Open AccessDesign and development of fault classification algorithm based on relevance vector machine for power transformer
Author(s) -
Patel Dharmesh,
Chothani Nilesh. G,
Mistry Khyati D,
Raichura Maulik
Publication year - 2018
Publication title -
iet electric power applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.815
H-Index - 97
eISSN - 1751-8679
pISSN - 1751-8660
DOI - 10.1049/iet-epa.2017.0562
Subject(s) - support vector machine , engineering , inrush current , relevance vector machine , artificial neural network , feature vector , transformer , pattern recognition (psychology) , computer science , artificial intelligence , algorithm , voltage , electrical engineering
Identification of faults within power transformers is the means of ensuring unit transformer protection. Existing relay maloperates during abnormalities such as magnetising inrush, CT saturation and high resistance internal fault condition. Therefore, it is essential to categorise the internal fault and external abnormality/fault in case of transformer protection. This study presents a new scheme, based on relevance vector machine (RVM) as a fault classifier. The developed algorithm is assessed by simulating various disorders on 345 MVA, 400/220 kV transformer in PSCAD/EMTDC software and also on prototype model with 2 kVA, 230/110 V multi‐tapping transformer. One cycle post fault current signals are captured from primary and secondary to form feature vectors. These feature vectors are used as an input to RVM for classification of various test cases. Wide variation in system parameters and fault conditions are considered for test data generation and validation. The proposed scheme is compared with the support vector machine (SVM) and probabilistic neural network (PNN)‐based techniques. The proposed scheme successfully discriminates various types of internal faults and external abnormalities in power transformer within a short time. The fault classification accuracy obtained by proposed RVM technique is more than 99% in comparison to SVM and PNN‐based schemes.