Abstract
The accurate and fast diagnosis of transformer winding deformation faults is of significance to power suppliers and utilities. An improved winding mechanical deformation fault classification method is proposed. In this study, the transformer frequency response data is used to draw polar plots, and then its texture features are extracted for fault classification. The classification model constructed by multiple support vector machines is successfully obtained and shows good classification effect. Besides, this article uses an improved genetic algorithm based on the Emperor-Selective mating scheme and catastrophic operation, to optimize the parameters of support vector machine. The feasibility and accuracy of the proposed method are verified with experimental data obtained from a model transformer, and the proposed method is demonstrated to exhibit better performance compared with the traditional method.
Highlights
Power transformers are among the most important and expensive elements of transmission and distribution networks, which are directly related to the performance and reliability of the network [1]
Mechanical faults can occur on transformer windings, which can subsequently lead to transformer failures [3]
It is necessary to diagnose transformer winding mechanical fault when the fault is at an early stage, to provide guidance on transformer maintenance [6]
Summary
JIANGNAN LIU1, ZHONGYONG ZHAO1, Member, IEEE, KAI PANG2, DONG WANG2, CHAO TANG1, Member, IEEE, and CHENGUO YAO3, Member, IEEE.
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