Design strategy and simulation of medium-frequency transformers for a three-phase dual active bridge

Kauder, Tobias; Hameyer, Kay; Belgrand, Thierry

doi:10.1109/iecon.2018.8592689

Cited by 4 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We extracted a thermal coefficient of resistance ( α ) of 0.00076 K −1 . 28 This value is smaller than that of aluminum ( α Al = 0.004308 K −1 ) 43 and suggests the presence of metallic and V o 2+ contributions during the RS events. 17,44 To confirm the existence of the metallic part in the CF, we investigated the likelihood of Al ions diffusing from the TE into the Hf-hybrid matrix, which can be expressed through the following reaction: 45 6Al + 3HfO 2 → Hf 3 Al 2 + 2Al 2 O 3 .…”

Section: Resultsmentioning

confidence: 95%

A non-invasive approach to the resistive switching physical model of ultra-thin organic–inorganic dielectric-based ReRAMs

Martinez,

Cho,

Kim

2023

Nanoscale

View full text Add to dashboard Cite

show abstract

Section: Resultsmentioning

confidence: 95%

A non-invasive approach to the resistive switching physical model of ultra-thin organic–inorganic dielectric-based ReRAMs

Martinez,

Cho,

Kim

2023

Nanoscale

View full text Add to dashboard Cite

show abstract

“…A well-known deterministic method is Pattern Search (PS) [1,2]. Among the most commonly used stochastic methods are evolutionary algorithms such as Genetic Algorithms (GA) [3,4] or Evolution Strategy (ES) [5], Particle Swarm Optimizations [6], and Simulated Annealing (SA) [4]. Both deterministic and stochastic optimization methods are applied in the field of electric machine optimization.…”

Section: Introductionmentioning

confidence: 99%

Multi-Stage Optimization of Induction Machines Using Methods for Model and Parameter Selection

2021

Self Cite

View full text Add to dashboard Cite

Optimization methods are increasingly used for the design process of electrical machines. The quality of the optimization result and the necessary simulation effort depend on the optimization methods, machine models and optimization parameters used. This paper presents a multi-stage optimization environment for the design optimization of induction machines. It uses the strategies of simulated annealing, evolution strategy and pattern search. Artificial neural networks are used to reduce the solution effort of the optimization. The selection of the electromagnetic machine model is made in each optimization stage using a methodical model selection approach. The selection of the optimization parameters is realized by a methodical parameter selection approach. The optimization environment is applied on the basis of an optimization for the design of an electric traction machine using the example of an induction machine and its suitability for the design of a machine is verified by a comparison with a reference machine.

show abstract

Medium-frequency power transformer using GOES for a three-phase dual active bridge

Kauder

Belgrand²,

Lemaître³

et al. 2020

Journal of Magnetism and Magnetic Materials

View full text Add to dashboard Cite

Design strategy and simulation of medium-frequency transformers for a three-phase dual active bridge

Cited by 4 publications

References 15 publications

A non-invasive approach to the resistive switching physical model of ultra-thin organic–inorganic dielectric-based ReRAMs

A non-invasive approach to the resistive switching physical model of ultra-thin organic–inorganic dielectric-based ReRAMs

Multi-Stage Optimization of Induction Machines Using Methods for Model and Parameter Selection

Medium-frequency power transformer using GOES for a three-phase dual active bridge

Contact Info

Product

Resources

About