Details
| Original language | English |
|---|---|
| Title of host publication | 2021 IEEE International Electric Machines and Drives Conference (IEMDC) |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (electronic) | 9781665405102 |
| ISBN (print) | 978-1-6654-4628-0 |
| Publication status | Published - 2021 |
| Event | 2021 IEEE International Electric Machines and Drives Conference, IEMDC 2021 - Hartford, United States Duration: 17 May 2021 → 20 May 2021 |
Abstract
An automated conductor placement algorithm for random-wound windings is presented in this work. It allows for a reliable generation of conductor-filled stator slots of random-wound windings based on given input data. The conductors' placement is decided by coordinates which are randomly drawn from probability distribution functions. This novel approach shows promising results in the prediction of the high-frequency characteristic of electrical machines compared to sectional views of a stator specimen. The algorithm itself is generalized and therefore adaptive to every stator slot shape and conductor size. Since the paper presents a closed mathematical description, the algorithm can be implemented via the automation interfaces of finite element software. Such an interface is used in order to generate conductor distributions to validate different probability distribution functions against a stator specimen and to predict the winding-stator capacitance. The continuous uniform probability distribution has been identified as the most promising approach.
Keywords
- common-mode, electrical machine, FEM automation, HF modeling, probability density function, winding-stator capacitance
ASJC Scopus subject areas
- Energy(all)
- Energy Engineering and Power Technology
- Engineering(all)
- Electrical and Electronic Engineering
- Engineering(all)
- Mechanical Engineering
- Engineering(all)
- Safety, Risk, Reliability and Quality
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2021 IEEE International Electric Machines and Drives Conference (IEMDC). Institute of Electrical and Electronics Engineers Inc., 2021. 9449557.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - High-Frequency Analysis of Electrical Machines Using Probability Density Functions for an Automated Conductor Placement of Random-Wound Windings
AU - Hoffmann, A.
AU - Knebusch, B.
AU - Stockbrugger, J. O.
AU - Dittmann, J.
AU - Ponick, B.
N1 - Funding Information: Supported by: Federal Ministry of Economic Affairs and Energy on the basis of a decision by the German Bundestag
PY - 2021
Y1 - 2021
N2 - An automated conductor placement algorithm for random-wound windings is presented in this work. It allows for a reliable generation of conductor-filled stator slots of random-wound windings based on given input data. The conductors' placement is decided by coordinates which are randomly drawn from probability distribution functions. This novel approach shows promising results in the prediction of the high-frequency characteristic of electrical machines compared to sectional views of a stator specimen. The algorithm itself is generalized and therefore adaptive to every stator slot shape and conductor size. Since the paper presents a closed mathematical description, the algorithm can be implemented via the automation interfaces of finite element software. Such an interface is used in order to generate conductor distributions to validate different probability distribution functions against a stator specimen and to predict the winding-stator capacitance. The continuous uniform probability distribution has been identified as the most promising approach.
AB - An automated conductor placement algorithm for random-wound windings is presented in this work. It allows for a reliable generation of conductor-filled stator slots of random-wound windings based on given input data. The conductors' placement is decided by coordinates which are randomly drawn from probability distribution functions. This novel approach shows promising results in the prediction of the high-frequency characteristic of electrical machines compared to sectional views of a stator specimen. The algorithm itself is generalized and therefore adaptive to every stator slot shape and conductor size. Since the paper presents a closed mathematical description, the algorithm can be implemented via the automation interfaces of finite element software. Such an interface is used in order to generate conductor distributions to validate different probability distribution functions against a stator specimen and to predict the winding-stator capacitance. The continuous uniform probability distribution has been identified as the most promising approach.
KW - common-mode
KW - electrical machine
KW - FEM automation
KW - HF modeling
KW - probability density function
KW - winding-stator capacitance
UR - http://www.scopus.com/inward/record.url?scp=85112854930&partnerID=8YFLogxK
U2 - 10.1109/IEMDC47953.2021.9449557
DO - 10.1109/IEMDC47953.2021.9449557
M3 - Conference contribution
AN - SCOPUS:85112854930
SN - 978-1-6654-4628-0
BT - 2021 IEEE International Electric Machines and Drives Conference (IEMDC)
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Electric Machines and Drives Conference, IEMDC 2021
Y2 - 17 May 2021 through 20 May 2021
ER -