Abstract
A method available for early detection of abnormality in an oil-filled transformer is discribed, in which four gas sensors having different characteristics and neural network are used to identify gas species (H2, CH4, C2H4, C2H2 and mixture of two species). In order to improve the selectivity of gas sensors, the time response patterns induced by changing sensor temperature, and the stationary sensor output is identified by neural network. Furthermore, the mixture ratio of gases is derived by using the stationary sensor output in response to the changing sensor temperature.Gas species are well discriminated, and the mixture ratio derived from sensor output agrees well with the measurement by gas chromatography. Therefore, it is confirmed that our method is applicable to the transformer diagnostic technology.
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: 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.
