Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/121803
Title: Signal De-Noising of Quench Detection by Real-Time Wavelet Analysis Algorithm for HTS Coil and Magnet
Authors: Yuxiang Liao;Yuejin Tang;Jing Shi;Li Ren;Jingdong Li;Zhuang Wang;Zhong Xia
Year: 2017
Publisher: IEEE
Abstract: For automatic quench protection system, correction of quench judgment is important to the prolonged stable operation of superconducting devices. In poor electromagnetic environment, quench detection based on electrometric method is easily interfered by noise and severely disrupted quench signal may cause wrong quench determination and malfunction of quench protection, thereby resulting in unnecessary loss. One reason causing noise is current fluctuation and the noise is hard to be filtered by conventional filter circuit. In order to restrain noise and reduce false action of quench detection, this paper proposes a real-time wavelet analysis algorithm based on field programmable gate array platform. Experiment of filtering noise coupled whit quench signal of a HTS coil shows that the real-time wavelet analysis algorithm can filter noise out effectively. This method can be applied for filtering noise of quench detection for HTS coil and magnet.
URI: http://localhost/handle/Hannan/121803
volume: 27
issue: 4
More Information: 1,
5
Appears in Collections:2017

Files in This Item:
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7797475.pdf1.01 MBAdobe PDF
Title: Signal De-Noising of Quench Detection by Real-Time Wavelet Analysis Algorithm for HTS Coil and Magnet
Authors: Yuxiang Liao;Yuejin Tang;Jing Shi;Li Ren;Jingdong Li;Zhuang Wang;Zhong Xia
Year: 2017
Publisher: IEEE
Abstract: For automatic quench protection system, correction of quench judgment is important to the prolonged stable operation of superconducting devices. In poor electromagnetic environment, quench detection based on electrometric method is easily interfered by noise and severely disrupted quench signal may cause wrong quench determination and malfunction of quench protection, thereby resulting in unnecessary loss. One reason causing noise is current fluctuation and the noise is hard to be filtered by conventional filter circuit. In order to restrain noise and reduce false action of quench detection, this paper proposes a real-time wavelet analysis algorithm based on field programmable gate array platform. Experiment of filtering noise coupled whit quench signal of a HTS coil shows that the real-time wavelet analysis algorithm can filter noise out effectively. This method can be applied for filtering noise of quench detection for HTS coil and magnet.
URI: http://localhost/handle/Hannan/121803
volume: 27
issue: 4
More Information: 1,
5
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7797475.pdf1.01 MBAdobe PDF
Title: Signal De-Noising of Quench Detection by Real-Time Wavelet Analysis Algorithm for HTS Coil and Magnet
Authors: Yuxiang Liao;Yuejin Tang;Jing Shi;Li Ren;Jingdong Li;Zhuang Wang;Zhong Xia
Year: 2017
Publisher: IEEE
Abstract: For automatic quench protection system, correction of quench judgment is important to the prolonged stable operation of superconducting devices. In poor electromagnetic environment, quench detection based on electrometric method is easily interfered by noise and severely disrupted quench signal may cause wrong quench determination and malfunction of quench protection, thereby resulting in unnecessary loss. One reason causing noise is current fluctuation and the noise is hard to be filtered by conventional filter circuit. In order to restrain noise and reduce false action of quench detection, this paper proposes a real-time wavelet analysis algorithm based on field programmable gate array platform. Experiment of filtering noise coupled whit quench signal of a HTS coil shows that the real-time wavelet analysis algorithm can filter noise out effectively. This method can be applied for filtering noise of quench detection for HTS coil and magnet.
URI: http://localhost/handle/Hannan/121803
volume: 27
issue: 4
More Information: 1,
5
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7797475.pdf1.01 MBAdobe PDF