Identification of Power Transformer Winding Mechanical Fault Types Based on Online IFRA by Support Vector Machine
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 mechanica...
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MDPI AG
2017-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/10/12/2022 |
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doaj-341c87c0623747be858bfc874228fa0c2020-11-24T23:56:43ZengMDPI AGEnergies1996-10732017-12-011012202210.3390/en10122022en10122022Identification of Power Transformer Winding Mechanical Fault Types Based on Online IFRA by Support Vector MachineZhongyong Zhao0Chao Tang1Qu Zhou2Lingna Xu3Yingang Gui4Chenguo Yao5College of Engineering and Technology, Southwest University, Chongqing 400715, ChinaCollege of Engineering and Technology, Southwest University, Chongqing 400715, ChinaCollege of Engineering and Technology, Southwest University, Chongqing 400715, ChinaCollege of Engineering and Technology, Southwest University, Chongqing 400715, ChinaCollege of Engineering and Technology, Southwest University, Chongqing 400715, ChinaState Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, ChinaA 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.https://www.mdpi.com/1996-1073/10/12/2022transformeronline impulse frequency responsemechanical faultwindingssupport vector machine |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Zhongyong Zhao Chao Tang Qu Zhou Lingna Xu Yingang Gui Chenguo Yao |
| spellingShingle |
Zhongyong Zhao Chao Tang Qu Zhou Lingna Xu Yingang Gui Chenguo Yao Identification of Power Transformer Winding Mechanical Fault Types Based on Online IFRA by Support Vector Machine Energies transformer online impulse frequency response mechanical fault windings support vector machine |
| author_facet |
Zhongyong Zhao Chao Tang Qu Zhou Lingna Xu Yingang Gui Chenguo Yao |
| author_sort |
Zhongyong Zhao |
| title |
Identification of Power Transformer Winding Mechanical Fault Types Based on Online IFRA by Support Vector Machine |
| title_short |
Identification of Power Transformer Winding Mechanical Fault Types Based on Online IFRA by Support Vector Machine |
| title_full |
Identification of Power Transformer Winding Mechanical Fault Types Based on Online IFRA by Support Vector Machine |
| title_fullStr |
Identification of Power Transformer Winding Mechanical Fault Types Based on Online IFRA by Support Vector Machine |
| title_full_unstemmed |
Identification of Power Transformer Winding Mechanical Fault Types Based on Online IFRA by Support Vector Machine |
| title_sort |
identification of power transformer winding mechanical fault types based on online ifra by support vector machine |
| publisher |
MDPI AG |
| series |
Energies |
| issn |
1996-1073 |
| publishDate |
2017-12-01 |
| description |
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. |
| topic |
transformer online impulse frequency response mechanical fault windings support vector machine |
| url |
https://www.mdpi.com/1996-1073/10/12/2022 |
| work_keys_str_mv |
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| _version_ |
1725456858811990016 |