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Energies 2017, 10(12), 2022; https://doi.org/10.3390/en10122022

Identification of Power Transformer Winding Mechanical Fault Types Based on Online IFRA by Support Vector Machine

1
College of Engineering and Technology, Southwest University, Chongqing 400715, China
2
State Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China
*
Author to whom correspondence should be addressed.
Received: 13 November 2017 / Revised: 28 November 2017 / Accepted: 29 November 2017 / Published: 1 December 2017
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Abstract

A transformer is the most valuable and expensive property for power utility, thus ensuring its reliable operation is a major task for both operators and researchers. Online impulse frequency response analysis has proven to be a promising technique for detecting transformer internal winding mechanical deformation faults when a power transformer is in service. However, as so far, there is still no reliable standard code for frequency response signature interpretation and quantification. This paper tries to utilize a machine learning method, namely the support vector machine, to identify and classify the winding mechanical fault types, based on online impulse frequency response analysis. Actual transformer fault data from a specially manufactured model transformer are collected and analyzed. Two feature vectors are proposed and the diagnostic results are predicted. The diagnostic results indicate the satisfied classifying accuracy by the proposed method. View Full-Text
Keywords: transformer; online impulse frequency response; mechanical fault; windings; support vector machine transformer; online impulse frequency response; mechanical fault; windings; support vector machine
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Zhao, Z.; Tang, C.; Zhou, Q.; Xu, L.; Gui, Y.; Yao, C. Identification of Power Transformer Winding Mechanical Fault Types Based on Online IFRA by Support Vector Machine. Energies 2017, 10, 2022.

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