More Robust and Reliable Optimized Energy Conversion Facilitated through Electric Machines, Power Electronics and Drives, and Their Control: State-of-the-Art and Trends

Bramerdorfer, Gerd; Lei, Gang; Cavagnino, Andrea; Zhang, Yongchang; Sykulski, J.K.; Lowther, D.A.

doi:10.1109/tec.2020.3013190

Cited by 32 publications

(10 citation statements)

References 201 publications

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“…To compare the performance of different methods, some benchmark works have been developed in International Compumag Society (ICS), like TEAM problems [6,11,12,[16][17][18]. Some papers reviewed popular design optimization methods of several types of electromagnetic devices, such as electrical machines [4][5][6]13,14], and antennas [7]. This section presents an overview of the recent advances in design optimization of electromagnetic devices, including multi-objective, multidisciplinary, multilevel, topology, and robust optimization methods.…”

Section: An Overview Of Recent Advances In Design Optimization Of Elementioning

confidence: 99%

“…From the perspective of industrial production, the performance of a good design of an electromagnetic device, like a transformer, should not be sensitive to those uncertainties. To achieve this goal, reliabilitybased and robust optimizations have attracted significant research attention recently, especially when the industrial big data about the material and manufacturing process are considered [6,[10][11][12][13][14][15]. These topics are of ever-growing significance for smart manufacturing in the context of industry 4.0.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to the current state of the art, this review has three new contributions. First, this review covers more types of electromagnetic devices, instead of specific types, like electrical machines and antennas [4,5,7,13,14]. Second, besides the review of recent developments in typical optimization methods, such as multi-objective and multidisciplinary optimizations, this work reviews the topology optimization, fuzzy optimization, and new optimization strategies like space-reduction strategy.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

Lei

Bramerdorfer

et al. 2021

Applied Sciences

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This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices.

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Section: An Overview Of Recent Advances In Design Optimization Of Elementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

Lei

Bramerdorfer

et al. 2021

Applied Sciences

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“…However, to verify the results of the optimization sequence a proper measurement proceeding is required. The results are affected by manufacturing tolerances and deviation of material properties, which can be considered with robust optimization techniques [5]- [7]. Typically, a standard electrical machine test setup includes a load machine, a torque transducer and a power analyzer [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Model Validation of Synchronous Motors With a New Standstill Measurement Technique

et al. 2023

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Permanent magnet synchronous machines (PMSM), synchronous reluctance machines (SynRM), and electrically excited synchronous machines (EESM), are used widely in the industry and transportation sectors. Often their rotors are equipped with conducting parts like permanent magnets, thick rotor laminations, real damper windings, a conducting sleeve or even have a solid rotor core. Such conducting elements do not allow applying standard AC-supply techniques for the inductance measurements due to the alternating flux induced by eddy currents. We propose a new technique for inductance measurement at standstill with a quasi-static voltage supply, which mitigates the eddy current phenomenon. The inductance is measured at several rotor positions over the complete electrical cycle, which allows to proceed with a harmonic spectrum analysis of the inductance variation. With this information, it is possible to estimate motor performance characteristics, such as optimum torque, voltage control, power factor, and torque ripple. The new technique is experimentally validated on a 12 kW axially laminated anisotropic solid-rotor high-speed SynRM. In principle, the method also suits to PMSMs, EESMs, and other possible synchronous machines (not verified in this paper). Experimental validation with a solid-rotor SynRM shows a good correspondence with the simulation. Furthermore, the torque computed using the measured inductance is very similar to the experimental one confirming the effectiveness of the method.

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“…The robust design and optimization, aiming to obtain a scheme that is not sensitive to the uncertainties, are becoming more and more popular [16][17][18]. In the robust optimization, the design performance considering the uncertainties are usually calculated and adopted as extra objectives or constraints [19].…”

Section: Introductionmentioning

confidence: 99%

A Permanent Magnet Assembling Approach to Mitigate the Cogging Torque for Permanent Magnet Machines Considering Manufacturing Uncertainties

et al. 2022

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Conventionally, the small mean and variance of peak-to-peak cogging torque of permanent magnet (PM) machines considering manufacturing uncertainties can be achieved by a robust design or by reducing the uncertainties range. However, the consequent compromise of other design objectives or the increase in the manufacturing costs are frequently inevitable. In this paper, the combination sequence of the uncertainties is highlighted and implemented to achieve a stable performance even for a non-robust design without increasing the cost too much. A PM assembling approach is proposed to mitigate the influences of PM uncertainties on cogging torque by means of combining the uncertainties in a particular sequence, where the effects of uncertainties would counteract each other. Both the singular type of PM uncertainties and the combined ones are considered in the assembling approach. Furthermore, the proposed approach is verified by comparing the cogging torque performance with PM randomly assembled models, where several hundreds of models featuring different uncertainties are calculated through the finite element method. The proposed approach is discussed, particularly for the application of mass production and a small amount of prototypes. Two prototypes with different PM assembling sequences are fabricated and further verify the effectiveness of the proposed PM assembling approach.

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More Robust and Reliable Optimized Energy Conversion Facilitated through Electric Machines, Power Electronics and Drives, and Their Control: State-of-the-Art and Trends

Cited by 32 publications

References 201 publications

Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

Model Validation of Synchronous Motors With a New Standstill Measurement Technique

A Permanent Magnet Assembling Approach to Mitigate the Cogging Torque for Permanent Magnet Machines Considering Manufacturing Uncertainties

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