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

Zhongyong Zhao, Chao Tang, Qu Zhou, Lingna Xu, Yingang Gui and Chenguo Yao
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Zhongyong Zhao: College of Engineering and Technology, Southwest University, Chongqing 400715, China
Chao Tang: College of Engineering and Technology, Southwest University, Chongqing 400715, China
Qu Zhou: College of Engineering and Technology, Southwest University, Chongqing 400715, China
Lingna Xu: College of Engineering and Technology, Southwest University, Chongqing 400715, China
Yingang Gui: College of Engineering and Technology, Southwest University, Chongqing 400715, China
Chenguo Yao: State Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China

Energies, 2017, vol. 10, issue 12, 1-16

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.

Keywords: transformer; online impulse frequency response; mechanical fault; windings; support vector machine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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