Dry-type air-core reactors (DARs) often have inter-turn short circuit (ITSC) faults. However, traditional fault detection methods for DARs generally demonstrate poor timeliness and low sensitivity, and few methods combine intelligent algorithms for objective and accurate diagnosis. Therefore, a novel online diagnosis method for ITSC faults was proposed. First, the "field-circuit" coupling 2D model of reactors was established to simulate the impact of ITSC faults on the characteristics of various state parameters; accordingly, the Lissajous graph was introduced to characterize the short circuit fault. Then, the variation law of the Lissajous graph under different inter-turn fault layers, turns, and degrees was explored to verify the feasibilities of the proposed method. Finally, to achieve rapid diagnosis and fulfill the requirements of edge computing, a lightweight network model named MobileNetV3-Small was used and combined as a classifier to achieve accurate diagnosis of ITSC faults. The results robustly validate that the Lissajous graphical method can significantly reflect ITSC faults through observing the variation in the graph and feature parameters. Furthermore, the MobileNetV3-Small model achieves a diagnostic accuracy of up to 95.91%, which can further enhance the diagnostic accuracy of the ITSC fault degree. [ABSTRACT FROM AUTHOR]

Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)