Diagnostics of Inter-Turn Short Circuit Fault in Dry-Type Air-Core Reactor Based on Lissajous Graph and Lightweight Network Model

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.