Radio location of partial discharge sources: a support vector regression approach

E. T. Iorkyase, C. Tachtatzis, P. Lazaridis, I. A. Glover, R. C. Atkinson

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12 Citations (Scopus)
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Partial discharge (PD) can provide a useful forewarning of asset failure in electricity substations. A significant proportion of assets are susceptible to PD due to incipient weakness in their dielectrics. This paper examines a low cost approach for uninterrupted monitoring of PD using a network of inexpensive radio sensors to sample the spatial patterns of PD received signal strength. Machine learning techniques are proposed for localisation of PD sources. Specifically, two models based on Support Vector Machines (SVMs) are developed: Support Vector Regression (SVR) and Least-Squares Support Vector Regression (LSSVR). These models construct an explicit regression surface in a high dimensional feature space for function estimation. Their performance is compared to that of artificial neural network (ANN) models. The results show that both SVR and LSSVR methods are superior to ANNs in accuracy. LSSVR approach is particularly recommended as practical alternative for PD source localisation due to it low complexity.
Original languageEnglish
Number of pages9
JournalIET Science, Measurement and Technology
Early online date1 Nov 2017
Publication statusE-pub ahead of print - 1 Nov 2017


  • partial discharge
  • electricity substations
  • support vector regression

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