Offline inductance identification of PMSM with adaptive inverter nonlinearity compensation

Wang, Gaolin; Wang, Ying; Qi, Jiangbo; Ni, Ronggang; Chen, Wei; Xu, Dianguo

doi:10.1109/icpe.2015.7168113

Cited by 13 publications

(8 citation statements)

References 29 publications

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“…For (17) and (18), the orders of magnitude of A x , B x and C x (xϵ{1, 2}) are 10 -2 , 10 -7 and 10 -6 , respectively. Then comparing the magnitude of each part of (15) and (16), and ignoring the smaller parts, the approximate relationship between inductance deviation and prediction error can be obtained  …”

Section: B C B a C Bmentioning

confidence: 99%

“…Parameter mismatch is the primary cause of current prediction error, so, some studies have focused on motor parameter identification, such as parameter offline identification algorithm and parameter online identification algorithm. Among them, the offline parameter identification algorithms are mostly implemented by using high frequency voltage injection methods [17], adding low-pass filters [18] and so on.…”

Section: Introductionmentioning

confidence: 99%

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Current Prediction Error Reduction Method of Predictive Current Control for Permanent Magnet Synchronous Motors

et al. 2020

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Predictive current control (PCC) has been widely investigated in motor drive field. The PCC is a model-based control method and the motor prediction model is quite sensitive to motor parameters which may be inaccurately measured and change under different working conditions. In order to reduce the current prediction error of PCC caused by inductance mismatch, a current prediction error reduction method is proposed for permanent magnet synchronous motor (PMSM) by online inductance correction which is based on the relationship between inductance mismatch and current prediction error. Firstly, a proportional regulator is used to obtain the absolute correction value according to the relationship between inductance error and current prediction error. Secondly, the polarity of correction value is judged according to the fluctuation amplitudes of predicted current and actual current. Finally, an adaptive adjustment method for correction period is proposed to ensure the accuracy of inductance correction value. Experimental tests are carried out to verify the effectiveness of proposed method, and results show that the proposed method can obviously reduce current prediction error and improve control performance in practical applications. In addition, the proposed method is suitable for interior and surface mounted PMSMs. INDEX TERMS Current prediction error reduction, online inductance correction, permanent magnet synchronous motor (PMSM), predictive current control (PCC).

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Section: B C B a C Bmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Current Prediction Error Reduction Method of Predictive Current Control for Permanent Magnet Synchronous Motors

et al. 2020

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show abstract

“…Accurate offline identification of stator resistance and inductance is necessary for proper tuning of the system controller [4,5], the position based on electromotive force (EMF) [6] or the based model [7], the parameters should be obtained before motor startup, otherwise, the performance of driver will be poor. Moreover, parameter identification at standstill is essential for applications with load machinery connected, since it is not permissible to make the rotor deviate from the initial position during estimating parameters [8]. According to the operation status in the voltage source inverter (VSI) based parameter identification system, the methods can be divided into two classes: the offline and online identification methods [9].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…A lot of offline methods have already been proposed to identify the inductance and resistance parameters [8,13,14,15,16,12,17,18,19,20]. The methods [8,13] identify dand q-axis inductances and stator resistance considering inverter nonlinearities; however, additional initial rotor position estimation algorithm is required. Inductance identification methods with sequence pulse injection has been proposed [12], these techniques require a regression process, based on the fitting method.…”

Section: Introductionmentioning

confidence: 99%

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A Simple Method for Stator Inductance and Resistance Estimation for PMSM at Standstill

Dilys

Stankevič

2022

IJRCS

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An accurate stator resistance and inductance are necessary for high-performance permanent magnet synchronous motor (PMSM) control. The stator resistance and inductance can be estimated during motor standstill operation. This study proposes a standstill estimation method for the determination of dq-axis inductances and resistance of a PMSM drive system fed by a conventional voltage source inverter (VSI). The proposed method estimates both inductance and the rotor's position using the same algorithm, and knowledge of its initial position is not required. The d- and q-axis inductances were estimated by applying three short-time voltage pulses and measuring phase current peak values. The stator's resistance is estimated by monitoring the exponential decay process of the direct axis current. The method was verified by simulation and experiments conducted on two different PM synchronous motors. A good agreement of simulation and experimental results was obtained. Moreover, the proposed method is relatively simple and can identify stator resistance and inductance at any motor load condition. Compared to the existing parameter estimation strategies, the proposed estimation scheme has a relatively faster estimation time. Additionally, it is shown that the method accounts for the dead-time effect as well.

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Parameter Identification of Permanent Magnet Synchronous Motor Based on Improved MRAS Algorithm

Liu

Quan²,

Zhao³

2023

J. Phys.: Conf. Ser.

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During the operation of a permanent magnet synchronous motor, the electrical parameters of the motor will be affected by the temperature and the saturation of the magnetic chain, which will lead to a decrease in the effectiveness of the control algorithm for motor control. In this paper, we address the problem that the traditional MRAS algorithm has under-ranked equations when identifying motor parameters, and the three parameter identification values are coupled with each other and affect each other, which leads to scattered identification results and slow identification speed. In this paper, we propose a segmentation identification algorithm that combines the traditional MRAS algorithm with the recursive least squares method. In this paper, the inductance value is first identified by the recursive least squares method, and then the inductance value is substituted into the MRAS to identify the stator resistance and rotor magnetic chain, which effectively solves the problems of equation under-ranking and slow identification speed in the traditional MRAS, thus improving the stability of the permanent magnet synchronous motor operation.

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Offline inductance identification of PMSM with adaptive inverter nonlinearity compensation

Cited by 13 publications

References 29 publications

Current Prediction Error Reduction Method of Predictive Current Control for Permanent Magnet Synchronous Motors

Current Prediction Error Reduction Method of Predictive Current Control for Permanent Magnet Synchronous Motors

A Simple Method for Stator Inductance and Resistance Estimation for PMSM at Standstill

Parameter Identification of Permanent Magnet Synchronous Motor Based on Improved MRAS Algorithm

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