Online Multiparameter Identification Method for Sensorless Control of SPMSM

Wang, Yangrui; Xu, Yongxiang; Zou, Jibin

doi:10.1109/tpel.2020.2974870

Cited by 23 publications

(2 citation statements)

References 20 publications

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“…Whereas for online methods parameters are found in running conditions and hence different effects like temperature, saturation, VSI non-linearities can be considered during estimation for developing high control performance. Online methods can be classified as Numerical methods [11], [12], [14]− [32], [42]− [47], [49]− [51], Observer based methods [52], AI-ML based methods [33]− [41], [53], [54]. The focus of this article is on different online numerical methods for electrical parameter estimation.…”

Section: Classification Of Parameter Identification Methods For Pmsmmentioning

confidence: 99%

Comparative analysis of features of online numerical methods used for parameter estimation of PMSM

Naikawadi

Patil

Kalantri

et al. 2022

IJPEDS

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As permanent magnet synchronous motors (PMSM) have high power density, efficiency, good dynamic performance, and small size they are becoming popular in electric vehicle (EV) applications. Control performance and the efficiency of the system get affected due to electrical, mechanical parameters. Parameters value gets affected by voltage source inverter (VSI) non-linearities, temperature and magnetic saturation effects. If exact parameters for particular torque speed requirement are found, the efficiency of system increases. There are various offline and online methods for finding parameters. Offline methods are easy to implement but requires extra setup and estimate parameters in steady state. Because the effects of transient conditions are taken into account during identification, online methods for obtaining real-time data under running conditions are becoming more popular. An overview about online numerical methods to estimate electrical parameters of PMSM is given. It discusses difference between various methods in terms of computational cost, convergence speed, noise and identification error. Choosing of method will be easy using this work. For inductance estimation, the extended Kalman filter (EKF) algorithm has an identification error of 0.24% under temperature effect and -0.3% under VSI non-linearities effect. The identification error for Rs and ψf using the recursive least square (RLS) method is 0.5% and 0.02%, respectively, when temperature is considered. EKF and RLS algorithms are proposed.

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Section: Classification Of Parameter Identification Methods For Pmsmmentioning

confidence: 99%

Comparative analysis of features of online numerical methods used for parameter estimation of PMSM

Naikawadi

Patil

Kalantri

et al. 2022

IJPEDS

View full text Add to dashboard Cite

show abstract

“…Online identification methods such as the recursive least square method [23], the model reference adaptive system [24] and the extended Kalman filter [25] can also be used to solve the parameter dependency problem. However, only a limited number of parameters can be simultaneously identified, or else the rank deficiency will lead to wrong convergence points or stability problems [26]. And the accuracy of the other parts of the model that are not identified will influence the accuracy of the identification [24].…”

mentioning

confidence: 99%

Ripple Excitation-Based Adaptive Sensorless Control of IPMSM in Full Speed Range

Chen¹,

Qu²,

Qiu

2023

IEEE Access

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This paper proposes an adaptive sensorless finite-control-set model predictive control (FCS-MPC) method for the interior permanent magnet synchronous motor (IPMSM). The method is feasible in the full speed range from zero speed to the flux-weakening region above rated speed, without the method transition between the low and the high speeds. A ripple excitation-based position estimation method is proposed to extract the rotor-saliency-based position information from the inherent voltage ripples of the FCS-MPC. Therefore, the additional voltage margin consumption, the special sampling timing, or the interference in the fundamental control are all avoided. Furthermore, to alleviate the parameter dependency, an adaptive predictive model with the online estimation of the lumped voltage terms and the equivalent dynamic susceptances is proposed. In this way, the main machine parameters and the unmodeled disturbances in the d-q voltage equations of the IPMSM are all considered. The position deviation due to the cross-coupling effect is also analyzed, and a compensation method is proposed. Finally, the effectiveness of the proposed method is validated by the experimental results and the comparison with existing methods.

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Recurrent neural dynamics for handling linear equation system with rank-deficient coefficient and disturbance existence

Shi

Pan

et al. 2022

Journal of the Franklin Institute

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Online Multiparameter Identification Method for Sensorless Control of SPMSM

Cited by 23 publications

References 20 publications

Comparative analysis of features of online numerical methods used for parameter estimation of PMSM

Comparative analysis of features of online numerical methods used for parameter estimation of PMSM

Ripple Excitation-Based Adaptive Sensorless Control of IPMSM in Full Speed Range

Recurrent neural dynamics for handling linear equation system with rank-deficient coefficient and disturbance existence

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