Low Complexity Wireless Sensor System for Partial Discharge Localisation

Ephrain T. Iorkyase, Christos Tachtatzis, Pavlos Lazaridis, Ian Glover, Robert Atkinson

Research output: Contribution to journalArticle

Abstract

This study describes a key element of any modern wireless sensor system: data processing. The authors describe a system consisting of a wireless sensor network and an algorithmic software for condition-based monitoring of electrical plant in a live substation. Specifically, the aim is to monitor for the presence of partial discharge (PD) using a matrix of inexpensive radio sensors with limited processing capability. A low-complexity fingerprinting technique is proposed, given that the sensor nodes to be deployed will be highly constrained in terms of processing power, memory and battery life. Two variants of artificial neural network (ANN) learning models (multilayer perceptron and generalised regression neural network) that use regression as a form of function approximation are developed and their performance compared to K-nearest neighbour and weighted K-nearest neighbour models. The results indicate that the ANN models yield superior performance in terms of robustness against noise and may be particularly suited for PD localisation.

LanguageEnglish
Pages158-165
Number of pages8
JournalIET Wireless Sensor Systems
Volume9
Issue number3
Early online date25 Mar 2019
DOIs
Publication statusPublished - 27 May 2019

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Partial discharges
Neural networks
Sensors
Multilayer neural networks
Processing
Sensor nodes
Wireless sensor networks
Data storage equipment
Monitoring

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Iorkyase, Ephrain T. ; Tachtatzis, Christos ; Lazaridis, Pavlos ; Glover, Ian ; Atkinson, Robert. / Low Complexity Wireless Sensor System for Partial Discharge Localisation. In: IET Wireless Sensor Systems. 2019 ; Vol. 9, No. 3. pp. 158-165.
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Low Complexity Wireless Sensor System for Partial Discharge Localisation. / Iorkyase, Ephrain T.; Tachtatzis, Christos; Lazaridis, Pavlos; Glover, Ian; Atkinson, Robert.

In: IET Wireless Sensor Systems, Vol. 9, No. 3, 27.05.2019, p. 158-165.

Research output: Contribution to journalArticle

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