Journal of international Conference on Electrical Machines and Systems

  • 제3권3호
  • /
  • Pages.272-275
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  • 2014
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  • 2234-6902(pISSN)
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  • 2234-7577(eISSN)
대한전기학회 분과 (Journal of International Conference on Electrical Machines and Systems)
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Research on Speed Estimation Method of Induction Motor based on Improved Fuzzy Kalman Filtering

  • Chen, Dezhi (Special Electric Machines and High Voltage Apparatus Key Lab of National Education Ministry and Liaoning Province, Shenyang University of Technology) ;
  • Bai, Baodong (Special Electric Machines and High Voltage Apparatus Key Lab of National Education Ministry and Liaoning Province, Shenyang University of Technology) ;
  • Du, Ning (Special Electric Machines and High Voltage Apparatus Key Lab of National Education Ministry and Liaoning Province, Shenyang University of Technology) ;
  • Li, Baopeng (Special Electric Machines and High Voltage Apparatus Key Lab of National Education Ministry and Liaoning Province, Shenyang University of Technology) ;
  • Wang, Jiayin (Special Electric Machines and High Voltage Apparatus Key Lab of National Education Ministry and Liaoning Province, Shenyang University of Technology)
  • 투고 : 2014.04.03
  • 심사 : 2014.04.09
  • 발행 : 2014.09.01
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초록

An improved fuzzy Kalman filtering speed estimation scheme was proposed by means of measuring stator side voltage and current value based on vector control state equation of induction motor. The designed fuzzy adaptive controller conducted recursive online correction of measurement noise covariance matrix by monitoring the ratio of theory residuals and actual residuals to make it approach real noise level gradually, allowing the filter to perform optimal estimation to improve estimation accuracy of EKF. Meanwhile, co-simulation scheme based on MATLAB and Ansoft was proposed in order to improve simulation accuracy. Field-circuit coupling problems of induction motor under the action of vector control were solved and the parameter optimization accuracy was improved dramatically. The simulation and experimental results show that this algorithm has a strong ability to inhibit the random measurement noise. It is able to estimate motor speed accurately, and has superior static and dynamic characteristics.

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참고문헌

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