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 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

scholarly journals Study on the Fault Diagnosis Method of Scraper Conveyor Gear under Time-Varying Load Condition

2020 ◽  
Vol 10 (15) ◽  
pp. 5053
Author(s):  
Shuanfeng Zhao ◽  
Pengfei Wang ◽  
Shijun Li
Keyword(s):  
Fault Diagnosis ◽  
Load Condition ◽  
Analytical Signal ◽  
Vibration Signal ◽  
Frequency Component ◽  
Time Varying ◽  
Fault Strength ◽  
Scraper Conveyor ◽  
Gear Fault ◽  
Diagnosis Method

Vibration signal is often used in traditional gear fault diagnosis techniques. However, the working face of the scraper conveyor is narrow, harsh and easily explosive, so it is inconvenient to obtain vibration signals by installing sensors. Motor current signature analysis (MCSA) is a fault-diagnosis method without sensor installation, which is easier to realize in the mine. Therefore, a fault diagnosis method for local gear fault, which is based on bispectral analysis (BA) of analytical signal envelope obtained by processing a stator current under time-varying load condition, is proposed in our paper. In this method, the fault frequency component is enhanced by eliminating the interference of fundamental frequency and coal flow impact. Then, the enhanced fault frequency component is extracted by BA, and a quantitative analysis of the fault strength under time-varying load is carried out from the perspective of energy. Finally, the proposed method is verified on the number HB-kpl-75 scraper conveyor reducer, and the results show that this method can successfully diagnose the failure of the scraper conveyor gear under time-varying load conditions.

Research on Indirect Fault Diagnosis Method of Top Gearbox on Blast Furnace

2013 ◽  
Vol 823 ◽  
pp. 9-12 ◽  
Author(s):  
Xiang Li Zhao ◽  
Li Xin Gao ◽  
Jian Feng Li
Keyword(s):  
Fault Diagnosis ◽  
Characteristic Frequency ◽  
High Speed ◽  
Fault Location ◽  
Low Frequency ◽  
Vibration Signal ◽  
Heavy Duty ◽  
Low Speed ◽  
Transmission Chain ◽  
Diagnosis Method

Aiming at the difficulties in diagnosis for low speed and heavy duty components of furnace top gearbox, an indirect diagnosis method for vibration signal is proposed in this subject, through which the vibration features of high speed rotating parts that near input end of gearbox is effectively utilized and analyzed for fault judgment of low speed components and a useful methodology is also given for fault diagnosis of both furnace top gearbox and low speed and heavy duty equipments. Since the identification for all faults and accurate fault location cannot be realized by using the existing diagnosis methods, a method of vibration analysis for fault diagnosis to furnace top gearbox is presented to realize accurate judgment and fault location. It can be found out that if near the basic frequency and double frequency of characteristic frequency of high speed components of upper gearbox, there were frequency spacing of fault characteristic frequency of low speed components of subordinate transmission chain apparently showing up, which also happened in low frequency range after demodulation, then the fault location can be determined to the low speed parts of subordinate transmission chain.

Power Transformer Fault Diagnosis Based on Vibration Correlation Analysis

2014 ◽  
Author(s):  
Kaixing Hong ◽  
Hai Huang
Keyword(s):  
Fault Diagnosis ◽  
Correlation Analysis ◽  
Fundamental Frequency ◽  
Vibration Analysis ◽  
Laboratory Tests ◽  
Power Transformer ◽  
Assessment Model ◽  
Vibration Method ◽  
Proposed Model ◽  
Transformer Fault

In this paper, a condition assessment model using vibration method is presented to diagnose winding structure conditions. The principle of the model is based on the vibration correlation. In the model, the fundamental frequency vibration analysis is used to separate the winding vibration from the tank vibration. Then, a health parameter is proposed through the vibration correlation analysis. During the laboratory tests, the model is validated on a test transformer, and manmade deformations are provoked in a special winding to compare the vibrations under different conditions. The results show that the proposed model has the ability to assess winding conditions.

Research on Roller Bearing with Fault Diagnosis Method Based on EMD and BP Neural Network

2014 ◽  
Vol 1014 ◽  
pp. 501-504 ◽  
Author(s):  
Shu Guo ◽  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Kun Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Classification Performance ◽  
Outer Race ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Main Fault

In order to discover the fault with roller bearing in time, a new fault diagnosis method based on Empirical mode decomposition (EMD) and BP neural network is put forward in the paper. First, we get the fault signal through experiments. Then we use EMD to decompose the vibration signal into a series of single signals. We can extract main fault information from the single signals. The kurtosis coefficient of the single signals forms a feature vector which is used as the input data of the BP neural network. The trained BP neural network can be used for fault identification. Through analyzing, BP neural network can distinguish the fault into normal state, inner race fault, outer race fault. The results show that this method can gain very stable classification performance and good computational efficiency.

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