Abstract
To reduce emissions from the aviation industry and meet the targets set by different countries, research has been focused on investigating all-electric aircraft. To make this vision practical, superconducting machines are expected to power the propellers, as they are half the size and a third the weight of conventional machines. The main purpose of this paper is to do a higher-level study of a reliable holistic protection system for all-electric aircraft; that can reduce heat leakage and be able to detect faults reliably. Thus, three main protection systems were investigated; 1) cryogenic voltage source converter superconducting magnetic energy storage system (VSC-SMES), 2) cryogenic dc breaker integrated with superconducting fault current limiter (SFCL), and 3) machine learning algorithm for fault detection. By immersing the protection system at cryogenic temperature, the paper has shown that passive leakage can be eliminated, and thus more energy can be saved for the fuel cell. The paper has also demonstrated that using machine learning for the SFCL-dc-breaker system can consistently eliminate faults and protect the system.
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