期刊
IEEE TRANSACTIONS ON POWER DELIVERY
卷 18, 期 4, 页码 1257-1261出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRD.2003.817736
关键词
data mining; dissolved gas analysis; power transformers
This paper proposes genetic algorithm tuned wavelet networks (GAWNs) for data mining of dissolved-gas-analysis (DGA) records and incipient fault detection of oil-insulated power transformers. The genetic algorithm-based (GA) optimization process automatically tunes the parameters of wavelet networks: translation and dilation of the wavelet nodes, and the weighting values of the weighting nodes. The GAWNs can identify the complex relations between the dissolved gas content of transformer oil and corresponding fault types. The proposed GAWNs have been tested on the Taipower Company's diagnostic records, using four diagnosis criteria, and compared with artificial neural networks (ANNs) and conventional methods. Experimental results demonstrate that the GAWNs have remarkable diagnosis accuracy and require far less learning time than ANNs for different diagnosis criteria.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据