The recent adoption of high-T _c superconductor (HTS) wires for ultra-high field magnet windings provide great promise for future applications, such as high-power generators and Tokamak fusion reactors. However, an open issue with the use of HTS is the challenge of rapidly detecting a hot spot which could lead to a quench. Optical fiber sensors have been shown to be promising alternatives to the voltage-based quench detection method. In this paper, we report on ultra-long fiber Bragg gratings (ULFBG) for hotspot detection at cryogenic temperatures using a new detection algorithm. This novel sensing system is suitable for applications in which solely the occurrence of a hotspot but not its precise location is of importance. This is the case e.g. for quench detection in HTS. The developed system provides the advantages of cost reduction and faster response time compared to conventional FBGs with wavelength-division multiplexing and continuous FBGs with time-division multiplexing, respectively. We demonstrate the functionality of the system for the ULFBG with a grating length of 100 mm and 500 mm at 77 K and 90 K respectively. The ULFBG is shown to respond as fast as conventional FBG, to a hot spot as small as 1 K temperature rise. Furthermore, using the proposed signal processing algorithm, ULFBG exhibits much higher signal to noise ratio than that from the conventional FBG. It is believed that artificial intelligence based technique can assist the signal processing algorithm in detecting a small hot spot more rapidly from the big spectral data in real-time.

中文翻译：

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连续光纤布拉格光栅的评估和低温下热点检测的信号处理方法

最近采用高吨 _C用于超高场磁体绕组的超导体 (HTS) 导线为未来的应用提供了广阔的前景，例如大功率发电机和托卡马克聚变反应堆。然而，使用 HTS 的一个悬而未决的问题是快速检测可能导致淬火的热点的挑战。光纤传感器已被证明是基于电压的淬火检测方法的有希望的替代方法。在本文中，我们报告了超长光纤布拉格光栅 (ULFBG) 使用新的检测算法在低温下进行热点检测。这种新颖的传感系统适用于仅热点的出现而不是其精确位置很重要的应用。例如，高温超导中的淬火检测就是这种情况。与具有波分复用的传统 FBG 和具有时分复用的连续 FBG 相比，所开发的系统分别具有成本降低和响应时间更快的优势。我们展示了 ULFBG 系统的功能，光栅长度分别为 77 K 和 90 K，光栅长度为 100 mm 和 500 mm。ULFBG 显示与传统 FBG 一样快地响应小至 1 K 温升的热点。此外，使用所提出的信号处理算法，ULFBG 表现出比传统 FBG 更高的信噪比。人们相信，基于人工智能的技术可以帮助信号处理算法从大光谱数据中实时更快速地检测出小热点。