| Feb. 13, 2024, 4:20 p.m. |
Created
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[{"model": "core.projectfund", "pk": 63558, "fields": {"project": 11763, "organisation": 2, "amount": 199295, "start_date": "2015-11-01", "end_date": "2018-01-31", "raw_data": 179797}}]
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| Jan. 30, 2024, 4:24 p.m. |
Created
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[{"model": "core.projectfund", "pk": 56396, "fields": {"project": 11763, "organisation": 2, "amount": 199295, "start_date": "2015-11-01", "end_date": "2018-01-31", "raw_data": 156722}}]
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| Jan. 2, 2024, 4:15 p.m. |
Created
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[{"model": "core.projectfund", "pk": 49210, "fields": {"project": 11763, "organisation": 2, "amount": 199295, "start_date": "2015-11-01", "end_date": "2018-01-31", "raw_data": 134776}}]
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| Dec. 5, 2023, 4:24 p.m. |
Created
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[{"model": "core.projectfund", "pk": 41963, "fields": {"project": 11763, "organisation": 2, "amount": 199295, "start_date": "2015-11-01", "end_date": "2018-01-31", "raw_data": 102703}}]
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| Nov. 27, 2023, 2:14 p.m. |
Added
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{"external_links": []}
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| Nov. 21, 2023, 4:39 p.m. |
Created
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[{"model": "core.projectfund", "pk": 34669, "fields": {"project": 11763, "organisation": 2, "amount": 199295, "start_date": "2015-11-01", "end_date": "2018-01-31", "raw_data": 62764}}]
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| Nov. 21, 2023, 4:39 p.m. |
Created
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[{"model": "core.projectorganisation", "pk": 101226, "fields": {"project": 11763, "organisation": 14852, "role": "COLLAB_ORG"}}]
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| Nov. 21, 2023, 4:39 p.m. |
Created
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[{"model": "core.projectorganisation", "pk": 101225, "fields": {"project": 11763, "organisation": 11348, "role": "LEAD_ORG"}}]
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| Nov. 21, 2023, 4:39 p.m. |
Created
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[{"model": "core.projectperson", "pk": 63686, "fields": {"project": 11763, "person": 16810, "role": "PI_PER"}}]
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| Nov. 21, 2023, 4:39 p.m. |
Created
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[{"model": "core.projectperson", "pk": 63685, "fields": {"project": 11763, "person": 16811, "role": "PI_PER"}}]
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| Nov. 20, 2023, 2:05 p.m. |
Updated
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{"title": ["", "SYNAPS (Synchronous Analysis and Protection System)"], "description": ["", "\nSYNAPS is an innovative project which brings together experts from the\npower engineering, powerline communications, and statistical signal\nprocessing communities to target the so-termed energy trilemma, namely\nthe challenge to improve energy security, reduce carbon emissions, and\nreduce costs.\nSYNAPS aims to develop a networked distribution automation platform for\nlow-voltage networks which will provide fault detection, classification\nand location of faults, together with smart protection and\nreconfiguration, at a significantly lower cost than has previously been\npossible. In effect, this project will add a cost-efficient smart layer\nacross the national power grid which will not only solve long-standing,\nindustry-wide challenges but will also open up countless other\nopportunities for stable, future-proofed growth as our cities and infrastructure become smarter and progress to the internet-of-things\nfuture.\nSince the low-voltage network was originally intended for one-way\ndistribution of energy, there has been little previous interest in\nmonitoring it. However, there is now a new imperative created by the\nimpact on network stability due to the growing deployment of consumer\noperated renewable distributed generation equipment, electric\nvehicles--- not to mention the 'exploding pavements' issue.\nCurrently, distributed generation amounts to only a small proportion of\nthe total network generating capacity, hence its impact on low-voltage\nnetwork performance is negligible. However, there is significant\nindustry concern about the effects of increased numbers of distributed\ngeneration and electric vehicle installations, especially when these are\nconcentrated in co-located clusters.\nThe low-voltage electricity network needs to be able to support two way\nelectrical flow and real-time communication. About 9% of electricity is\nlost in the distribution network, annually, and it has been reported\nthat 45% of Distribution Network Operator total network costs and 50% of\ncustomer minutes lost are due to low-voltage cable faults.\nManaging these new low carbon technologies present significant\nchallenges but early preparation and introduction of a Smart Grid should\nmake the transition easier and reduce overall costs. This project will\ndraw upon machine learning methodology to automatically monitor\nlow-voltage networks and detect and localise both known, and anomalous,\nproblem events. Furthermore, algorithms will also be progressed to\nsupport software-based protection and reconfiguration of the network.\nIt is anticipated that such smart sensor networks will make a\nsignificant contribution in network efficiency and future-proofing, and\nhave immense benefits for both consumers and EU/UK environmental and\nenergy policy targets.\n\n"], "extra_text": ["", "\n\nPotential Impact:\nThis project will deliver economic and commercial impact throughout the\nenergy sector and beyond. Through close collaboration with Powerline\nTechnologies Ltd, Techna International Ltd, Akya Ltd, and other\nstakeholders, including Distribution Network Operators (DNOs), it will\nfacilitate rapid pull-through and knowledge transfer of advanced machine\nlearning and statistical signal processing to deliver enhanced network\nperformance, decrease downtime, and increase reliability. In turn, this\nwill support a range of EU/UK environmental and energy policy targets.\nAnticipated impacts include: \n. Distribution Network Operators are worried about the effect of new\nrenewable technologies on the quality of their network service. The\ndeployment of the Synaps platform will enable them to detect such\nissues rapidly taking remedial action in a much shorter timescale.\nHence, better detection and mitigation of issues related to introduction\nof photovoltaic and electric vehicles into the network, facilitating\nwider deployment with resultant reduction in carbon emissions.\n. Reduce the need for expensive site visits to investigate circuit\nfailures and enabling DNOs to not only fix feeder faults proactively\nbefore they cause a power outage but also to plan their maintenance more\neffectively by concentrating on those cables estimated most probable to\nfail.\n. Increased reliability of low-voltage network with reduced network\ndowntime, providing a better service to electricity consumers.\n. Better low-voltage asset management potentially contributing to lower\nenergy costs. DNOs are under great pressure to extract more value from\ntheir assets.\n. provide are a necessary prerequisite to the evolution of DNOs into a\nmore active role, going beyond mere distribution to take on a role as a\nmanager of "microgrids" balancing renewable energy generation and\nconsumption, leading to a more resilient, cost effective and greener\nindustry.\nIn wider terms, it is anticipated that a successful project will\nshowcase the possibilities afforded by embedding advanced statistical\nsignal processing and machine learning methodology into a physical,\nreal-world system. By forming a genuinely interdisciplinary team from\nacademia and industry, this project will make a genuine leap forward\ninto the future and bring the long anticipated smart cities and\ninternet-of-things concepts one step closer to realisation.\n\n\n"], "status": ["", "Closed"]}
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| Nov. 20, 2023, 2:05 p.m. |
Added
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{"external_links": [47787]}
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| Nov. 20, 2023, 2:05 p.m. |
Created
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[{"model": "core.project", "pk": 11763, "fields": {"owner": null, "is_locked": false, "coped_id": "c61d4c3f-b8eb-4a40-b644-0e96533375d0", "title": "", "description": "", "extra_text": "", "status": "", "start": null, "end": null, "raw_data": 62747, "created": "2023-11-20T13:43:54.497Z", "modified": "2023-11-20T13:43:54.497Z", "external_links": []}}]
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