Synergistic Optimization Design Method to Improve the Overload Capacity and Efficiency of Canned Permanent Magnet Synchronous Motor for Vacuum Pump

Li, Ming; Chen, Zilin; Mu, Haiqi; Lun, Shuxian

doi:10.3390/electronics12224631

Cited by 2 publications

(2 citation statements)

References 21 publications

(22 reference statements)

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“…Currently, prevalent modeling approaches for motor optimization encompass both model-driven and data-driven methodologies. Model-driven modeling relies on fundamental electromagnetic principles to construct motor models [3][4][5][6], while data-driven methods leverage observed motor performance data and employ statistical and machine learning techniques to unveil system patterns and relationships [7][8][9]. Employing data-driven methodologies to establish mathematical models for motors offers advantages such as reduced computational complexity and robust generalization capabilities [5,10,11].…”

Section: Introductionmentioning

confidence: 99%

“…Model-driven modeling relies on fundamental electromagnetic principles to construct motor models [3][4][5][6], while data-driven methods leverage observed motor performance data and employ statistical and machine learning techniques to unveil system patterns and relationships [7][8][9]. Employing data-driven methodologies to establish mathematical models for motors offers advantages such as reduced computational complexity and robust generalization capabilities [5,10,11]. Moreover, data-driven techniques harness machine learning methodologies to better capture the intricacies of complex systems [12].…”

Section: Introductionmentioning

confidence: 99%

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A Data-Driven Motor Optimization Method Based on Support Vector Regression—Multi-Objective, Multivariate, and with a Limited Sample Size

Li,

Hou

et al. 2024

Electronics

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The increasing demand for sustainable development and energy efficiency underscores the importance of optimizing motors in driving the upgrade of energy structures. This paper studies a data-driven approach for the multi-objective optimization of motors designed for scenarios involving multiple variables, objectives, and limited sample sizes and validates its efficacy. Initially, sensitivity analysis is employed to identify potentially influential variables, thus selecting key design parameters. Subsequently, Latin hypercube sampling (LHS) is utilized to select experimental points, ensuring the coverage of the modeled test points across the experimental space to enhance fitting accuracy. Finally, the support vector regression (SVR) algorithm is employed to fit the objective function, in conjunction with multi-objective particle swarm optimization (MOPSO) for solution derivation. The presented method is used to optimize the efficiency, average output torque, and induced electromotive force harmonic distortion rate of a permanent magnet synchronous motor (PMSM). The results show an improvement of approximately 6.80% in average output torque and a significant decrease of about 59.5% in the induced electromotive force harmonic distortion rate, with minimal impact on efficiency. This study offers a pathway for enhancing motor performance, holding practical significance.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Data-Driven Motor Optimization Method Based on Support Vector Regression—Multi-Objective, Multivariate, and with a Limited Sample Size

Li,

Hou

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

The impact of stator module gaps on the performance of canned permanent magnet synchronous motors with segmented stators

Ming,

Jixin,

Guanghui

et al. 2024

Preprint

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The segmented stator structure is applied to the canned permanent magnet synchronous motor (CPMSM) can improve the performance of the motor. However, the influence of the stator module gap on the CPMSM performance is not clear. In this paper, an iterative calculation method for the electromagnetic performance of the motor is proposed. Using this method, the influence of stator module gap on the electromagnetic performance of the CPMSM with segmented stators is analyzed under the conditions of constant current and constant torque. The calculation results are compared with the finite element calculation results. The results show that under constant current, the increase of the stator module gap leads to the decrease of the loss, torque, efficiency and power factor. Under the condition of constant torque, with the increase of the stator module gap, the loss and power factor of the CPMSM are reduced, but the efficiency of the CPMSM is gradually improving. Finally, a prototype is made and a test platform is built. The test results verify the correctness and effectiveness of the proposed method and results. This study not only enriches the theoretical knowledge of the CPMSM with segmented stators, but also has practical significance for helping and guiding the design of the CPMSM.

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Synergistic Optimization Design Method to Improve the Overload Capacity and Efficiency of Canned Permanent Magnet Synchronous Motor for Vacuum Pump

Cited by 2 publications

References 21 publications

A Data-Driven Motor Optimization Method Based on Support Vector Regression—Multi-Objective, Multivariate, and with a Limited Sample Size

A Data-Driven Motor Optimization Method Based on Support Vector Regression—Multi-Objective, Multivariate, and with a Limited Sample Size

The impact of stator module gaps on the performance of canned permanent magnet synchronous motors with segmented stators

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