NDLI: Design and development of fault classification algorithm based on relevance vector machine for power transformer

Content Provider	IET Digital Library
Author	Patel, Dharmesh Chothani, Nilesh. G Mistry, Khyati D Raichura, Maulik
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.
Starting Page	557
Ending Page	565
Page Count	9
ISSN	17518660
Volume Number	12
e-ISSN	17518679
Issue Number	Issue 4, Apr (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-epa/12/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-epa.2017.0562
Journal	IET Electric Power Applications
Publisher Date	2018-01-29
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Apparent Power 2 KVA Apparent Power 345 MVA CT Saturation External Abnormality Fault Classification Accuracy Fault Classification Algorithm Fault Classifier Fault Diagnosis Fault Identification Feature Vector High Resistance Internal Fault Condition Internal Fault Magnetising Inrush Multitapping Transformer Neural Nets PNN-based Scheme Post Fault Current Signal Power Transformer Power Transformer Protection Probabilistic Neural Network Relay Relevance Vector Machine Support Vector Machine SVM Transformer And Reactors Transformer Protective Scheme Unit Transformer Protection Voltage 110 V Voltage 220 KV Voltage 230 V Voltage 400 KV
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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