Study on diagnosis of power transformer winding deformation through vibration signal

2016 ◽  
Author(s):  
Fan Zhang ◽  
Shengchang Ji ◽  
Yuhang Shi ◽  
Yanjie Cui ◽  
Lingyu Zhu ◽  
...  
Keyword(s):  
Power Transformer ◽  
Vibration Signal ◽  
Transformer Winding ◽  
Winding Deformation

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.

Research on the Vibration Condition On-Line Monitoring System for EHV Power Transformer

2015 ◽  
Vol 734 ◽  
pp. 675-679
Author(s):  
Wan Qing Li ◽  
Wei Wang ◽  
Le Ting Lin ◽  
Bei Min Xie ◽  
Ming Chao Xia ◽  
...  
Keyword(s):  
Data Analysis ◽  
Monitoring System ◽  
Power Transformer ◽  
Vibration Signal ◽  
Acceleration Signal ◽  
Transformer Winding ◽  
On Line ◽  
Characteristic Values ◽  
Harmonic Components ◽  
Extra High Voltage

The paper introduces a design scheme for the Extra High Voltage (EHV) transformer condition on-line monitoring system, which is based on the collection and analysis of the transformer winding and core vibration signals. This system is composed of vibration acceleration signal sensors and the signal analyzing computer where the collected vibration signal is saved and processed. The analyzing computer can accomplish the missions of data acquisition control, data analysis and the historical data query. Vibration characteristic values of transformer winding and core include peak to peak value, spectrum, kurtosis, and the amplitude 100Hz component and its higher harmonic components. They are extracted, and the characteristic trend curves are drawn by data analysis, so that EHV transformer on-line monitoring and fault diagnosis are accomplished.

scholarly journals Novel Transformer Fault Identification Optimization Method Based on Mathematical Statistics

Mathematics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 288 ◽  
Author(s):  
Zhanlong Zhang ◽  
Yongye Wu ◽  
Ruixuan Zhang ◽  
Peiyu Jiang ◽  
Guohua Liu ◽  
...  
Keyword(s):  
Fault Detection ◽  
Vibration Analysis ◽  
Power Transformer ◽  
Short Circuit ◽  
Vibration Signal ◽  
Fault Identification ◽  
Iron Core ◽  
Analysis Method ◽  
Transformer Winding ◽  
Transformer Fault

Most power transformer faults are caused by iron core and winding faults. At present, the method that is most widely used for transformer iron core and winding faults identification is the vibration analysis method. The vibration analysis method generally determines the degree of fault by analyzing the energy spectrum of the transformer vibration signal. However, the noise reduction step in this method is complicated and costly, and the effect of denoising needs to be further improved to make the fault identification results more accurate. In addition, it is difficult to perform an accurate determination of the early mild failure of the transformer due to the effect of noise on the results. This paper presents a novel mathematical statistics method based on the vibration signal to optimize the vibration analysis method for the short-circuit failure of the transformer winding. The proposed method was used for linear analysis of the transformer vibration signal with different degrees of short-circuit failure of the transformer winding. By comparing the slope value of the transformer vibration signal cumulative probability distribution curve and analyzing the energy spectrum of the signal, the degree of short-circuit failure of the transformer winding was identified quickly and accurately. This method also simplified the signal denoising process in transformer fault detection, improved the accuracy of fault detection, reduced the time of fault detection, and provided good predictability for early mild faults of the transformer, thereby reducing the hidden hazards of operating the power transformer. The proposed optimization procedure offers a new research idea in transformer fault identification.

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