Application of an ensemble neural network for classifying partial discharge patterns

A.A.a Mas'ud, B.G. Stewart, S.G. McMeekin

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

This paper proposes a technique for classifying partial discharge (PD) patterns based on ensemble neural network (ENN) learning. The ENN technique is based on training a number of neural network (NN) models with statistical parameters from PD patterns and combining their predictions. In this paper, six constituent NN models form the ensemble. Combining the outputs of the constituent NNs through an aggregating unit using dynamically weighted averaging strategy gives a final evaluation of PD patterns in relation to a range of PD fault types. Using the data sets of measured PD patterns as the system input fingerprints, the classification performance of the ENN has been compared statistically and quantitatively with a single neural network (SNN). This is achieved through evaluating the average, variance and standard error of the means of ENN and SNN recognition performances over 100 different initial states of the NNs thus providing an effective comparison to be made. The result shows that the ENN appears to be more robust with statistically improved performance in recognizing untrained PD patterns for a number of PD fault geometries. © 2014 Elsevier B.V.
Original languageEnglish
Pages (from-to)154-162
Number of pages9
JournalElectric Power Systems Research
Volume110
DOIs
Publication statusPublished - 31 May 2014
Externally publishedYes

Keywords

  • partial discharge
  • ensemble neural network
  • single neural network

Fingerprint Dive into the research topics of 'Application of an ensemble neural network for classifying partial discharge patterns'. Together they form a unique fingerprint.

  • Cite this