SYNAPS

  • Le Blond, Simon (PI)
  • Redfern, Miles (CoI)

Project: Research council

Project Details

Description

SYNAPS is an innovative project which brings together experts from the power engineering, powerline communications, and statistical signal processing communities to target the so-termed energy trilemma, namely the challenge to improve energy security, reduce carbon emissions, and reduce costs. SYNAPS aims to develop a networked distribution automation platform for low-voltage networks which will provide fault detection, classification and location of faults, together with smart protection and reconfiguration, at a significantly lower cost than has previously beenpossible. In effect, this project will add a cost-efficient smart layer across the national power grid which will not only solve long-standing, industry-wide challenges but will also open up countless other opportunities for stable, future-proofed growth as our cities and infrastructure become smarter and progress to the internet-of-things future. Since the low-voltage network was originally intended for one-way distribution of energy, there has been little previous interest in monitoring it. However, there is now a new imperative created by the impact on network stability due to the growing deployment of consumer operated renewable distributed generation equipment, electric vehicles - not to mention the 'exploding pavements' issue. Currently, distributed generation amounts to only a small proportion of the total network generating capacity, hence its impact on low-voltage network performance is negligible. However, there is significant industry concern about the effects of increased numbers of distributed generation and electric vehicle installations, especially when these are concentrated in co-located clusters. The low-voltage electricity network needs to be able to support two way electrical flow and real-time communication. About 9% of electricity is lost in the distribution network, annually, and it has been reported that 45% of Distribution Network Operator total network costs and 50% of customer minutes lost are due to low-voltage cable faults. Managing these new low carbon technologies present significant challenges but early preparation and introduction of a Smart Grid should make the transition easier and reduce overall costs. This project will draw upon machine learning methodology to automatically monitor low-voltage networks and detect and localise both known, and anomalous, problem events. Furthermore, algorithms will also be progressed to support software-based protection and reconfiguration of the network. It is anticipated that such smart sensor networks will make a significant contribution in network efficiency and futureproofing, and have immense benefits for both consumers and EU/UK environmental and energy policy targets.
StatusFinished
Effective start/end date1/06/1531/05/17

Funding

  • Engineering and Physical Sciences Research Council

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