Litcius/Paper detail

A Wireless Rectifier for Inductively Energizing High Direct-Current High-Temperature Superconducting Magnets

Jianzhao Geng, Rodney A. Badcock, Chris W. Bumby

2020IEEE Transactions on Industrial Electronics32 citationsDOI

Abstract

High-temperature superconducting (HTS) magnets have been widely used in various applications due to their excellent performance. One long-lasting problem, however, is that they have to be powered by electronic power supplies via a pair of thick current leads, which go through room temperature environment into a cryogenic environment. The considerable heat load generated by these resistive current leads at a cryogenic temperature substantially limits the operating current and the energy density of the magnet. In this article, we report a novel mechanism of inductively energizing closed-loop HTS dc magnets. This exploits a newly discovered effect, which appears within a superconducting loop when a global screening current interacts with local screening current. This results in a dc voltage across the superconductor, which enables an alternating superconducting current to be rectified in order to energize a superconducting magnet. Based on this principle, a superconducting transformer-rectifier prototype is realized and demonstrated. Test results show that the prototype can output a dc voltage of up to 25 mV and a maximum direct current over 500 A. It is envisaged that this work will enable future HTS dc magnets to be operated in a closed cryogenic environment, eliminating the need for electronic power supplies and bulky current leads. This would greatly reduce the footprint and power demand of HTS magnet systems and unlock many new opportunities for the applications of this technology.

Topics & Concepts

MagnetSuperconducting magnetMaterials scienceElectrical engineeringRectifier (neural networks)Superconducting magnetic energy storageSuperconducting electric machineTransformerVoltageComputer scienceEngineeringMachine learningArtificial neural networkRecurrent neural networkStochastic neural networkPhysics of Superconductivity and MagnetismSuperconducting Materials and ApplicationsFrequency Control in Power Systems