| Nov. 27, 2023, 2:13 p.m. |
Added
35
|
{"external_links": []}
|
|
| Nov. 20, 2023, 2:03 p.m. |
Added
35
|
{"external_links": []}
|
|
| Nov. 13, 2023, 1:33 p.m. |
Added
35
|
{"external_links": []}
|
|
| Nov. 6, 2023, 1:31 p.m. |
Added
35
|
{"external_links": []}
|
|
| Aug. 14, 2023, 1:31 p.m. |
Added
35
|
{"external_links": []}
|
|
| Aug. 7, 2023, 1:32 p.m. |
Added
35
|
{"external_links": []}
|
|
| July 31, 2023, 1:34 p.m. |
Added
35
|
{"external_links": []}
|
|
| July 24, 2023, 1:35 p.m. |
Added
35
|
{"external_links": []}
|
|
| July 17, 2023, 1:34 p.m. |
Added
35
|
{"external_links": []}
|
|
| July 10, 2023, 1:26 p.m. |
Added
35
|
{"external_links": []}
|
|
| July 3, 2023, 1:26 p.m. |
Added
35
|
{"external_links": []}
|
|
| June 26, 2023, 1:26 p.m. |
Added
35
|
{"external_links": []}
|
|
| June 19, 2023, 1:27 p.m. |
Added
35
|
{"external_links": []}
|
|
| June 12, 2023, 1:29 p.m. |
Added
35
|
{"external_links": []}
|
|
| June 5, 2023, 1:33 p.m. |
Added
35
|
{"external_links": []}
|
|
| May 29, 2023, 1:27 p.m. |
Added
35
|
{"external_links": []}
|
|
| May 22, 2023, 1:29 p.m. |
Added
35
|
{"external_links": []}
|
|
| May 15, 2023, 1:31 p.m. |
Added
35
|
{"external_links": []}
|
|
| May 8, 2023, 1:37 p.m. |
Added
35
|
{"external_links": []}
|
|
| May 1, 2023, 1:28 p.m. |
Added
35
|
{"external_links": []}
|
|
| April 24, 2023, 1:35 p.m. |
Added
35
|
{"external_links": []}
|
|
| April 17, 2023, 1:28 p.m. |
Added
35
|
{"external_links": []}
|
|
| April 10, 2023, 1:25 p.m. |
Added
35
|
{"external_links": []}
|
|
| April 3, 2023, 1:26 p.m. |
Added
35
|
{"external_links": []}
|
|
| Jan. 28, 2023, 11:08 a.m. |
Created
43
|
[{"model": "core.projectfund", "pk": 28374, "fields": {"project": 5581, "organisation": 2, "amount": 0, "start_date": "2017-09-30", "end_date": "2021-09-29", "raw_data": 47429}}]
|
|
| Jan. 28, 2023, 10:52 a.m. |
Updated
35
|
{"status": ["Active", "Closed"]}
|
|
| Jan. 28, 2023, 10:52 a.m. |
Added
35
|
{"external_links": []}
|
|
| April 11, 2022, 3:47 a.m. |
Created
43
|
[{"model": "core.projectfund", "pk": 20496, "fields": {"project": 5581, "organisation": 2, "amount": 0, "start_date": "2017-09-30", "end_date": "2021-09-29", "raw_data": 25932}}]
|
|
| April 11, 2022, 3:47 a.m. |
Created
41
|
[{"model": "core.projectorganisation", "pk": 77659, "fields": {"project": 5581, "organisation": 2209, "role": "STUDENT_PP_ORG"}}]
|
|
| April 11, 2022, 3:47 a.m. |
Created
41
|
[{"model": "core.projectorganisation", "pk": 77658, "fields": {"project": 5581, "organisation": 1047, "role": "LEAD_ORG"}}]
|
|
| April 11, 2022, 3:47 a.m. |
Created
40
|
[{"model": "core.projectperson", "pk": 47851, "fields": {"project": 5581, "person": 7889, "role": "STUDENT_PER"}}]
|
|
| April 11, 2022, 1:48 a.m. |
Updated
35
|
{"title": ["", "System intelligence for identifying environmental threats to distribution networks"], "description": ["", "\nTransmission and distribution network asset health can be threatened by surroundings that can impose highly localised weather effects, missed through regional meteorological observations. Diversity of plant utilisation can add further complexity to the relation between true asset health and the environment it is installed in. The challenge posed in monitoring these assets in a more representative manner is in drawing together diverse data streams to model the relation between established weather stations, specific measurement locations (such as on-tower weather measurements or LV substation outbuilding environmental monitoring) on the network and using this as a means of generating prioritisations of maintenance or inspection. Improved indication of network pinch points and their likely location can direct limited field resource to potential causes of network disruption in shorter timeframes.\n\nTo capture the potentially complex relation between the influencing factors involved, a machine learning approach will be developed to automatically identify where and how network assets may be challenged and how this can be reported in an actionable manner. These analytics will need to be designed to unlock additional value from existing data streams that network operators already hold with no additional monitoring equipment required owing to the potential difficulties associated with deploying remote or invasive sensing on certain power system assets. Analysis and re-purposing of these data streams will facilitate development of:\n- Understanding of actual asset utilisation and potential operational headroom for planning purposes\n- Metrics from utilisation characteristics to infer asset health index and highlight network 'hotspots'\nThe immediate operational value of these analysis tools is likely to lie in the ability to prioritise maintenance according to condition rather than time which could make more effective use of limited field staff resources from a network reliability perspective. The immediate operational value of these analysis tools is likely to lie in the ability to prioritise maintenance according to condition rather than time which could make more effective use of limited field staff resources from a network reliability perspective.\n\n"], "extra_text": ["", "\n\n\n\n"], "status": ["", "Active"]}
|
|
| April 11, 2022, 1:48 a.m. |
Added
35
|
{"external_links": [20664]}
|
|
| April 11, 2022, 1:48 a.m. |
Created
35
|
[{"model": "core.project", "pk": 5581, "fields": {"owner": null, "is_locked": false, "coped_id": "e534c582-f9bb-4008-a865-8b6af80e9589", "title": "", "description": "", "extra_text": "", "status": "", "start": null, "end": null, "raw_data": 25916, "created": "2022-04-11T01:41:10.417Z", "modified": "2022-04-11T01:41:10.417Z", "external_links": []}}]
|
|