Power transformer fault diagnosis system based on Internet of Things
| Autor: | Sui Haibin, Liu Ying, Junfei Zhang, Guoshi Wang, Chen Xiaowen, Yan Qing, Ma Chao |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Internet of things
Instrument Driver Computer Networks and Communications Computer science business.industry lcsh:Electronics lcsh:TK7800-8360 Wireless communication and network Telecommunications network lcsh:Telecommunication Computer Science Applications law.invention System model Reliability engineering Electric power system Data acquisition Power transformer law lcsh:TK5101-6720 Signal Processing General Packet Radio Service Transformer Internet of Things business Fault diagnosis |
| Zdroj: | EURASIP Journal on Wireless Communications and Networking, Vol 2021, Iss 1, Pp 1-24 (2021) |
| ISSN: | 1687-1499 |
| DOI: | 10.1186/s13638-020-01871-6 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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