Abstract
The power transformer is an example of the key equipment of power grid, and its potential faults limit the system availability and the enterprise security. However, fault prediction for power transformers has its limitations in low data quality, binary classification effect, and small sample learning. We propose a method for fault prediction for power transformers based on dissolved gas chromatography data: after data preprocessing of defective raw data, fault classification is performed based on the predictive regression results. Here, Mish-SN Temporal Convolutional Network (MSTCN) is introduced to improve the accuracy during the regression step. Several experiments are conducted using data set from China State Grid. The discussion of the results of experiments is provided.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.