Research of Power Transformer Fault Diagnosis System Based on Rough Sets and Bayesian Networks

2011 ◽  
Vol 320 ◽  
pp. 524-529 ◽  
Author(s):  
Qin Li ◽  
Zhi Bin Li ◽  
Qi Zhang
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Network ◽  
Rough Sets ◽  
Power Transformer ◽  
Ratio Method ◽  
Diagnostic Model ◽  
Data Sets ◽  
Electric Equipment ◽  
Reliable Power Supply ◽  
Transformer Fault

As one of the most important electric equipment for reliable power supply, the secure operation of power transformer must be guaranteed. Three-ratio method based on the Dissolved Gases Analysis (DGA) is most widely used for transformer fault diagnosis currently. Its advantage is simple and easy to use, but its encoding is incomplete and the faults classification zone is over absolute. This paper combines rough sets and Bayesian Network. Rough sets is used to get useful characters, simplify data sets, obtain simplification rules and the minimum property sets; Bayesian Network is used to analyze the faults caused by uncertain elements in complex system. The fault diagnostic model is built by Bayesian Network Tool (BNT) in MATLAB, and the simulation result shows the validity of this method.

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.

Study on Fault Diagnosis of Power Transformer with Reduction Method of Attribute Significance

2014 ◽  
Vol 1049-1050 ◽  
pp. 665-668
Author(s):  
Hong Li Lv
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rough Sets ◽  
Power Transformer ◽  
Reduction Algorithm ◽  
Rough Sets Theory ◽  
Fault Diagnosis System ◽  
Transformer Fault ◽  
Sets Theory ◽  
Quality Diagnosis

This paper studies the power transformer fault quality diagnosis using rough sets theory and neural network. It is rough sets reduction as the pre-unit of neural network based on reduction algorithm with the attribute significance. The paper describes the reduction algorithm and implementation method detailed. Through the training and testing results with practical data, it is proved that the reduction algorithm with the attribute significance can make the number of input samples shorter, the training speed faster and the diagnostic accuracy higher. The algorithm is feasible and effective for applying to the fault diagnosis system of power transformer.

scholarly journals Power Transformer Fault Diagnosis System Based on Internet of Things

2020 ◽  
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 0.000 l, 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.

scholarly journals Power Transformer Fault Diagnosis System Based on Internet of Things

2020 ◽  
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 0.000l, 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.

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

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 Power Transformer Fault Forecast

2011 ◽  
Vol 204-210 ◽  
pp. 1553-1558
Author(s):  
Rui Rui Zheng ◽  
Ji Yin Zhao ◽  
Min Li ◽  
Bao Chun Wu
Keyword(s):  
Time Series ◽  
Fault Location ◽  
Power Transformer ◽  
Forecast Model ◽  
Ratio Method ◽  
Gray Model ◽  
Artificial Immune ◽  
Diagnosis Method ◽  
Type Data ◽  
Transformer Fault

To forecast power transformer fault, this paper proposed a integrated algorithm. Research found that discrete time series of power transformer dissolved gases concentration have 2 main types: the s type and the monotone increasing type. The gray verhulst model was chosen for forecasting the s type series, while the gray model predicted the monotone increasing type data. The two models combined a new integrated forecast model. The fault diagnosis method combines the improved three-ratio method and gray artificial immune algorithm, so it can diagnoses both single and multi power transformer faults, and give the fault location. Experiments show that the power transformer fault forecast algorithm is effective and reliable.

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