scholarly journals Analysis of Total Hydrocarbon Exceeding Standard in Oil Chromatogram of a 500kV Main Transformer

2021 ◽  
Vol 2136 (1) ◽  
pp. 012008
Author(s):  
Linan Wang ◽  
Yunfei Ma ◽  
Jinhua Han ◽  
Yusheng Zheng ◽  
Xiaopeng Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Chromatographic Analysis ◽  
Current Data ◽  
Submersible Pump ◽  
Chromatographic Data ◽  
Total Hydrocarbon ◽  
Transformer Fault ◽  
Total Hydrocarbons

Abstract Oil chromatographic analysis is widely used in transformer fault diagnosis However, the problem of excessive total hydrocarbons in the oil chromatogram caused by the failure of the transformer submersible pump is different from the failure of the transformer body. Technicians need to be able to accurately identify these two types of failure. For this problem, this article proposes a method of judging by manually starting and stopping the submersible pump and monitoring the change law of the transformer chromatographic data. This article first finds out suspicious submersible pumps through current data. Subsequently, the operator starts and stops the submersible pump and monitors the change law of the transformer chromatographic data. From this, the correlation between the start or stop of the submersible pump and the chromatographic data was found. Finally, the effectiveness of this method is verified by the submersible pump disassembly inspection and simulated live test.

scholarly journals Oil Chromatographic Analysis and Fault Diagnosis Case Analysis for Transformers

2020 ◽  
Vol 145 ◽  
pp. 02067
Author(s):  
Yanping Li ◽  
Yong Li ◽  
Yunqi Li
Keyword(s):  
Fault Diagnosis ◽  
Chromatographic Analysis ◽  
Case Analysis ◽  
Transformer Oil ◽  
Ratio Method ◽  
Multiple Characteristic ◽  
Fault Type ◽  
On Site Inspection ◽  
Transformer Fault ◽  
Transformer Core

The production principle of characteristic gas in transformer oil and its corresponding transformer fault type are discussed in this paper. Aiming at the fault of multiple characteristic gases exceeding the standard in a 110 kV transformer oil, this paper analyzed the fault reasons according to the characteristic gas method and the three-ratio method, and combined with relevant tests, it was judged that the transformer core multiple grounding fault caused the fault. The on-site inspection verifies the correctness of the failure analysis results and the corresponding measures were adopted to eliminate the transformer faults.

Application of the Improved Three-Ratio Method in Chromatographic Analysis of Locomotive Transformer Oil

2014 ◽  
Vol 1030-1032 ◽  
pp. 29-33 ◽  
Author(s):  
Xiao Qin Jiang ◽  
Yan Gong ◽  
Sa Han ◽  
Ke Zhou
Keyword(s):  
Fault Diagnosis ◽  
Chromatographic Analysis ◽  
Transformer Oil ◽  
Ratio Method ◽  
Transformer Fault

The chromatographic analysis of gas dissolved in oil can monitor transformer operation state at any time.It will help to find out transformer latent fault.The chromatogram analysis of locomotive transformer oil mainly adopts the improved three-ratio method.This paper introduces the improved three-ratio method and chromatogram analysis steps,through the concrete example,shows that the improved three-ratio method for transformer fault diagnosis result is basically matched with the actual fault reason,and summarizes attentions.

Improved PSO-BP neural network for power transformer fault diagnosis

2010 ◽  
Vol 30 (3) ◽  
pp. 783-785 ◽  
Author(s):  
Zhong-yang XIONG ◽  
Qing-bo YANG ◽  
Yu-fang ZHANG
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Power Transformer ◽  
Transformer Fault ◽  
Improved Pso

scholarly journals Research on transformer fault diagnosis based on GWO-RF algorithm

2021 ◽  
Vol 1952 (3) ◽  
pp. 032054
Author(s):  
Tianbing Wang ◽  
Lei Zhang ◽  
Yufeng Wu
Keyword(s):  
Fault Diagnosis ◽  
Transformer Fault

scholarly journals Power transformer fault diagnosis system based on Internet of Things

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Guoshi Wang ◽  
Ying Liu ◽  
Xiaowen Chen ◽  
Qing Yan ◽  
Haibin Sui ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Internet Of Things ◽  
Power Transformer ◽  
Fault System ◽  
Ratio Method ◽  
Diagnosis System ◽  
Network Training ◽  
Fault Diagnosis System ◽  
Training Error ◽  
Transformer Fault

Abstract Transformer is the most important equipment in the power system. The research and development of fault diagnosis technology for Internet of Things equipment can effectively detect the operation status of equipment and eliminate hidden faults in time, which is conducive to reducing the incidence of accidents and improving people's life safety index. Objective To explore the utility of Internet of Things in power transformer fault diagnosis system. Methods A total of 30 groups of transformer fault samples were selected, and 10 groups were randomly selected for network training, and the rest samples were used for testing. The matter-element extension mathematical model of power transformer fault diagnosis was established, and the correlation function was improved according to the characteristics of three ratio method. Each group of power transformer was diagnosed for four months continuously, and the monitoring data and diagnosis were recorded and analyzed result. GPRS communication network is used to complete the communication between data acquisition terminal and monitoring terminal. According to the parameters of the database, the working state of the equipment is set, and various sensors are controlled by the instrument driver module to complete the diagnosis of transformer fault system. Results The detection success rate of the power transformer fault diagnosis system model established in this paper is as high as 95.6%, the training error is less than 0.0001, and it can correctly identify the fault types of the non training samples. It can be seen that the technical support of the Internet of Things is helpful to the upgrading and maintenance of the power transformer fault diagnosis system.

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 Intelligent Diagnosis Technology of Transformer Fault

2013 ◽  
Vol 385-386 ◽  
pp. 589-592
Author(s):  
Hong Qi Wu ◽  
Xiao Bin Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Intelligent Diagnosis ◽  
Rbf Network ◽  
Working Principle ◽  
Research On Application ◽  
Transformer Fault

In order to improve the diagnosis rates of transformer fault, a research on application of RBF neural network is carried out. The structure and working principle of radial basis function (RBF) neural network are analyzed and a three layer RBF network is also designed for transformer fault diagnosis. It is proved by MATLAB experiment that RBF neural network is a strong classifier which is used to diagnose transformer fault effectively.

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