Fault Detection of Power Grid Using Graph Convolutional Networks
Abstract
References
Index Terms
- Fault Detection of Power Grid Using Graph Convolutional Networks
Recommendations
Intelligent fault detection using raw vibration signals via dilated convolutional neural networks
AbstractFault detection and diagnosis is critical to improve the reliability and availability in induction motors (IMs). Machine learning and deep learning techniques have been widely used in induction motor fault detection and diagnosis. In this paper, ...
Internal fault detection techniques for power transformers
This paper presents the methodologies for incipient fault detection in power transformers both off-line and on-line. An artificial neural network is used to detect faults off-line with dissolved gas analysis reports of transformers and whereas wavelet ...
Power System Fault Detection and Classification Using Wavelet Transform and Artificial Neural Networks
Advances in Neural Networks – ISNN 2019AbstractPower system fault detection has been an import area of study for power distribution networks. The power transmission systems often operate in the kV range with significant current flowing through the lines. A single fault, even lasting for a ...
Comments
Information & Contributors
Information
Published In

Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 17Total Downloads
- Downloads (Last 12 months)17
- Downloads (Last 6 weeks)3
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format