Application of an ensemble neural network for classifying partial discharge patterns

A. Abubakar Mas Ud*, B.G. Stewart, S.G. McMeekin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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).
Original languageEnglish
Pages (from-to)154–162
Number of pages9
JournalElectric Power Systems Research
Early online date15 Feb 2014
Publication statusPublished - May 2014


  • ensemble neural network
  • PD patterns

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