Abstract
With the wide application of SF6 gas insulated fully enclosed switchgear (GIS), the reliability of its operation has gradually attracted the attention of power systems at home and abroad. How to dig out valuable data information through various detection methods, realize the risk assessment of insulation failure of GIS equipment, guide the work of condition-based maintenance, and find potential defects in GIS equipment in time is an urgent problem to be solved in the current power system. This paper constructs a neural network, trains the common fault types and characteristics of GIS equipment, and verifies the feasibility of the fault diagnosis method of GIS equipment based on neural network through some real cases.
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Acknowledgments
The authors thank the Project Supported by the State Grid Chongqing Electric Power Company Chongqing Electric Power Research Institute (2018 Yudian Keji 5#).
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Qi, C. et al. (2020). Vibration Fault Diagnosis of 220 kV GIS Equipment Based on Neural Network. In: Hung, J., Yen, N., Chang, JW. (eds) Frontier Computing. FC 2019. Lecture Notes in Electrical Engineering, vol 551. Springer, Singapore. https://doi.org/10.1007/978-981-15-3250-4_59
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DOI: https://doi.org/10.1007/978-981-15-3250-4_59
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