Study on the diagnostic technology of isolating switch defects
A high-voltage isolating switch stands as the most significant switchgear in a substation, exerting a substantial influence on its safe operation. Over numerous years, accidents involving high-voltage isolating switches due to mechanical faults have been prevalent and have incurred severe consequences. Currently, there exists a plethora of diagnostic methods globally for insulator cracks. However, for issues such as rusting, stuck components, and failure to reach designated positions, there are fewer diagnostic methods available. The prevalent approach prioritizes preventive measures and empirical diagnosis. Therefore, it is imperative to conduct research on mechanical state monitoring of high-voltage isolating switches to promptly identify mechanical faults and prevent accidents from causing greater losses. The direction of power grid operations focuses on equipment status early warning and fault prediction through intelligent inspection. Isolation switches, being the most numerous and having the highest fault rate among high-voltage equipment, still rely on outdated traditional maintenance methods, such as sensory judgment and disintegration maintenance, for mechanical fault diagnosis. Hence, a convenient and reliable method for diagnosing mechanical faults in isolating switches is proposed, providing a reference for maintaining two typical mechanical faults in disconnecting switches. This method is widely applicable to motor-driven high-voltage disconnecting switches and can offer guidance for substation operation and maintenance management.
Authors
- Wenmin Man (ORCID: https://orcid.org/0000-0003-3004-5464)
- T. Luo (ORCID: https://orcid.org/0000-0001-5139-5784)
- Rui Luo (ORCID: https://orcid.org/0000-0001-8347-7725)
- Zhiang Wu (ORCID: https://orcid.org/0000-0002-0591-1861)
- Zijian Chen (ORCID: https://orcid.org/0000-0002-2338-7258)
- Yilong Xiao
- Boping Liu (ORCID: https://orcid.org/0000-0001-6791-8922)
- Ziwei Qiu
- Shunli Cheng
Institutions
- Wuhan University of Technology (CN)
- China Southern Power Grid (China) (CN)
Publication Details
- Published
- 2025-06-30
- DOI
- https://doi.org/10.1117/12.3067213
- Primary Topic
- Industrial Vision Systems and Defect Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00