An Efficient Method to Localize and Quantify Axial Displacement in Transformer Winding Using Support Vector Machines

Saji, P and Muhammed, A and Vinod, V (2022) An Efficient Method to Localize and Quantify Axial Displacement in Transformer Winding Using Support Vector Machines. In: 2022 IEEE Global Conference on Computing, Power and Communication Technologies, GlobConPT 2022, 23 - 25 September 2022, New Delhi.

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Abstract

Power transformers are an inevitable and expensive equipment in an electrical power system. Condition monitoring uses predictive analysis to determine whether a problem is present or absent in order to prevent transformer failures and guarantee the transformer's safe operation. Among various condition monitoring techniques, Sweep Frequency Response Analysis (SFRA) is a powerful and reliable tool to detect winding deformations. However, the diagnosing potential of SFRA is still its infant state. Any mechanical damage in the transformer winding will change the equivalent circuit parameters and this change will be reflected in the FRA traces. By comparing the FRA traces of the testing transformer with normal winding the fault can be detected. To locate and quantify the axial displacement these FRA traces need to be acknowledged precisely. Support Vector Machine (SVM), a supervised machine learning technique helps to locate and quantify the axial displacement with the help of features extracted from the FRA traces of testing transformer and nominal winding. A series of axial displacements is simulated in FEMM Software and corresponding equivalent circuit parameters are used to generate FRA traces. Furthermore, features are extracted from these FRA traces to train the SVM model to enable it to predict the location and quantity of axial displacement accurately. Finally, the accuracy of this SVM model is tested through randomly created axial displacements data. The result indicates the ability of this technique to be used as an intelligent and accurate diagnostic tool. © 2022 IEEE.

Item Type:	Conference Paper
Publication:	2022 IEEE Global Conference on Computing, Power and Communication Technologies, GlobConPT 2022
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	Condition monitoring; Electric network parameters; Electric transformer testing; Equivalent circuits; Frequency response; Learning systems; Power transformers; Transformer windings; Winding, Axial displacements; Electrical power system; Equivalent circuit parameter; Expensive equipments; Fault localization; Frequency response analysis; Support vector machine models; Support vectors machine; Sweep frequency response analysis; Transformers winding, Support vector machines
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	09 Jan 2023 07:06
Last Modified:	09 Jan 2023 07:06
URI:	https://eprints.iisc.ac.in/id/eprint/78909

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