| EPSRC Reference: |
EP/N508470/1 |
| Title: |
SYNAPS (Synchronous Analysis and Protection System) |
| Principal Investigator: |
Olhede, Professor SC |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Statistical Science |
| Organisation: |
UCL |
| Scheme: |
Technology Programme |
| Starts: |
01 November 2015 |
Ends: |
31 January 2018 |
Value (£): |
199,296
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| EPSRC Research Topic Classifications: |
| Sustainable Energy Networks |
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| EPSRC Industrial Sector Classifications: |
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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 been
possible. 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 future-proofing, 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: |
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