scholarly journals Research on mechanical fault diagnosis method of power transformer winding

2019 ◽  
Vol 2019 (16) ◽  
pp. 2096-2101
Author(s):  
Zhang Bin ◽  
Zhao Dan ◽  
Wang Feiming ◽  
Shi Kejian ◽  
Zhao Zhenyang
Keyword(s):  
Fault Diagnosis ◽  
Power Transformer ◽  
Transformer Winding ◽  
Diagnosis Method

Improved Power Transformer Winding Deformation Fault Diagnosis Method

2014 ◽  
Vol 666 ◽  
pp. 149-153 ◽  
Author(s):  
Hong Zhong Ma ◽  
Ning Jiang ◽  
Chun Ning Wang ◽  
Zhi Hui Geng
Keyword(s):  
Fault Diagnosis ◽  
Fundamental Frequency ◽  
Characteristic Frequency ◽  
Power Transformer ◽  
Vibration Signal ◽  
Frequency Component ◽  
Transformer Winding ◽  
Diagnosis Method ◽  
Winding Deformation ◽  
Harmonic Components

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.

Transformer Fault Diagnosis with the Duval Triangle and Heuristic Techniques

2014 ◽  
Vol 535 ◽  
pp. 157-161
Author(s):  
Jeeng Min Ling ◽  
Ming Jong Lin ◽  
Chao Tang Yu
Keyword(s):  
Fault Diagnosis ◽  
Power Transformer ◽  
Gas Analysis ◽  
Transformer Oil ◽  
Dissolved Gas ◽  
Power Company ◽  
Triangle Method ◽  
Diagnosis Method ◽  
Combustible Gases ◽  
Transformer Fault

Dissolved gas analysis (DGA) is an effective tool for detecting incipient faults in power transformers. The ANSI/IEEE C57.104 standards, the most popular guides for the interpretation of gases generated in oil-immersed transformers, and the IEC-Duval triangle method are integrated to develop the proposed power transformer fault diagnosis method. The key dissolved gases, including H2, CH4, C2H2, C2H4, C2H6, and total combustible gases (TCG), suggested by ASTM D3612s instruction for DGA is investigated. The tested data of the transformer oil were taken from the substations of Taiwan Power Company. Diagnosis results with the text form called IEC-Duval triangle method show the validation and accuracy to detect the incipient fault in the power transformer.

Transformer Winding Deformation Fault Diagnose Based on FRA and Energy Feature Extraction by Wavelet Packet

2012 ◽  
Vol 490-495 ◽  
pp. 1486-1490
Author(s):  
Su Xiang Qian ◽  
Qi Du ◽  
Xiao Jun Gu ◽  
Jia You Song
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Fault Diagnose ◽  
Characteristic Energy ◽  
Fault Type ◽  
Different Types ◽  
Transformer Winding ◽  
Diagnosis Method ◽  
Different Response ◽  
Transformer Fault

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.

Research on Transformer Fault Diagnosis Method Based on Artificial Immune Network and Fuzzy C-Means Clustering Algorithm

2014 ◽  
Vol 574 ◽  
pp. 468-473 ◽  
Author(s):  
Fu Zhong Wang ◽  
Shu Min Shao ◽  
Peng Fei Dong
Keyword(s):  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Power Transformer ◽  
Immune Network ◽  
Artificial Immune ◽  
Fuzzy C Means ◽  
Artificial Immune Network ◽  
Fuzzy C Means Clustering ◽  
Diagnosis Method ◽  
Transformer Fault

The transformer is one of the indispensable equipment in transformer substation, it is of great significance for fault diagnosis. In order to accurately judge the transformer fault types, an algorithm is proposed based on artificial immune network combined with fuzzy c-means clustering to study on transformer fault samples. Focus on the introduction of data processing of transformer faults based on artificial immune network, the identification of transformer faults based on fuzzy c-means clustering, and the simulation process. The experimental results show that the proposed algorithm can classify power transformer fault types effectively, and the algorithm has a good application prospect in the transformer fault diagnosis.

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