speed estimation error. Furthermore, a simple exponential function based on the fuzzy theory is used to reduce the computational burden, and the real-time performance of the system is improved. The correctness and the effectiveness of the proposed method are verified by the simulation and experimental results.

Keywords

Induction motor (IM);Speed estimation;Real-time adaptive extended Kalman filter (RAEKF);Fuzzy factor

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Fig. 1. Speed estimation structure based on AEKF

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Fig. 2. A fuzzy system

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Fig. 3. Membership functions of input and output variableof FIS: (a) Membership functions of DOMk; (b)Membership functions of sk

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Fig. 4. T-S fuzzy system

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Fig. 5. Output surface of FIS

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Fig. 6 The curve of adjustment factor sk

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Fig. 7. System block frame of sensorless vector controlbased on RAEKF

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Fig. 8. Experimental platform

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Fig. 9. Experimental results based on RAEKF in widespeed range

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Fig. 10. Speed response and stator current based onRAEKF when the given speed ranges from +100pirad/s to -100pi rad/s

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Fig. 11. Experimental results based on RAEKF at 1pi rad/swith step load from 0 to 150% rated torque

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Fig. 12. Speed estimation performance based on RAEKFwhen speed-sensor fails at 100pi rad/s with 100%rated torque

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Fig. 13. Experimental comparison of the estimated speedand the speed estimation error at 2pi rad/s with thestator resistance deviation |ΔRs|=30%. (a) EKF. (b)RAEKF.

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Fig. 14. Experimental comparison of the estimated speedand the speed estimation error at 2pi rad/s with therotor resistance deviation |ΔRr |=30%. (a) EKF.(b) RAEKF.

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Fig. 15. Experimental comparison of the estimated speedand the speed estimation error at 2pi rad/s with themutual inductance deviation |ΔLm |=30%. (a)EKF. (b) RAEKF

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Fig. 16. Experimental comparison of the estimated speedand the speed estimation error at 100pi rad/s withgross external disturbance. (a) EKF. (b) RAEKF

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Fig. 17. Experimental comparison of the estimated speedand the speed estimation error at 100pi rad/s withgross estimation error. (a) EKF. (b) RAEKF

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Fig. 18. Experimental results based on EKF and RAEKF at100pi rad/s when a step load is added with 100%rated torque. (a) EKF. (b) RAEKF

Table 1. Motor parameters

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Table 2. Comparison of the speed estimation error

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