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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| Published in: | IEEE transactions on applied superconductivity Vol. 33; no. 5; pp. 1 - 6 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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New York
IEEE
01.08.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1051-8223, 1558-2515 |
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| Abstract | 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 detection that accounts for system sensitivity and is able to continuously manage the response to disturbances. In this paper, we propose an integrated solution for fault severity diagnosis and anomaly detection colorred with AI based reflectometry. Anomalies such as the quench phenomenon can be detected through the anomaly score based on the reconstruction error calculated through the proposed autoencoder(AE) method. After anomaly detection, a fault classification algorithm based on the convolutional neural network (CNN) using a 2D image of a converted reflected signal through the proposed image processing was conducted. Based on the signal acquired from the <inline-formula><tex-math notation="LaTeX">7 m</tex-math></inline-formula> length 1st generation 22.9 kV/50 MVA HTS cable, PSCAD simulation was utilized to construct a long-distance line model and verified the performance of the proposed algorithm. |
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| AbstractList | 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 detection that accounts for system sensitivity and is able to continuously manage the response to disturbances. In this paper, we propose an integrated solution for fault severity diagnosis and anomaly detection colorredwith AI based reflectometry. Anomalies such as the quench phenomenon can be detected through the anomaly score based on the reconstruction error calculated through the proposed autoencoder(AE) method. After anomaly detection, a fault classification algorithm based on the convolutional neural network (CNN) using a 2D image of a converted reflected signal through the proposed image processing was conducted. Based on the signal acquired from the 7 [Formula Omitted] length 1st generation 22.9 kV/50 MVA HTS cable, PSCAD simulation was utilized to construct a long-distance line model and verified the performance of the proposed algorithm. 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 detection that accounts for system sensitivity and is able to continuously manage the response to disturbances. In this paper, we propose an integrated solution for fault severity diagnosis and anomaly detection colorred with AI based reflectometry. Anomalies such as the quench phenomenon can be detected through the anomaly score based on the reconstruction error calculated through the proposed autoencoder(AE) method. After anomaly detection, a fault classification algorithm based on the convolutional neural network (CNN) using a 2D image of a converted reflected signal through the proposed image processing was conducted. Based on the signal acquired from the <inline-formula><tex-math notation="LaTeX">7 m</tex-math></inline-formula> length 1st generation 22.9 kV/50 MVA HTS cable, PSCAD simulation was utilized to construct a long-distance line model and verified the performance of the proposed algorithm. |
| Author | Sim, Yeon-Sub Chang, Seung Jin |
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| SubjectTerms | 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 |
| Title | Time-frequency Domain Monitoring Method for the Fault of HTS HVDC Systems Based on AI Classifiers |
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