Design and development of fault classification algorithm based on relevance vector machine for power transformer. Issue 4 (27th February 2018)

• Record Type: Journal Article
• Authors: Patel, Dharmesh; Chothani, Nilesh. G; Mistry, Khyati D; Raichura, Maulik
• Is Part Of: IET electric power applications. Volume 12:Issue 4(2018)
• Page Start: 557
• Page End: 565
• Publication Date: 2018-02-27
• Subjects: neural nets -- support vector machines -- power transformer protection -- fault diagnosis; fault classification algorithm -- relevance vector machine -- power transformer -- fault identification -- unit transformer protection -- relay -- magnetising inrush -- CT saturation -- high resistance internal fault condition -- internal fault -- external abnormality -- transformer protective scheme -- fault classifier -- multitapping transformer -- post fault current signals -- feature vectors -- support vector machine -- SVM -- probabilistic neural network -- fault classification accuracy -- PNN‐based schemes -- apparent power 345 MVA -- voltage 400 kV -- voltage 220 kV -- apparent power 2 kVA -- voltage 230 V -- voltage 110 V; Electric power -- Periodicals; Electric power systems -- Periodicals; 621.305
• DOI: 10.1049/iet-epa.2017.0562
• Languages: English
• ISSNs: 1751-8660

Abstract

Abstract : 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.