I am leading activities for the European Union project FastGrid, within the Applied Superconductivity Group at EPFL. My research is multi-disciplinary, combining engineering, physics, optics and machine learning to increase the feasibility of High Temperature Superconductor power applications for use in modern supergrids.
I obtained my undergraduate degree in Electrical Engineering from Lahore University of Management and Sciences (LUMS), Pakistan. I then obtained a full scholarship to pursue Master’s at the University of Nottingham, UK. I graduated in December 2017 from the University of Nottingham, UK with a Master’s degree in Power Electronics and Drives.
My first research experience was as a research intern at EPFL after being selected at the Summer@EPFL program in Switzerland. Following that I did on research focusing on power quality and power factor correction at LUMS. Now I am working as a PhD student at EPFL in the Applied Superconductivity Group on the Fastgrid project. I am leading activities for the European Union project FastGrid, within the Applied Superconductivity Group at EPFL. My research is multi-disciplinary, combining engineering, physics, optics and machine learning to increase the feasibility of High Temperature Superconductor power applications for use in modern supergrids.
The motivation behind my research is to develop a health monitoring system that can be used to protect superconducting power applications like fault current limiters, HTS cables, nuclear fusion magnets that are becoming increasingly relevant with the shift to renewable energy. During the course of my work 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. The research carried out under this thesis has involved experimentation on HTS tapes (10cm to 1m in length). The technique has also been integrated and tested with a SFCL pancake prototype with long lengths of conductor. The thesis has also focused on finite element modelling to better understand the MZI sensitivity . My research has also been focused substantially on finding a suitable data anaylsis technique to supplement the MZI method. A discrete wavelet transform 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.