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

© The Institution of Engineering and Technology
Received 15/04/2017, Accepted 26/10/2017, Revised 27/09/2017, Published 01/11/2017

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.

Inspec keywords: radio direction-finding; partial discharge measurement; support vector machines; failure analysis; learning (artificial intelligence); wireless sensor networks; computerised instrumentation; least squares approximations; regression analysis

Other keywords: failure assessment; high-dimensional feature space; machine learning technique; ANN model; PD source localisation; artificial neural network model; PD received signal strength; partial discharge source; uninterrupted PD monitoring; least-squares support vector regression; explicit regression surface; function estimation; dielectric material; electricity substation; radio sensor; radio location; LSSVR approach; support vector machine

Subjects: Computerised instrumentation; Radionavigation and direction finding; Wireless sensor networks; Dielectric breakdown and discharges; Sensing devices and transducers; Other topics in statistics; Computerised instrumentation; Charge measurement; Reliability; Interpolation and function approximation (numerical analysis); Interpolation and function approximation (numerical analysis); Knowledge engineering techniques; Other topics in statistics

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