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.

Multi-Level Fault Diagnosis of Power Transformer Based on Fusion Technology

2013 ◽  
Vol 860-863 ◽  
pp. 1925-1928
Author(s):  
Zhi Bin Li ◽  
Qi Ben Li
Keyword(s):  
Fault Diagnosis ◽  
Power Transformer ◽  
Complex Structure ◽  
Transformer Oil ◽  
Fusion Technology ◽  
Fault Type ◽  
Neural Network Algorithm ◽  
Multi Level ◽  
Transformer Fault ◽  
Source Of Information

Traditional transformer fault diagnosis based on single source of information has significant limitation in identification of transformer fault type because of power transformers complex structure and changeable operating environment. So fusion technology is introduced into the fault diagnosis of power transformer. This method divides the progress of transformer fault diagnosis into two fusion levels. The first level is to ascertain whether it is overheated or discharged by content of gases dissolved in transformer oil. The second level is to ascertain the location or cause of the fault by electric data. The intelligence algorithms which are used in these two levels are both the improved BP neural network algorithm. Finally, the effectiveness is validated by the result of practical fault diagnosis examples.

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.

Fault Diagnosis of Transformer Based on RBF Neural Network

2014 ◽  
Vol 571-572 ◽  
pp. 201-204
Author(s):  
Jian Li Yu ◽  
Zhe Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
High Energy ◽  
Ratio Method ◽  
Matlab Simulation ◽  
Medium Temperature ◽  
The Neural Network ◽  
Transformer Fault ◽  
Network Diagnosis

According to the characteristics of fault types of the transformer ,RBF neural network is used to diagnose transformer fault. The paper regards six gases as inputs of the neural network and establishes RBF neural network model which can diagnose six transformer faults: low temperature overheat, medium temperature overheat, high temperature overheat, low energy discharge, high energy discharge and partial discharge . The Matlab simulation studies show that transformer fault diagnosis model based on RBF neural network diagnosis for failure beyond the traditional three-ratio method. The rate of the transformer fault diagnosis accuracy reaches 91.67% which is also much higher than the traditional three ratio method.

Parallelization Analysis of Dissolved Gases in Transformer Oil Based on Random Forest Algorithm

2014 ◽  
Vol 519-520 ◽  
pp. 98-101
Author(s):  
De Wen Wang ◽  
Zhi Wei Sun
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Power Transformer ◽  
Gas Analysis ◽  
Transformer Oil ◽  
Dissolved Gases ◽  
Random Forest Algorithm ◽  
Dissolved Gas ◽  
Important Method ◽  
Transformer Fault

Dissolved gas analysis (DGA) in oil is an important method for transformer fault diagnosis. This paper use random forest parallelization algorithm to analysis the dissolved gases in transformer oil. This method can achieve a fast parallel fault diagnosis for power equipment. Experimental results of the diagnosis of parallelization of random forest algorithm with DGA samples show that this algorithm not only can improve the accuracy of fault diagnosis, and more appropriate for dealing with huge amounts of data, but also can meet the smart grid requirements for fast fault diagnosis for power transformer. And this result also verifies the feasibility and effectiveness of the algorithm.

scholarly journals A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4170 ◽  
Author(s):  
Bing Zeng ◽  
Jiang Guo ◽  
Wenqiang Zhu ◽  
Zhihuai Xiao ◽  
Fang Yuan ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Least Square ◽  
Ratio Method ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Local Optimum ◽  
Grey Wolf ◽  
Diagnosis Model ◽  
Transformer Fault

Dissolved gas analysis (DGA) is a widely used method for transformer internal fault diagnosis. However, the traditional DGA technology, including Key Gas method, Dornenburg ratio method, Rogers ratio method, International Electrotechnical Commission (IEC) three-ratio method, and Duval triangle method, etc., suffers from shortcomings such as coding deficiencies, excessive coding boundaries and critical value criterion defects, which affect the reliability of fault analysis. Grey wolf optimizer (GWO) is a novel swarm intelligence optimization algorithm proposed in 2014 and it is easy for the original GWO to fall into the local optimum. This paper presents a new meta-heuristic method by hybridizing GWO with differential evolution (DE) to avoid the local optimum, improve the diversity of the population and meanwhile make an appropriate compromise between exploration and exploitation. A fault diagnosis model of hybrid grey wolf optimized least square support vector machine (HGWO-LSSVM) is proposed and applied to transformer fault diagnosis with the optimal hybrid DGA feature set selected as the input of the model. The kernel principal component analysis (KPCA) is used for feature extraction, which can decrease the training time of the model. The proposed method shows high accuracy of fault diagnosis by comparing with traditional DGA methods, least square support vector machine (LSSVM), GWO-LSSVM, particle swarm optimization (PSO)-LSSVM and genetic algorithm (GA)-LSSVM. It also shows good fitness and fast convergence rate. Accuracies calculated in this paper, however, are significantly affected by the misidentifications of faults that have been made in the DGA data collected from the literature.

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 Fault Diagnosis Method Based on Gross Error Examination

2012 ◽  
Vol 482-484 ◽  
pp. 2350-2354
Author(s):  
Jie Su ◽  
Xu Guang Wang
Keyword(s):  
Fault Diagnosis ◽  
Ratio Method ◽  
Fault Probability ◽  
Gross Error ◽  
Diagnosis Method ◽  
Incipient Fault ◽  
Transformer Fault ◽  
Gas Ratio ◽  
Fault Characteristic

This paper proposes a gross error judgment criterion and diagnoses the transformer fault by integrating the gross error judgment criterion and the characteristic gas ratio method. In this way it is possible to judge whether the transformer is in face of an incipient fault by examining the gross errors of the measured values of the fault characteristic gases, at the same time the fault probability could be calculated according to the remarkable level. And then in combination with the characteristic gas ratio method, the fault category and fault cause of the transformer could be figured out. The method has been validated by an actual example of fault diagnosis.

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.

Chromatographic Analysis of Oil-Based Electrical Equipment Discharge Failure

2014 ◽  
Vol 602-605 ◽  
pp. 2953-2957
Author(s):  
Guo Bin Liu ◽  
Ning Wang ◽  
Qing Hao Wang ◽  
Tian Shu Hai ◽  
Chuan Zong Zhao ◽  
...  
Keyword(s):  
Chromatographic Analysis ◽  
Chromatographic Method ◽  
Electrical Equipment ◽  
Test Methods ◽  
Transformer Oil ◽  
Stable Operation ◽  
Safe Operation ◽  
Fault Type ◽  
Important Means ◽  
Extent Of Damage

Discharge of failure was the fault type are likely to occur in transformers, bushings, transformers, and the extent of damage to the equipment is a serious and direct impact on the stable operation of the system, first introduced the principle and gas chromatographic analysis its test methods, then gas chromatography equipment discharge failure is how to judge the conduct described. Through the analysis of transformer oil chromatographic method can be found as early as possible transformers and other equipment inside the existence of latent failures, thus chromatography is to oversee and guarantee the safe operation of an important means of transformer.

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