Time-frequency Domain Monitoring Method for the Fault of HTS HVDC Systems Based on AI Classifiers

Since high-temperature superconductor (HTS) based HVDC systems are affected by temperature and cooling system performance, noise, the monitoring system which can classify the temporal disturbance and anomalies must be developed to ensure stable system operation. To solve this, it needs anomaly detec...

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Vydané v:	IEEE transactions on applied superconductivity Ročník 33; číslo 5; s. 1 - 6
Hlavní autori:	Sim, Yeon-Sub, Chang, Seung Jin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.08.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Anomalies Anomaly detection Artificial neural networks CNN Convolutional neural networks Cooling systems Fault severity classifier Feature extraction High temperature superconductors HTS-based HVDC HVDC transmission Image acquisition Image processing Image reconstruction Noise monitoring Power cables Quench Superconducting cables Time-frequency analysis
ISSN:	1051-8223, 1558-2515
On-line prístup:	Získať plný text
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