Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2022 9th International Conference on Condition Monitoring and Diagnosis, CMD 2022 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 289-292 |
| Seitenumfang | 4 |
| ISBN (elektronisch) | 9784886864314 |
| ISBN (Print) | 978-1-6654-7015-5 |
| Publikationsstatus | Veröffentlicht - 2022 |
| Veranstaltung | 9th International Conference on Condition Monitoring and Diagnosis, CMD 2022 - Virtual, Online, Japan Dauer: 13 Nov. 2022 → 18 Nov. 2022 |
Abstract
An artificial neural network is created to estimate the technical age of power transformers based on the measurement results of standard oil tests and dissolved gas analysis. The prediction accuracy for ±5 years is 75 percent indicates the overlapping between the technical and calendrical age of the power transformers.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
- Ingenieurwesen (insg.)
- Sicherheit, Risiko, Zuverlässigkeit und Qualität
- Werkstoffwissenschaften (insg.)
- Elektronische, optische und magnetische Materialien
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- BibTex
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2022 9th International Conference on Condition Monitoring and Diagnosis, CMD 2022. Institute of Electrical and Electronics Engineers Inc., 2022. S. 289-292.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Neural Network for Estimating the Technical Age of Power Transformers
AU - Schnittker, Henning
AU - Werle, Peter
AU - Münster, Tobias
AU - Lottner, Matthias
PY - 2022
Y1 - 2022
N2 - An artificial neural network is created to estimate the technical age of power transformers based on the measurement results of standard oil tests and dissolved gas analysis. The prediction accuracy for ±5 years is 75 percent indicates the overlapping between the technical and calendrical age of the power transformers.
AB - An artificial neural network is created to estimate the technical age of power transformers based on the measurement results of standard oil tests and dissolved gas analysis. The prediction accuracy for ±5 years is 75 percent indicates the overlapping between the technical and calendrical age of the power transformers.
KW - artificial neural network
KW - Asset management
KW - dissolved gas analysis
KW - health index
KW - power transformer
KW - standard oil test
UR - http://www.scopus.com/inward/record.url?scp=85146611156&partnerID=8YFLogxK
U2 - 10.23919/CMD54214.2022.9991307
DO - 10.23919/CMD54214.2022.9991307
M3 - Conference contribution
AN - SCOPUS:85146611156
SN - 978-1-6654-7015-5
SP - 289
EP - 292
BT - 2022 9th International Conference on Condition Monitoring and Diagnosis, CMD 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Condition Monitoring and Diagnosis, CMD 2022
Y2 - 13 November 2022 through 18 November 2022
ER -