A fibre optic and machine learning based health monitoring system for superconducting power applications in modern supergrids

The concerns for climate change and the transition to renewables is increasing the relevance of superconducting applications: High Temperature Superconductor (HTS) cables for efficient power transmission, superconducting magnets for nuclear fusion, superconducting fault current limiters (SFCLs) for HVDC grid protection and superconducting compact generators for wind turbines, to name a few. However, to tap into the full potential of HTS applications, the main problem of hotspots needs to be addressed. Hotspots are localized points of heating, which can arise in HTS devices mainly due to the intrinsic inhomogeneity of critical current along the superconductor length. The motivation behind this thesis, was to develop a health monitoring technique for a SFCL device under the European union project FastGrid. This device required a fast hotspot detection time of 10 ms in order to timely open the DC breaker in order to protect the device. During the course of this thesis an extremely fast and economical optical fibre sensing based hotspot detection technique has been developed and patented. The technique uses the Mach-Zehnder Interferometer (MZI) and is an efficient and economical way to detect hotspots in HTS applications. Due to the MZI sensitivity being a composite of strain sensitive and temperature sensitive contributions, the MZI gives an instantaneous response to a hotspot (within 10 ms), because of the quick strain transfer to the optical fibre. The research carried out under this thesis has involved experimentation on HTS tapes (10 cm to 1 m in length). The technique has also been integrated and tested with a SFCL pancake prototype with long lengths of conductor (12 m and 17 m). The thesis has also focused on finite element modelling to better understand the MZI sensitivity. The experimentation and modelling together, has enabled a better understanding of the MZI response to hotspots and the challenges of using MZI for HTS health monitoring. Due to the extremely high sensitivity of the MZI , the MZI output signal can also manifest the environmental noise caused by mechanical vibrations, bubbling in the cryostat and temperature variations, along with the response to heating in the sample. This presents the problems of false alarms and indiscernible response to hotspots. The work done for this thesis has also focused substantially on finding a suitable data analysis technique to supplement the MZI method. A discrete wavelet transform (DWT) based feature extraction together with a machine learning based classification for reliable quench detection has been developed in the course of this work with very encouraging results . This is an important development for the MZI technique as it significantly impacts and improves the MZI reliability and practicality for HTS applications. Additionally, this is also of substantial importance to the progress of HTS applications in the industry, especially the power sector which continues to evolve with the shif