Partial Discharge Classification Through Wavelet Packets of Their Modulated Ultrasonic Emission

Faculty of Engineering

Assiut University

Partial Discharge Classification Through Wavelet Packets of Their Modulated Ultrasonic Emission

Research Authors
M. Abdel-Salam, Y. Hasan, M. Sayed and S. Abdel~Sattar
Research Member
Salah Eldin Abdel Sattar
Research Department
Electrical Engineering Department
Research Year
2004
Research Journal
Proc. Of the 5th International Conference on Intelligent Data Engineering and Automated Learning - Ideal 2004, pp. 540-545, Exeter, England, U.K
Research Publisher
NULL
Research Vol
NULL
Research Rank
3
Research_Pages
NULL
Research Website
NULL
Research Abstract

Locating and classifying partial discharge due to sharp-edges, polluted
insulators and loose-contacts in power systems significantly reduce the
outage time, impending failure, equipment damage and supply interruption. In
this paper, based on wavelet packets features of their modulated ultrasound
emissions, an efficient novel scheme for neural network recognition of partial discharges is proposed. The employed preprocessing, wavelet features and near-optimally sized network led to successful classification up to 100%, particularly when longer duration signals are processed.