Bayesian neural network and discrete wavelet transform for partial discharge pattern classification in high voltage equipment

Author

Hui Ma ; Chan, Jeffery C. ; Saha, Tapan K.

Author_Institution

Sch. of Inf. Technol. & Electr. Eng., Univ. of Queensland, Brisbane, QLD, Australia

fYear

2013

fDate

21-25 July 2013

Firstpage

1

Lastpage

5

Abstract

Partial discharge (PD) pattern recognition has been applied for identifying the types of insulation defects in high voltage (HV) equipment. This paper proposes a novel Bayesian neural network (BNN) and discrete wavelet transform (DWT) hybrid algorithm for PD pattern recognition. Laboratory experiments on a number of PD models have been conducted for evaluating the performance of the proposed algorithm.

Keywords

belief networks; discrete wavelet transforms; electrical engineering computing; insulation; neural nets; partial discharge measurement; pattern classification; power apparatus; BNN; Bayesian neural network; DWT hybrid algorithm; HV; PD pattern recognition; discrete wavelet transform; high voltage equipment; insulation defects; partial discharge pattern classification; Bayes methods; Discharges (electric); Discrete wavelet transforms; Feature extraction; Partial discharges; Pattern recognition; Probability distribution; Bayesian neural network (BNN); Partial Discharge (PD); discrete wavelet transform (DWT); pattern recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Power and Energy Society General Meeting (PES), 2013 IEEE

Conference_Location

Vancouver, BC

ISSN

1944-9925

Type

conf

DOI

10.1109/PESMG.2013.6672075

Filename

6672075