| EPSRC Reference: |
EP/N508469/1 |
| Title: |
SYNAPS - SYNchronous Automation and Protection System |
| Principal Investigator: |
Le Blond, Dr S |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Electronic and Electrical Engineering |
| Organisation: |
University of Bath |
| Scheme: |
Technology Programme |
| Starts: |
01 June 2015 |
Ends: |
31 May 2017 |
Value (£): |
243,285
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| EPSRC Research Topic Classifications: |
| Sustainable Energy Networks |
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| EPSRC Industrial Sector Classifications: |
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| Panel History: |
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Summary on Grant Application Form |
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.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.bath.ac.uk |