Self-Commissioning of Permanent Magnet Synchronous Machine Drives at Standstill Considering Inverter Nonlinearities

Wang, Gaolin; Qu, Lizhi; Zhan, Hanlin; Xu, Jiazhu; Ding, Li; Zhang, Guoqiang; Xu, Dianguo

doi:10.1109/tpel.2014.2306734

Cited by 108 publications

(54 citation statements)

References 38 publications

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“…Inverter non linearity due to dead time, introduction of delays due to filtering, not accounting stator winding resistance and cross saturation effects results in significant position estimation error [14]. Many solutions are proposed and validated for compensating the dead time effect and other inverter nonlinearities [15]- [16]. Work performed in [17] adds cross saturation component to the input of position observer, which helps in improving the Position estimation accuracy due to cross saturation.…”

Section: Introductionmentioning

confidence: 99%

Novel Compensation Method to Reduce Rotor Position Estimation Error and Torque Reduction in Signal Injection Based PMSM Drives

Allampalli¹,

Mangsuli²,

Chatterjee³

2017

IJPEDS

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High frequency signal injection techniques are widely used to extract rotor position information from low speed to stand still. Accuracy of estimated rotor position is decreased when stator winding resistance is neglected. Position estimation error also results in output Torque reduction. Parasitic resistance of stator winding causes significant position estimation error and Torque reduction, if not compensated. Signal injection techniques developed in the literature does not provide detailed analysis and compensation methods to improve rotor position estimation of PMS Motors, where stator winding resistance cannot be neglected. This work analyzes the stator winding resistance effect on position estimation accuracy and proposes novel compensation technique to reduce the position estimation error and torque reduction introduced by stator winding resistance. Prototype hardware of a self-sensing PMSM drive is developed. The effectiveness of the proposed method is verified with the MATLAB/Simulink simulations and experimental results on a prototype self-sensing PMSM drive.

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Section: Introductionmentioning

confidence: 99%

Novel Compensation Method to Reduce Rotor Position Estimation Error and Torque Reduction in Signal Injection Based PMSM Drives

Allampalli¹,

Mangsuli²,

Chatterjee³

2017

IJPEDS

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show abstract

“…However, with the increasing complexity of operation conditions, these methods may not always work well. For example, in [2] it was proposed to use self-commissioning technique to estimate PMSM parameters under standstill before the start of machine. However, this method cannot estimate the permanent magnet when the machine is not running.…”

mentioning

confidence: 99%

Global Identification of Electrical and Mechanical Parameters in PMSM Drive Based on Dynamic Self-Learning PSO

Liu

Wei

et al. 2018

IEEE Trans. Power Electron.

120

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A global parameter estimation method for a PMSM drive system is proposed, where the electrical parameters, mechanical parameters and voltage-source-inverter (VSI) nonlinearity are regarded as a whole and parameter estimation is formulated as a single parameter optimization model. A dynamic learning estimator is proposed for tracking the electrical parameters, mechanical parameters and VSI of PMSM drive by using dynamic self learning particle swarm optimization (DSLPSO). In DSLPSO, a novel movement modification equation with dynamic exemplar learning strategy is designed to ensure its diversity and achieve a reasonable tradeoff between the exploitation and exploration during the search process.Moreover, a nonlinear multi-scale based interactive learning operator is introduced for accelerating the convergence speed of the Pbest particles; meanwhile a dynamic opposition-based learning (OBL) strategy is designed to facilitate the gBest particle to explore a potentially better region. The proposed algorithm is applied to parameter estimation for a PMSM drive system. The results show that the proposed method has better performance in tracking the variation of electrical parameters, and estimating the immeasurable mechanical parameters and the VSI disturbance voltage simultaneously.

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“…INTRODUCTION 1 RECENTLY, permanent magnet synchronous machines (PMSMs) are widely used in high-performance applications such as industrial robots, servo drive, wind power generation, and machine tools due to their overall good performance [1]- [2]. The design of the control system of such a machines crucially involves the choice of many key machine parameters such as winding resistance, dq-axis inductances, and rotor PM flux linkage [3] [4].…”

mentioning

confidence: 99%

“…In practical engineering, parameter optimization using numerical methods is an ideal technology for directly estimating the needed parameters based on regular measurable data instead of using additional measurement instruments [7]. Existing research mainly focused on online estimation algorithms including self-commissioning technique [2] ,extended Kalman filter (EKF) [9], model reference adaptive system (MRAS) [10], recursive least-squares (RLS) [3] [11], adaptive observer [12], and artificial neural networks (ANN) [10] [11]. However, with the increasing complexity of operation conditions, these methods may not always work well.…”

mentioning

confidence: 99%

“…However, with the increasing complexity of operation conditions, these methods may not always work well. For example, in [2] a self-commissioning technique was proposed to estimate PMSM parameters under standstill .However, it cannot estimate the permanent magnet when the machine is at standstill state. In [9], an EKF algorithm was proposed to estimate the rotor speed and position of PMSM, but it may be difficult for real applications as the algorithm is sensitive to noise.…”

mentioning

confidence: 99%

See 1 more Smart Citation

GPU Implementation of DPSO-RE Algorithm for Parameters Identification of Surface PMSM Considering VSI Nonlinearity

Liu

Wei

Zhong

et al. 2017

IEEE J. Emerg. Sel. Topics Power Electron.

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In this study, an accurate parameter estimation model of surface permanent magnet synchronous machines (SPMSM) is established by taking into account voltage-source-inverter (VSI) nonlinearity. A fast dynamic particle swarm optimization (DPSO) algorithm combined with a receptor editing (RE) strategy is proposed to explore the optimal values of parameter estimations. This combination provides an accelerated implementation on graphics processing unit (GPU), and the proposed method is therefore referred to as G-DPSO-RE. In G-DPSO-RE, a dynamic labor division strategy is incorporated into the swarms according to

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Self-Commissioning of Permanent Magnet Synchronous Machine Drives at Standstill Considering Inverter Nonlinearities

Cited by 108 publications

References 38 publications

Novel Compensation Method to Reduce Rotor Position Estimation Error and Torque Reduction in Signal Injection Based PMSM Drives

Novel Compensation Method to Reduce Rotor Position Estimation Error and Torque Reduction in Signal Injection Based PMSM Drives

Global Identification of Electrical and Mechanical Parameters in PMSM Drive Based on Dynamic Self-Learning PSO

GPU Implementation of DPSO-RE Algorithm for Parameters Identification of Surface PMSM Considering VSI Nonlinearity

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