Remote fault diagnosis system for power transformer insulation based on RIA model

2016 ◽  
Author(s):  
Mingjun Liu ◽  
Peng Wang ◽  
Qiukuan Zhou ◽  
Chen Kang
Keyword(s):  
Fault Diagnosis ◽  
Power Transformer ◽  
Diagnosis System ◽  
Fault Diagnosis System ◽  
Transformer Insulation ◽  
Remote Fault Diagnosis

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.

Remote Fault Diagnosis System of Ultrahigh Speed Grinding Based on Multi-Agent

2009 ◽  
Vol 76-78 ◽  
pp. 67-71
Author(s):  
Wan Shan Wang ◽  
Tian Biao Yu
Keyword(s):  
Fault Diagnosis ◽  
Theoretical Research ◽  
Case Based Reasoning ◽  
Diagnosis System ◽  
Knowledge Reasoning ◽  
Diagnosis Method ◽  
Multi Agent ◽  
Fault Diagnosis System ◽  
Set Up ◽  
Remote Fault Diagnosis

A remote fault diagnosis method for ultrahigh speeding grinding based on multi-agent is presented. The general faults of ultrahigh speed grinding are analyzed and diagnosis model based on multi-agent is established, the dialogue layer, problem decomposition layer, control layer and problem solving layer in the process of diagnosis are studied and the knowledge reasoning model of fault diagnosis is set up based case-based reasoning (CBR) combining rule-based reasoning (RBR). Based on theoretical research, a remote fault diagnosis system of ultrahigh speed grinding is developed. Results of the system running prove the theory is correctness and the technology is feasibility.

scholarly journals Design of Remote Fault Diagnosis System for Medium and Small-sized Enterprises

2011 ◽  
Vol 24 ◽  
pp. 48-52
Author(s):  
Jianwei Li ◽  
Zhihua Yuan ◽  
Hui Tian ◽  
Aiqin Liang
Keyword(s):  
Fault Diagnosis ◽  
Diagnosis System ◽  
Fault Diagnosis System ◽  
Remote Fault Diagnosis

Power Transformer Fault Diagnosis System Based on Learn++

2014 ◽  
Vol 602-605 ◽  
pp. 2053-2056
Author(s):  
Bin Chen ◽  
Bo Meng
Keyword(s):  
Fault Diagnosis ◽  
Incremental Learning ◽  
Traditional Method ◽  
Power Transformer ◽  
New Method ◽  
Diagnosis System ◽  
Learning Machines ◽  
Fault Diagnosis System ◽  
Transformer Fault

Aiming at the shortages of traditional method for power transformer fault diagnosis, the ensemble idea and incremental learning idea are used for better performance. The SVM is selected to establish the fault diagnosis models as sub learning machines. And then, the Learn++ algorithm is used to aggregate the sub learning machines. The new with new method will ensure the accuracy of fault diagnosis, and will update online. The experiments demonstrate that the performance of power transformer fault diagnosis system based on Learn++ is the best.

Study on XML-Based Apriori Algorithm Applying to Remote Diagnosis System for Hoist

2011 ◽  
Vol 201-203 ◽  
pp. 510-513
Author(s):  
Juan Li Li ◽  
Zhao Jian Yang
Keyword(s):  
Fault Diagnosis ◽  
Apriori Algorithm ◽  
Remote Diagnosis ◽  
Diagnosis System ◽  
Operation Process ◽  
Fault Diagnosis System ◽  
Remote Fault Diagnosis

According to the data characteristics in the hoister operation process and the network requirements of fault diagnosis system, this paper proposes a XML-based apriori algorithm, and the algorithm is applied to hoist remote fault diagnosis system. The experiment result shows that applying the system can make the hoist fault diagnosis more scientific, reasonable and improve the efficiency and accuracy of diagnosis.

The Remote Fault Intelligent Diagnosis System Based on B/S Structure

2011 ◽  
Vol 328-330 ◽  
pp. 1067-1071
Author(s):  
Jia Liu ◽  
Chun Liang Zhang ◽  
Jian Li ◽  
Sen Li ◽  
Yue Hua Xiong
Keyword(s):  
Fault Diagnosis ◽  
Markov Models ◽  
Support Vector ◽  
Main Function ◽  
Test Bed ◽  
Diagnosis System ◽  
Intelligent Diagnosis ◽  
Database Technology ◽  
Fault Diagnosis System ◽  
Remote Fault Diagnosis

The feasibility and superiority of the remote fault diagnosis system based on B/S structure is analyzed in this paper. The B/S structure is introduced and compared with C/S structure briefly. The paper summarize frame and main function module of the remote fault diagnosis system and introduce its key technology, such as data acquisition technology, data transmission technology between server and client, intelligent diagnosis technology, database technology etc. The hybrid model of support vector machine (SVM) and hidden markov models(HMM) is used as a intelligent diagnosis method of the system, and a new design which could improve the integrity and privacy of the system database data is applied. According to the diagnostic results to all kinds of simulated faults in the Bently vibration test bed, it shows the system is not only stable, reliable and high accuracy, but also has a certain application value to engineering.

Sign in / Sign up

Export Citation Format

Share Document