Discharge fault diagnosis model for transformers based on free characteristic gas

Zhengqi Li; Weigen Chen; Tianyan Jiang; Zhixian Zhang

doi:10.1117/12.2611057

14 October 2021 Discharge fault diagnosis model for transformers based on free characteristic gas

Zhengqi Li, Weigen Chen, Tianyan Jiang, Zhixian Zhang

Proceedings Volume 11930, International Conference on Mechanical Engineering, Measurement Control, and Instrumentation; 119302W (2021) https://doi.org/10.1117/12.2611057
Event: International Conference on Mechanical Engineering, Measurement Control, and Instrumentation (MEMCI 2021), 2021, Guangzhou, China

Abstract

Transformers are the critical equipment in power systems. During the operation of transformers, there is a risk of discharging faults such as arc discharge. High-energy discharge fault occurs with a large amount of free gas, which causes transformer fire, explosion, and other accidents. Based on this, this paper designs a discharge fault simulation device in transformer oil. It conducts breakdown discharge, spark discharge, and arc discharge tests to collect free gas generated when a discharge fault occurs. This paper selects four typical characteristic gases to form characteristic gas histograms. It uses a support vector machine (SVM) and ant colony optimization support vector machine (ASVM) to establish a fault recognition model to compare and diagnose the characteristic parameters of the map. The results show that the ant colony optimization support vector function can effectively identify the free distinct gas map of the transformer. This model is suitable for diagnosing and analyzing discharging faults of transformers based on free characteristic gases.

Citation Download Citation

Zhengqi Li, Weigen Chen, Tianyan Jiang, and Zhixian Zhang "Discharge fault diagnosis model for transformers based on free characteristic gas", Proc. SPIE 11930, International Conference on Mechanical Engineering, Measurement Control, and Instrumentation, 119302W (14 October 2021); https://doi.org/10.1117/12.2611057

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