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논문 기본 정보

자료유형
학술저널
저자정보
Zhonggang Yin (Xi’an University of Technology) Guoyin Li (Xi’an University of Technology) Chao Du (Xi’an University of Technology) Xiangdong Sun (Xi’an University of Technology) Jing Liu (Xi’an University of Technology) Yanru Zhong (Xi’an University of Technology)
저널정보
전력전자학회 JOURNAL OF POWER ELECTRONICS JOURNAL OF POWER ELECTRONICS Vol.17 No.1
발행연도
2017.1
수록면
149 - 160 (12page)

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초록· 키워드

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To improve the performance of sensorless induction motor (IM) drives, an adaptive speed estimation method based on a strong tracking extended Kalman filter with a least-square algorithm (LS-STEKF) for induction motors is proposed in this paper. With this method, a fading factor is introduced into the covariance matrix of the predicted state, which forces the innovation sequence orthogonal to each other and tunes the gain matrix online. In addition, the estimation error is adjusted adaptively and the mutational state is tracked fast. Simultaneously, the fading factor can be continuously self-tuned with the least-square algorithm according to the innovation sequence. The application of the least-square algorithm guarantees that the information in the innovation sequence is extracted as much as possible and as quickly as possible. Therefore, the proposed method improves the model adaptability in terms of actual systems and environmental variations, and reduces the speed estimation error. The correctness and the effectiveness of the proposed method are verified by experimental results.

목차

Abstract
I. INTRODUCTION
II. EKF OBSERVER FOR INDUCTION MOTORS
III. STEKF OBSERVER WITH THE LEAST-SQUARE ALGORITHM
IV. EXPERIMENTAL RESULTS
V. CONCLUSION
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UCI(KEPA) : I410-ECN-0101-2017-560-002047769