DOI QR코드

DOI QR Code

Comparison Study of On-line Rotor Resistance Estimators based on Alternate QD Model and Classical QD Model for Induction Motor Drives

유도전동기 드라이브에서의 대안모델과 일반표준모델에 기반한온라인 회전자저항 추정기의 성능 비교 연구

  • Kwon, Chun-Ki (Department of Medical IT Engineering, Soonchunhyang University) ;
  • Kim, Dong-Sik (Department of Electrical Engineering, Soonchunhyang University)
  • 권춘기 (순천향대학교 의료IT공학과) ;
  • 김동식 (순천향대학교 전기공학과)
  • Received : 2018.11.20
  • Accepted : 2019.01.04
  • Published : 2019.01.31

Abstract

Most of rotor resistance estimators utilizes Classical qd Model (CQDM) and Alternate qd Model (AQDM). The rotor resistance estimators based on both models were shown to provide an accurate rotor resistance estimate under conditions where flux is constant such as a field-oriented control (FOC) based induction motor drives. Under the conditions where flux is varying such as a Maximum torque per amp (MTPA) control, AQDM based rotor resistance estimator estimates actual rotor resistance accurately even in different operating points. However, CQDM based rotor resistance estimator has not been investigated and its performance is questionable under condition where flux level is varying. Thus, in this work, the performance of CQDM based rotor resistance estimator was investigated and made comparisons with AQDM based estimator under conditions where flux level is significantly varying such as in MTPA control based induction motor drives. Unlike AQDM based estimator, the laboratory results show that the CQDM based estimator underestimates actual rotor resistance and exhibits an undesirable dip in the estimates in different operating points.

대부분의 회전자 저항 추정기는 표준모델(CQDM)과 대안모델(AQDM)을 활용한다. 두가지 모델에 기반한 회전자 저항 추정기들은 자속이 일정한 FOC와 같은 제어 환경에서는 정확한 회전자 저항 추정치를 제공하는 것으로 확인되었다. 반면, 단위전류당최대토크 (MTPA) 제어기와 같이 자속이 변화하는 동작환경에서는, AQDM에 기반한 회전자 저항 추정기가 다른 동작 운전점에서도 실제 회전자 저항을 정확하게 추정함을 보여주었다. 하지만, 자속이 변화하는 동작환경에서의 CQDM애 기반 회전자 저항 추정기의 성능은 검토된 적이 없으며 그의 성능은 의문이다. 따라서, 본 연구에서는 자속이 많이 변화하는 MTPA 제어기 기반 유도전동기 드라이브에서 CQDM에 기반한 회전자 저항 추정기의 성능을 검토하였으며 AQDM에 기반한 추정기와 비교하였다. AQDM에 기반한 추정기와는 달리, CQDM에 기반한 추정기는 실제 저항치보다 낮게 추정할 뿐만 아니라 여러 운전조건변화시마다 추정한 값에서 실제 존재할 수 없는 급격한 굴곡이 존재함을 실험 결과에서 확인하였다.

Keywords

SHGSCZ_2019_v20n1_1_f0001.png 이미지

Fig. 1. Steady-state equivalent circuit of CQDM

SHGSCZ_2019_v20n1_1_f0002.png 이미지

Fig. 2. Steady-state equivalent circuit of AQDM

SHGSCZ_2019_v20n1_1_f0003.png 이미지

Fig. 3. Block diagram of rotor resistance estimators based on CQDM and AQDM

SHGSCZ_2019_v20n1_1_f0004.png 이미지

Fig. 4. Effect of temperature on torque with $T^*_e$ =150(Nm) using the MTPA control strategy based on (17)-(18)

SHGSCZ_2019_v20n1_1_f0005.png 이미지

Fig. 5. Comparison of the estimated rotor resistance by two rotor resistance estimators at one operating condition with the torque command of 150Nm

SHGSCZ_2019_v20n1_1_f0006.png 이미지

Fig. 6. Comparison of the estimated rotor resistance by two rotor resistance estimators at various operating conditions

