Proceedings of the KIEE Conference (대한전기학회:학술대회논문집)

The Korean Institute of Electrical Engineers (대한전기학회)
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On-line Parameter Estimation of IPMSM Drive using Neural Network

신경회로망을 이용한 IPMSM 드라이브의 온라인 파라미터 추정

  • Park, Ki-Tae (Sunchon National University Major of Electrical Control Engineering) ;
  • Choi, Jung-Sik (Sunchon National University Major of Electrical Control Engineering) ;
  • Ko, Jae-Sub (Sunchon National University Major of Electrical Control Engineering) ;
  • Lee, Jung-Ho (Sunchon National University Major of Electrical Control Engineering) ;
  • Kim, Jong-Kwan (Sunchon National University Major of Electrical Control Engineering) ;
  • Park, Byung-Sang (Sunchon National University Major of Electrical Control Engineering) ;
  • Chung, Dong-Hwa (Sunchon National University Major of Electrical Control Engineering)
  • 박기태 (순천대학교 전기제어공학과) ;
  • 최정식 (순천대학교 전기제어공학과) ;
  • 고재섭 (순천대학교 전기제어공학과) ;
  • 이정호 (순천대학교 전기제어공학과) ;
  • 김종관 (순천대학교 전기제어공학과) ;
  • 박병상 (순천대학교 전기제어공학과) ;
  • 정동화 (순천대학교 전기제어공학과)
  • Published : 2006.07.12
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Abstract

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

Keywords