Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Yanxin Wang"'
Publikováno v:
IET Generation, Transmission & Distribution, Vol 18, Iss 9, Pp 1785-1794 (2024)
Abstract Due to the requirement for highly precise synchronous sampling and the substantial reliance on time difference calculations, the current partial discharge (PD) localization based on the time difference of arrival is only applicable in certai
Externí odkaz:
https://doaj.org/article/a24a8a2933214ecebc164b45b3914605
Publikováno v:
IET Generation, Transmission & Distribution, Vol 17, Iss 19, Pp 4316-4328 (2023)
Abstract In recent years, deep learning‐based fault diagnosis technology for high‐voltage circuit breakers (HVCB) has advanced significantly, but the working environment of HVCBs is complex, resulting in unsatisfactory fault diagnosis results of
Externí odkaz:
https://doaj.org/article/8c5cf25848954005b4e0cac2c7ed606f
Publikováno v:
IET Generation, Transmission & Distribution, Vol 16, Iss 14, Pp 2903-2912 (2022)
Abstract Deep learning (DL) has attracted significant attention in partial discharge (PD) pattern recognition of gas‐insulated switchgear (GIS). However, the existing DL method extracts numerical information and ignores spatial correlation informat
Externí odkaz:
https://doaj.org/article/cda5b5b5d97d40b3be190bf812e7697b
Publikováno v:
IET Generation, Transmission & Distribution, Vol 15, Iss 23, Pp 3229-3241 (2021)
Abstract Recently, numerous data‐driven fault diagnosis methods have been developed, and the tasks involving the same distribution of training and test data have been well solved. However, considering the particularity of gas‐insulated switchgear
Externí odkaz:
https://doaj.org/article/c2a327a8b5854b7588f5892c0f867912
Publikováno v:
IET Generation, Transmission & Distribution. 15:3229-3241