Compensation of permanent magnet motors torque ripple by means of current supply waveshapes control determined by finite element method

Clénet, S.; Lefèvre, Yvan; Sadowski, N.; Astier, Stéphan; Lajoie‐Mazenc, M.

doi:10.1109/20.250806

Cited by 43 publications

(11 citation statements)

References 3 publications

(2 reference statements)

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“…The work presented in [14] optimizes the currents only for harmonics of rank 5 and 7 while the method presented in [15], [16] gives the optimal currents by calculating a pseudo-inverse matrix containing the harmonics of the back-EMF. A formula was proposed in [17] which works in the a-b-c reference frame. In this method the homopolar current is null and moreover, the loss by Joule effect is not optimal.…”

Section: Nomenclature Imentioning

confidence: 99%

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A Self-Learning Solution for Torque Ripple Reduction for Nonsinusoidal Permanent-Magnet Motor Drives Based on Artificial Neural Networks

Flieller

Nguyen

Wira

et al. 2014

IEEE Trans. Ind. Electron.

157

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Abstract-This paper presents an original method, based on artificial neural networks, to reduce the torque ripple in a permanent-magnet non-sinusoidal synchronous motor. Solutions for calculating optimal currents are deduced from geometrical considerations and without a calculation step which is generally based on the Lagrange optimization. These optimal currents are obtained from two hyperplanes. The study takes into account the presence of harmonics in the back-EMF and the cogging torque. New control schemes are thus proposed to derive the optimal stator currents giving exactly the desired electromagnetic torque (or speed) and minimizing the ohmic losses. Either the torque or the speed control scheme, both integrate two neural blocks, one dedicated for optimal currents calculation and the other to ensure the generation of these currents via a voltage source inverter. Simulation and experimental results from a laboratory prototype are shown to confirm the validity of the proposed neural approach.

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Section: Nomenclature Imentioning

confidence: 99%

“…This section has developed the expressions of optimal currents which are already presented in various works by using different approaches. Indeed, i opt−0 is identical to the solution given in [17]. Based on the Lagrange's optimization, the currents i opt−1 and i opt−2 are also obtained in [19], [20], [35].…”

Section: Geometrical Interpretation Of the Threementioning

confidence: 99%

A Self-Learning Solution for Torque Ripple Reduction for Nonsinusoidal Permanent-Magnet Motor Drives Based on Artificial Neural Networks

Flieller

Nguyen

Wira

et al. 2014

IEEE Trans. Ind. Electron.

157

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“…Along with the recent interest in the PMSM by both military and industry came the development of new control techniques for current and direct torque control [2,3], efficiency maximization [4], and torque ripple minimization [5,6,7,8]. Each of these control techniques require accurate knowledge of the rotor angle in order to be effective.…”

Section: Statement Of Problemmentioning

confidence: 99%

Sensorless Electric Drive for Integral Horsepower Permanent Magnet Synchronous Motor

Batzel¹

2001

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Public moorina burden for this collection of information r» estimated to average t hour per response PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)Applied Research Laboratory P.O. Box 30 State College, PA 16804 SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)Naval Undersea Warfare Center PERFORMING ORGANIZATION REPORT NUMBERTR 01-005 SPONSOR/MONITOR'S ACRONYM(S) NUWC SPONSORING/MONITORING AGENCY REPORT NUMBER DISTRIBUTION AVAILABILITY STATEMENTApproved for Public Release SUPPLEMENTARY NOTES14. ABSTRACT The permanent magnet synchronous motor (PMSM) is increasingly playing an important role m electric propulsion systems due to its many advantages over competing technologies. For successful implementation of the PMSM, rotor portion and speed information are required. A resolver or encoder attached to the shaft of the machine usually provides this information. Many applications, however, cannot tolerate the use of the position sensor due to space and weight limitations, reliability concemsTor packaging issues. Thus, there has been an intense interest in the development of a so-called position sensorless drive, where the PMSM stator itself is used as the rotor position sensor. In this work, a previously developed sensorless electric drive technique fl is applied to a high power permanent magnet machine. The sensorless drive hardware implementation is presented in detail. A set of experiments is used to quantify the performance of the sensorless drive approach under a wide variety of operating conditions, and implementation issues and results are discussed The developed sensorless electric drive serves as a technology demonstrator for future naval applications. SUBJECT TERMS 814-863-7338Standard Form 298 (Rev. 8-98) Prescribed by ANSI-Std Z39-18 AbstractThe permanent magnet synchronous motor (PMSM) is increasingly playing an important role in electric propulsion systems due to its many advantages over competing technologies. For successful implementation of the PMSM, rotor position and speed information are required. A resolver or encoder attached to the shaft of the machine usually provides this information. Many applications, however, cannot tolerate the use of the position sensor due to space and weight limitations, reliability concerns, or packaging issues. Thus, there has been an intense interest in the development of a so-called position sensorless drive, where the PMSM stator itself is used as the rotor position sensor. In this work, a previously developed sensorless electric drive technique [1] is applied to a high power permanent magnet machine. The sensorless drive hardware implementation is presented in detail. A set of experiments is used to quantify the performance of the sensorless drive approach under a wide variety of operating conditions, and implementation issues and results are discussed. The developed sensorless electric drive serves as a technology demonstrator for future naval applications.

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“…Torque ripples may degrade speed and position control and cause large acoustic noises and vibrations. In previous researches, the optimal current command for the torque smoothness are achieved by developing torque controllers with mathematical model which has spatially dependent machine parameters has been proposed (3)- (5) . However, this technique requires amount of off line data for look up tables.…”

Section: Introductionmentioning

confidence: 99%

Advanced Torque and Current Control Techniques for PMSMs with a Real-time Simulator Installed Behavior Motor Model

Tanabe

Akatsu

2016

IEEJ Journal IA

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This paper presents advanced torque and current control techniques for Permanent Magnet Synchronous Motors (PMSMs) with a real-time simulator which has a nonlinear motor model. This model is called "Behavior motor model", and it is developed based on finite element analysis results considering non-linear characteristics that include spatial harmonics and magnetic saturation. The real-time simulator is implemented within a circuit simulator that couples the behavior motor model with switching circuits in real-time, and it can be applied to torque and current control as an advanced controller. In this paper, the torque and current control of a PMSM with the proposed system is presented. Effectiveness of this technique is verified by performing simulations and experiments.

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Compensation of permanent magnet motors torque ripple by means of current supply waveshapes control determined by finite element method

Abstract: OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 19544To link to this article :

Cited by 43 publications

References 3 publications

A Self-Learning Solution for Torque Ripple Reduction for Nonsinusoidal Permanent-Magnet Motor Drives Based on Artificial Neural Networks

A Self-Learning Solution for Torque Ripple Reduction for Nonsinusoidal Permanent-Magnet Motor Drives Based on Artificial Neural Networks

Sensorless Electric Drive for Integral Horsepower Permanent Magnet Synchronous Motor

Advanced Torque and Current Control Techniques for PMSMs with a Real-time Simulator Installed Behavior Motor Model

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