Abductive network model-based diagnosis system for power transformer incipient fault detection

Y. C. Huang, Hong-Tzer Yang, K. Y. Huang

研究成果: Article同行評審

25 引文 斯高帕斯(Scopus)

摘要

An abductive network model (ANM)-based diagnosis system for power transformers fault detection is presented that enhances the diagnostic accuracy of the power transformer incipient fault. The ANM formulates the diagnosis problem into a hierarchical architecture with several layers of function nodes of simple low-order polynomials. The ANM links the complicated and numerical knowledge relationships of diverse dissolved gas contents in the transformer oil with their corresponding fault types. The proposed ANM has been tested on the Taipower company diagnostic records and compared with the previous fuzzy diagnosis system, artificial neural network as well as with the conventional method. The test results confirm that the ANM possesses far superior diagnosis accuracy and requires less effort to develop.

原文English
頁(從 - 到)326-330
頁數5
期刊IEE Proceedings: Generation, Transmission and Distribution
149
發行號3
DOIs
出版狀態Published - 2002 5月 1

All Science Journal Classification (ASJC) codes

  • 電氣與電子工程

指紋

深入研究「Abductive network model-based diagnosis system for power transformer incipient fault detection」主題。共同形成了獨特的指紋。

引用此