An EKF‐SVM machine learning‐based approach for fault detection and classification in three‐phase power transformers
Abstract In this paper, a hybrid approach for effective diagnosis of power transformers is proposed. In the proposed method, the extended Kalman filter is used for the estimation of three‐phase currents in the primary windings of the transformer. Three residual signals are defined as the differences...
Main Authors: | Zahra Kazemi, Farshid Naseri, Mehran Yazdi, Ebrahim Farjah |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2021-03-01
|
Series: | IET Science, Measurement & Technology |
Subjects: | |
Online Access: | https://doi.org/10.1049/smt2.12015 |
Similar Items
-
Machine learning in eigensubspace for network path identification and flow forecast
by: Irfan Lateef, et al.
Published: (2021-09-01) -
On the secrecy outage analysis of underlay cognitive radio systems with buffer‐aided relaying under Nakagami‐m channels
by: Mohammad Javad Saber, et al.
Published: (2021-11-01) -
Modelling of the packet delivery rate in an actual LoRaWAN network
by: Ahmed Abdelghany, et al.
Published: (2021-05-01) -
Robust state estimation for uncertain linear discrete systems with d‐step state delay
by: Jing Wang, et al.
Published: (2021-09-01) -
Covariance regulation based invariant Kalman filtering for attitude estimation on matrix Lie groups
by: Jiaolong Wang, et al.
Published: (2021-10-01)