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Fault detection and identification in three phase transformer using AI based FSA and PVR analysis
Master of Engineering, Control and Instrumentation, CEG, Anna University, Chennai, India.
Review
International Journal of Science and Research Archive, 2023, 10(02), 021–028.
Article DOI: 10.30574/ijsra.2023.10.2.0857
Publication history:
Received on 19 September 2023; revised on 28 October 2023; accepted on 31 October 2023
Abstract:
The objective of this paper is to identify the winding faults in three phase transformer. A prototype model of the transformer has been designed and simulated using finite element method (FEM) based CAD package called Infolitica MagNet 6.11.2 in transient 2D solver. From the simulated results the peak variation response in flux linkage signature, flux density magnitude has been captured to identify the transformer winding faults. Then the faulty transformer result has been compared with simulated healthy transformer result. The design incorporated with external taps to create winding short circuit faults. The flux linkage simulation results are verified and validated with an experimental setup by the help of LabVIEW software.
Keywords:
Fault detection on Transformers, Flux Signature Analysis (FSA); Peak Variation Response (PVR); Park vector approach (PVA); Standard Deviation
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
