Open AccessRadio location of partial discharge sources: a support vector regression approach
Author(s) -
Iorkyase Ephraim T.,
Tachtatzis Christos,
Lazaridis Pavlos,
Glover Ian A.,
Atkinson Robert C.
Publication year - 2018
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2017.0175
Subject(s) - support vector machine , artificial neural network , regression , partial discharge , computer science , feature vector , artificial intelligence , regression analysis , data mining , least squares support vector machine , pattern recognition (psychology) , machine learning , kernel (algebra) , statistics , engineering , mathematics , combinatorics , voltage , electrical engineering
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 study 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 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 with 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 its low complexity.