Acoustic-based particle detection in oil using artificial neural networks

Author

Sharkawy, R.M. ; Anis, H.

Author_Institution

Dept. of Electr. Metrol., Nat. Inst. for Stand., Giza, Egypt

Volume

4

fYear

2001

fDate

2001

Abstract

This paper contributes to the detection of the presence of free conducting particles in oil-insulated apparatus based on particle-produced acoustics. Acoustic signals are generally produced-under AC-by particle collision against the tank walls. The work uses an inference engine to test for particle contamination in oil by deliberate application of an AC test voltage. The work proposes subjecting the oil-insulated systems to an intentional pre-calculated voltage magnitude for a pre-qualified duration. Using inference, the acoustic signal and pulse train and their statistics could uniquely disclose the characteristics of the contaminating particle

Keywords

acoustic emission testing; automatic test software; electric breakdown; insulation testing; neural nets; power engineering computing; power transformer insulation; power transformer testing; transformer oil; acoustic signal; acoustic-based particle detection; artificial neural networks; contaminating particle; free conducting particles; inference; insulation breakdown testing; oil-insulated power apparatus; particle collision; particle-produced acoustics; pre-calculated voltage magnitude; pulse train; Acoustic signal detection; Acoustic testing; Artificial neural networks; Dielectrics and electrical insulation; Electrostatics; Intelligent networks; Oil insulation; Partial discharges; Petroleum; Power transformer insulation;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Tech Proceedings, 2001 IEEE Porto

Conference_Location

Porto

Print_ISBN

0-7803-7139-9

Type

conf

DOI

10.1109/PTC.2001.964858

Filename

964858