Classifying Transformer Winding Deformation Fault Types and Degrees Using FRA Based on Support Vector Machine

Jiangnan Liu,Chao Tang,Zhongyong Zhao,Chengxiang Li,Syed Islam,Chenguo Yao

doi:10.1109/access.2019.2932497

Abstract

As an important part of power system, power transformer plays an irreplaceable role in the process of power transmission. Diagnosis of transformer’s failure is of significance to maintain its safe and stable operation. Frequency response analysis (FRA) has been widely accepted as an effective tool for winding deformation fault diagnosis, which is one of the common failures for power transformers. However, there is no standard and reliable code for FRA interpretation as so far. In this paper, support vector machine (SVM) is combined with FRA to diagnose transformer faults. Furthermore, advanced optimization algorithms are also applied to improve the performance of models. A series of winding fault emulating experiments were carried out on an actual model transformer, the key features are extracted from measured FRA data, and the diagnostic model is trained and obtained, to arrive at an outcome for classifying the fault types and degrees of winding deformation faults with satisfactory accuracy. The diagnostic results indicate that this method has potential to be an intelligent, standardized, accurate and powerful tool.

Highlights

Large power transformers constitute very expensive and vital components in electric power systems [1]
In view of above background, this study proposes the identification of actual transformer winding deformation faults by combining Frequency response analysis (FRA) and support vector machine (SVM)
In this paper, a method of combining SVM with FRA has been evaluated for discriminating fault types and degrees of transformer deformation faults

Summary

INTRODUCTION

Large power transformers constitute very expensive and vital components in electric power systems [1]. J. Liu et al.: Classifying Transformer Winding Deformation Fault Types and Degrees Using FRA Based on SVM is proven to be equivalent of an electrical network consisting of resistance, capacitance and inductance in high frequency range, and its frequency response signature can represent the status of the winding [10]. In reference [17], the identification of winding fault is based on the short circuit impedance method, not the FRA signature; what’s more, the data for training are obtained by FEA. VOLUME 7, 2019 due to its unique advantages in solving small sample, nonlinear and high-dimensional pattern recognition problems; it has good generalization ability in the case of limited samples This is of great practical significance for the fault diagnosis and prediction of power transformer, which, the identification of winding deformation faults is always the problem of sample shortage and high nonlinearity between the fault phenomena and fault reasons [18]. The two sets of data were named normal 1 and normal 2

EXPERIMENTAL RESULT

STRUCTURE OF DATA SETS

Findings

CONCLUSION