A Transformer Winding Deformation Fault Diagnosis Method Based on Improved Directed Acyclic Graph

Author(s):  
Miao Zhao ◽  
Dai Wan ◽  
Hengyi Zhou ◽  
Jibin Fang ◽  
Tao Peng ◽  
...  
2014 ◽  
Vol 666 ◽  
pp. 149-153 ◽  
Author(s):  
Hong Zhong Ma ◽  
Ning Jiang ◽  
Chun Ning Wang ◽  
Zhi Hui Geng

according to analysing the generation principle of transformer winding deformation and its impact on the vibration signal, and make a large number of trial, it can be found in addition to the fundamental frequency component that can reflect the failure, the new characteristic frequency which conclude 50Hz frequency component and some of its harmonic components, the harmonic components of the fundamental frequency can also reflect the failure. Transformer winding deformation fault diagnosis method is proposed based on the relationship between the characteristic frequency, it can not only diagnose whether the failure inside the transformer windings, but also determine the type of fault. In order to verify the proposed method, deformation fault is set to the actual transformer winding. After de-noising, discounted processing, the acquisition monitoring points of vibration signal is used by the proposed method, and the actual transformer is diagnosed, The diagnostic result is same with actual failure. It is shown that the proposed diagnostic method is accurate and feasible.


2019 ◽  
Vol 2019 (16) ◽  
pp. 2096-2101
Author(s):  
Zhang Bin ◽  
Zhao Dan ◽  
Wang Feiming ◽  
Shi Kejian ◽  
Zhao Zhenyang

2012 ◽  
Vol 490-495 ◽  
pp. 1486-1490
Author(s):  
Su Xiang Qian ◽  
Qi Du ◽  
Xiao Jun Gu ◽  
Jia You Song

When different types and extent of faults occurs at transformer winding, the energy of the signals in different frequency bands will change. So it can calculate the characteristic energy of different response signals at different states to determine whether the winding failure. The transformer fault diagnosis method based on FRA and characteristic energy extraction is presented, the maximum cross-correlation between the signal and the wavelet was taken as criterion to choose the wavelet. The method is verified by test. Experimental results show that this method can diagnose winding fault type and extent effectively, and improve the sensitivity of fault diagnosis.


Sign in / Sign up

Export Citation Format

Share Document