Abstract
Gas-insulated switchgear (GIS) disconnectors play an important role in power systems, and their mechanical condition is critical for operational reliability. However, existing diagnostic methods frequently fail to accurately identify critical operational states during switching processes, such as the initial contact and separation points. This study proposes a novel diagnostic approach that combines arc signal detection and motor power analysis to address these limitations. The arc signal captured by a Rogowski coil provides precise timing information for contact engagement and separation. An adaptive wavelet packet decomposition method, guided by power spectral entropy, is used to effectively denoise the arc signal, allowing for precise extraction of ignition and extinction points. By synchronising the arc signal with motor power data, the proposed method makes it easier to diagnose common mechanical faults such as refusal to close, refusal to open, insufficient overtravel, and insufficient opening distance. Experimental validation confirms the method’s robustness and reliability, emphasising its potential for improving GIS disconnector fault diagnosis and system reliability.
Data availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
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Song He: Conceptualization, data curation, formal analysis, methodology, validation, visualization, writing—original draft. Jiangjun Ruan: Supervision. Yufei Liu: Validation. Feiyue Yan: Validation. Rui Luo: Validation.
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He, S., Ruan, J., Liu, Y. et al. A novel arc signal and motor power synchronization-based method for precise contact fault diagnosis of GIS disconnector. Sci Rep (2026). https://doi.org/10.1038/s41598-026-44930-6
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DOI: https://doi.org/10.1038/s41598-026-44930-6
