Vulnerability of Wireless Network Technology to Impulsive Noise in Electricity Transmission Substations

Lead Research Organisation: University of Strathclyde
Department Name: Electronic and Electrical Engineering

Abstract

The infrastructure investment in a national power transmission system is colossal. It is therefore necessary to operate such systems as efficiently as possible, consistent with maintaining acceptable security of supply. Efficient and reliable operation demands continuous monitoring of the system's state resulting in instrumentation and control equipment being widely scattered throughout transmission substation compounds. Information and control signals for both normal and abnormal operation are traditionally connected to a monitoring and control system using cables or optical fibres. Significant flexibility and cost advantages over a wired infrastructure would be gained, however, if signals could be routed around electricity substation compounds wirelessly. Furthermore, wireless communication technologies hold out the prospect of 'hot-line', sensors that can be deployed on energised high voltage equipment without the inconvenience and costs associated with bridging the power system's primary insulation.Wireless LAN (WLAN) and Bluetooth technologies represent obvious opportunities to realise these advantages.The casual deployment of wireless technologies for critical functions is not, however, without risk. The man-made noise environment within a substation compound is extremely hostile due to partial discharge (PD) radiation from imperfect insulation and radiation from switching and fault transients.The principal aim of the proposed programme is to assess the vulnerability of WLAN technologies to impulsive noise in the particularly hostile environment of electrical transmission substations, and thus assess the suitability of such systems for monitoring, control and protection applications. If these technologies are found to be sufficiently robust the economic and technical benefit of their deployment will be substantial. If they are found to be insufficiently robust then the programme will identify the mechanisms by which performance and reliability are degraded, allowing the development of appropriate mitigation techniques and countermeasures.

Publications

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Shan Q (2009) Detection of super-high-frequency partial discharge by using neural networks in Insight - Non-Destructive Testing and Condition Monitoring