Table 1. Specification of Baldor ZDM4115T-AM1 Induction Motor

SHGSCZ_2019_v20n1_1_t0001.png 이미지

Table 2. Table 2. Resultant parameters of CQDM for the test motor

SHGSCZ_2019_v20n1_1_t0002.png 이미지

Table 3. Coefficients of each parameter in AQDM

SHGSCZ_2019_v20n1_1_t0003.png 이미지

References

  1. M. N. Uddin and Sang Woo Nam, "New Online Loss-Minimization-Based Control of an Induction Motor Drive," IEEE Transactions on Power Electronics, Vol. 23, pp. 926-933, March, 2008. DOI: https://dx.doi.org/10.1109/TPEL.2007.915029
  2. Y. Wang, J. Arribas, T. Ito, and R. Lorenz, "Loss Manipulation Capabilities of Deadbeat Direct Torque and Flux Control Induction Motor Drives," IEEE Transactions on Industry Applications, Vol. 51, No. 6, pp. 4454-4566, 2015. DOI: https://dx.doi.org/10.1109/ECCE.2014.6954101
  3. S. Odhano, R. Bojoi, A. Boglietti, S. Rosu, and G. Griva, "Maximum Efficiency per Torque Direct Flux Vector Control of Induction Motor Drives," IEEE Transactions on Industry Applications, Vol. 51, No. 6, pp. 4415-4424, 2015. DOI: https://dx.doi.org/10.1109/TIA.2015.2448682
  4. M. Zaky and M. Metwaly, "Sensorless Torque/Speed Control of Induction Motor Drives at Zero and Low Frequencies with Stator and Rotor Resistance Estimations," IEEE Journal of Emerging and Selected Topics in Power Electroncis, Vol. 4, No. 4, pp. 1416-1429, 2016. DOI: https://dx.doi.org/10.1109/JESTPE.2016.2597003
  5. L. Zhao, J. Huang, H. Liu, B. Li, and W. Kong, "Second-Order Sliding-Mode Observer With Online Parameter Identification for Sensorless Induction Motor Drives," IEEE Transactions on Industrial Electronics, Vol. 61, No. 10, pp. 5280-5289, 2014. DOI: https://dx.doi.org/10.1109/TIE.2014.2301730
  6. F. Zidani, M. S. Nait-Said, M. E. H. Benbouzid, D. Dialto, and R. Abdessemed, "A Fuzzy Rotor Resistance Updating Scheme for an IFOC Induction Motor Drive," IEEE Power Engineering Review, pp. 47-50, 2001. DOI: https://dx.doi.org/10.1109/MPER.2001.4311131
  7. C. Kwon and S. D. Sudhoff, "An On-line Rotor Resistance Estimator for Induction Machine Drives," the 2005 International Electric Machines and Drives Conference, pp. 391-397, May 2005. DOI: https://dx.doi.org/10.1109/IEMDC.2005.195752
  8. C. Kwon and S. D. Sudhoff, "An Adaptive Maximum Torque per Amp Control Strategy," the 2005 International Electric Machines and Drives Conference, pp. 783-788, May 2005. DOI: https://dx.doi.org/10.1109/IEMDC.2005.195811
  9. C. Kwon and S. D. Sudhoff, "Genetic Algorithm-based Induction Machine Characterization Procedure with Application to Maximum Torque Per Amp Control," IEEE Transactions on Energy Conversion, Vol. 21, pp. 405-415, 2006. DOI: https://dx.doi.org/10.1109/TEC.2006.874224
  10. S. D. Sudhoff, D. C. Aliprantis, B. T. Kuhn, and P. L. Chapman, "An Induction Machine Model for Predicting Inverter-Machine Interaction," IEEE Transactions on Energy Conversion, Vol. 17, pp. 203-210, June 2002. DOI: https://dx.doi.org/10.1109/TEC.2002.1009469
  11. P. C. Krause, O. Wasynczuk, S. D. Sudohff, and S. Pekarek, Analysis of Electric Machinery and Drive Systems, p. 215-265, John Wiley & Sons, 2013. DOI: https://dx.doi.org/10.1002/9781118524336