Online monitoring and fault diagnosis system of Power Transformer

2010 ◽  
Author(s):  
Weixuan Li ◽  
Zixiang Xia
Keyword(s):  
Fault Diagnosis ◽  
Online Monitoring ◽  
Power Transformer ◽  
Diagnosis System ◽  
Fault Diagnosis System

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.

Based on the Online Monitoring and Fault Diagnosis System of the Main Hoist for Mine

2010 ◽  
Vol 42 ◽  
pp. 250-254
Author(s):  
Zhi Yong Pan ◽  
Quan Cai Wang ◽  
Wei Hong Ren
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Online Monitoring ◽  
Signal Acquisition ◽  
Diagnosis System ◽  
Current Voltage ◽  
Fault Diagnosis System ◽  
Real Time Database ◽  
Signal Acquisition And Processing ◽  
Main Hoist

According to the reality, an online monitoring and fault diagnosis system of the main hoist for Mine was designed in this article. The system adopts the signal acquisition and processing, fault diagnosis, Web visualization, network real-time database and other related technologies, Real-time monitoring the current, voltage, temperature, speed, vibration and other parameters of the main elevators to Achieve the goals that Increasing efficiency by downsizing, protecting the safe operation of equipment, reducing the maintenance costs.

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.

Design and Implementation of Online Monitoring and Remote Diagnostic System for CNC Machine Tools

2013 ◽  
Vol 819 ◽  
pp. 136-139
Author(s):  
Lu Yang Jing ◽  
Tai Yong Wang ◽  
Dong Xiang Chen ◽  
Jing Xiang Fang
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Machine Tools ◽  
Online Monitoring ◽  
Acquisition System ◽  
Cnc Machine Tools ◽  
Cnc Machine ◽  
Diagnosis System ◽  
Fault Diagnosis System ◽  
Remote Fault Diagnosis

With the development of network technology and fault diagnosis technology, monitoring and diagnosis methods for the CNC machine tools had a great change. In this paper, an online monitoring and remote diagnosis system for CNC machine tools was built. The system was consisted of the multi-channel online acquisition system and remote fault diagnosis system. The online acquisition system achieved a real-time monitoring for CNC machine tools. The remote fault diagnosis system provided the management of devices and assistant for experts to analyze data which was uploaded from acquisition system. The system offered real-time state information of CNC machine tools and reduced downtime of machine effectively.

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.

Study on Networking and Intelligent Fault Diagnosis System for Large-Scale Rotating Machinery

2012 ◽  
Vol 588-589 ◽  
pp. 178-184
Author(s):  
Jie Liu ◽  
Fang Xia Hu
Keyword(s):  
Fault Diagnosis ◽  
Remote Monitoring ◽  
Large Scale ◽  
Online Monitoring ◽  
Rotating Machinery ◽  
Functional Modules ◽  
Mixed Structure ◽  
Diagnosis System ◽  
Monitoring And Diagnosis ◽  
Fault Diagnosis System

A networking and intelligent online monitoring and fault diagnosis system for large-scale rotating machinery is developed according to requirements of an iron & steel enterprise. On the aspect of networking, a mixed structure of C/S and B/S is adopted, and the system integrates local online monitoring and diagnosis, remote monitoring and diagnosis, and remote diagnosis center. On the aspect of intelligent diagnosis, a multi-symptom comprehensive parallel diagnosis technology is adopted based on expert system, neural network and fuzzy logic. Finally, main functional modules and its realization are introduced. Application shows that the system runs normally, and the expected objective is achieved.

Sign in / Sign up

Export Citation Format

Share Document