Paper
10 November 2022 Fault simulation and identification of capacitive casings based on fault tree theory
He Liu, Daiyong Yang, Lixin Jiao, Junbo Liu, Shouxue Li, Yi Li
Author Affiliations +
Proceedings Volume 12301, 6th International Conference on Mechatronics and Intelligent Robotics (ICMIR2022); 123012P (2022) https://doi.org/10.1117/12.2644657
Event: 6th International Conference on Mechatronics and Intelligent Robotics, 2022, Kunming, China
Abstract
The safe and stable operation of the power system is related to the quality and efficiency of national production. With the mature development of the detection technology of large power equipment, online monitoring has gradually become an important means of detecting the insulation status of equipment. Casings, as the core component of power transformer, bears the electromagnetic environment of high voltage and strong electric field in operation, and has multiple interfaces (oil-air interface). The complex working environment of casing leads to the occurrence of faults, so it has high engineering value to study the on-line monitoring system of Casing. Firstly, this article designed casing shrinkage ratio model, and make tail damp and metal particle discharge fault artificially. Secondly, we extract five kinds of characteristic value in the laboratory including the capacitance variation, the dielectric loss tangent, the maximum discharge amplitude, the maximum amplitude and the average discharge pulse number. At last, we used the analytic hierarchy process (AHP) to build the fault tree model for fault diagnosis and classification of two kinds of discharge.
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
He Liu, Daiyong Yang, Lixin Jiao, Junbo Liu, Shouxue Li, and Yi Li "Fault simulation and identification of capacitive casings based on fault tree theory", Proc. SPIE 12301, 6th International Conference on Mechatronics and Intelligent Robotics (ICMIR2022), 123012P (10 November 2022); https://doi.org/10.1117/12.2644657
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KEYWORDS
Capacitance

Metals

Particles

Dielectrics

Sensors

Data modeling

Transformers

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