We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Identification of Power Transformer Winding Mechanical Fault Types Based on Online IFRA by Support Vector Machine.
- Authors
Zhongyong Zhao; Chao Tang; Qu Zhou; Lingna Xu; Yingang Gui; Chenguo Yao
- 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.
- Subjects
POWER transformers; SUPPORT vector machines; FREQUENCY response; ELECTRIC utilities; FAULT indicators (Electricity)
- Publication
Energies (19961073), 2017, Vol 10, Issue 12, p2022
- ISSN
1996-1073
- Publication type
Article
- DOI
10.3390/en10122022