Thesis

Vulnerability of short range wireless technologies to impulsive noise in electricity substations

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Awarding institution
  • University of Strathclyde
Date of award
  • 2019
Thesis identifier
  • T15288
Person Identifier (Local)
  • 200755588
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Qualification Name
Department, School or Faculty
Abstract
  • The technical reliability and economic advantages of using sensors, communications and computing to more precisely monitor and control the state of electrical power systems are many. Implementing some of the communications functions wirelessly is cheaper, more flexible and more convenient than an implementation with their wired counterparts. Whilst wireless networks offer these obvious benefits over wired networks, concerns remain which need to be addressed. One such concern is the performance of wireless networks in the electromagnetically aggressive substation environment; an environment that is particularly rich in impulsive noise due to the presence of partial discharge, power electronics switching and other transient processes. -- This thesis investigates the degree to which the dominantly impulsive noise environment of an electricity substation will degrade the performance of wireless technologies, primarily designed to operate in a Gaussian noise environment. -- The electricity-substation noise environment is modelled as both a Middleton class-A process and a symmetric α-stable process. Values of model parameters are estimated from a database of impulsive noise measurements made in a 400/275/132 kV air-insulated substation. Computer simulations are then employed to evaluate the physical layer bit-error-ratio performance of the candidate wireless networking technologies including WLAN, Bluetooth and Zigbee. -- All candidate technologies are shown to suffer a departure in performance degradation from that expected in a Gaussian noise environment in the high SNR region whereas AWGN dominates in the low SNR region. In the high SNR region, there appears to be a noise floor which reduces the effect of an increase in SNR on the corresponding BER.
Advisor / supervisor
  • Glover, Ian Andrew
Resource Type
DOI
Date Created
  • 2018
Former identifier
  • 9912727789402996

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