PMSM Parameter Estimation for Sensorless FOC Based on Differential Power Factor

Candelo-Zuluaga, Carlos; Riba, Jordi-Roger; García, A.

doi:10.1109/tim.2021.3096861

Cited by 25 publications

(12 citation statements)

References 30 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

“…Instead, to identify the stator resistance and inductance, an additional single-phase voltage source is connected to the PMSM to collect the required measurements. In [12], no load and load tests on several operating points in the entire torque-speed range are performed to identify the motor parameters. In [13], the measurements are collected under multiple predefined speed and current conditions achieved with ad hoc tests.…”

Section: Introductionmentioning

confidence: 99%

Nonintrusive Parameter Identification of IoT-Embedded Isotropic PMSM Drives

Brescia

Massenio

Nardo

et al. 2023

IEEE J. Emerg. Sel. Topics Power Electron.

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This paper proposes a nonintrusive parameter identification procedure suitable for Internet-of-Things (IoT)embedded isotropic permanent magnet synchronous machines (PMSM). The method is designed for scenarios where only measurements collected without additional sensors, dedicated tests or signal injection from in-service off-the-shelves motor drives are available. After the automatic detection of the steady state operating conditions defined by the triplet current-speedtemperature, the rotor flux linkage, the stator resistance, the inductance, and the inverter distorted voltage term are estimated using two operating points. Particular emphasis is placed in defining the criteria of selecting these two optimal operating conditions to minimize the estimation errors. The latter are due to the inevitable difference between the parameters in different operating points. As a vessel to investigate the effectiveness of the proposed parameter identification, experimental and simulation tests carried out on a high-speed PMSM drive have been used for validation purpose. The proposed method is also compared with existing methods from the literature to demonstrate its superiority in the considered scenario.

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“…Commonly used online parameter identification methods include the Adaline neural network, the extended Kalman filter, the least square method, etc., [13][14][15][16][17][18]. The extended Kalman filter method has a good recognition effect in practical applications, but it involves a lot of calculation, and even involves the inversion of high-order matrices.…”

Section: Introductionmentioning

confidence: 99%

Model Reference Adaptive Control of Marine Permanent Magnet Propulsion Motor Based on Parameter Identification

et al. 2022

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Marine permanent magnet synchronous propulsion motors have problems, such as low reliability and difficult maintenance in the traditional control. In this paper, a sensorless control system for a permanent magnet synchronous motor (PMSM) based on parameter identification is proposed. According to the mathematical model of the motor in the two-phase synchronous rotating coordinate system, a model reference adaptation system (MRAS) is used to estimate the rotor speed and rotor position of the motor. Because the MRAS is highly dependent on the motor parameters, and they will change with the environment, working state, etc., the Adaline neural network is used to identify the motor parameters online, and then the model parameters in the MRAS are corrected. The simulation results show that the combined control system can reduce the estimated error of the rotor speed by about 50% compared with the traditional method, and reduces the rotor position angle estimation error by 96%. It shows that the combined system can accurately estimate the rotational speed and rotor position of the motor, and it has high identification accuracy for the motor parameters.

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PMSM Parameter Estimation for Sensorless FOC Based on Differential Power Factor

Cited by 25 publications

References 30 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

Nonintrusive Parameter Identification of IoT-Embedded Isotropic PMSM Drives

Model Reference Adaptive Control of Marine Permanent Magnet Propulsion Motor Based on Parameter Identification

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