Partial discharge classification using neural networks and statistical parameters

被引：0

作者：

Chen, Hung-Cheng ^{[1
]}

Chen, Po-Hung ^{[2
]}

Wang, Meng-Hui ^{[1
]}

机构：

[1] Natl Chin Yi Univ Technol, Dept Elect Engn, 35,Lane 215,Sec 1,Chungshan Rd, Taichung, Taiwan

[2] Saint Johns Univ, Dept Elect Engn, Taipei, Taiwan

来源：

6TH WSEAS INT CONF ON INSTRUMENTATION, MEASUREMENT, CIRCUITS & SYSTEMS/7TH WSEAS INT CONF ON ROBOTICS, CONTROL AND MANUFACTURING TECHNOLOGY, PROCEEDINGS | 2007年

关键词：

partial discharge; pattern classification; neural network; statistical parameter;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Partial discharge (PD) pattern recognition is an important tool in high-voltage insulation diagnosis of power systems. A PD pattern classification approach of high-voltage power transformers based on a neural network is proposed in this paper. A commercial PD detector is firstly used to measure the 3-D PD patterns of epoxy resin power transformers. Then, the gray intensity histogram extracted from the raw 3-D PD patterns are statistically analyzed for the neural-network-based (NN-based) classification system. The system can quickly and stably learn to categorize input patterns and permit adaptive processes to access significant new information. To demonstrate the effectiveness of the proposed method, the classification ability is investigated on 120 sets of field tested PD patterns of epoxy resin power transformers. Different types of PD within power transformers are identified with rather encouraged results.

引用

页码：84 / +

页数：2

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