This project will develop the technology, data processing, support services, BAU operating model and CBRM based asset health modelling required allowing LV cable condition data to be included in the Condition Based Risk Model giving Network Operators the ability to assign Health Indices to its low voltage cables and associated networks.
Benefits
- Production of hardware and backend data processing technologies.
- Production of the relevant processes and models to allow LV cable condition to be included in the CBRM framework.
- Development of a BAU operating model to allow wide scale deployment.
- Production of the CBRM methodology, specifications and codes of practice to permit replication.
Learnings
Outcomes
This has been a very valuable project that has provided learning and outcomes that influence several other projects in the asset health and fault prediction fields. One of the key goals of the project was to identify substations that had faults developing and to provide information that will help give a CBRM score. At the outset of the project, the intent was to design and install monitoring system at LV substations that had a very high bandwidth monitoring system, so as not to miss any vital data that could lead to scoring or fault identification very early in the process.
For the high bandwidth monitoring, a > 100 MHz sampling system was utilised. As well as a signal injection system to take advantage of impulse response analysis. However, this decision to include high bandwidth in the monitoring system had several repercussions:
- Each unit was much more expensive to manufacture, assemble and test – with a high number of units initially failing the testing stage, and having to be re-worked extensively, further increasing cost and time for many of the units.
- Limitations related to the decision of monitoring transformer tails only for current – this aggregated data meant that a substation with failing cable assets could easily be identified, but not individual feeders without further intervention. This is ok from a CBRM analysis for investment purposes, but not practical for individual feeder management.
When the benefits of high bandwidth sampling were reviewed the benefit of it over decent ~ 200 kHz sampling that was synchronised was marginal.
Learning outcomes from the project included:
- Deployment experience of fitting monitoring for current and voltage in substations, showing that advanced triggering and monitoring could be successfully deployed,
- Tested the comms infrastructure by generating very large amounts of data.
- Limitations of only monitoring aggregate transformer tail data
- Requirements for PRESense device, regarding the sampling and individual feeder elements
PRESense has taken these requirements forward and provided an advanced LV monitoring platform with high performance in the LCT identification, asset and fault management and power quality areas. With a wide deployment of PRESense monitors, it is envisaged that the data coming from them will provide a basis for building of a CBRM model for LV cable assets.
Lessons Learnt
Based on the challenges faced during the project additional time spent prototyping the devices to fully understand the complexities of design choices may have highlighted some of the issues at an earlier stage.
This project has shown the benefits of field trials, even where the original plan does not quite come to fruition, the learning generated on this project has benefitted several other projects. This does, however, need to be balanced against the need to provide value for money for customers. This can be a difficult balance to strike, in this instance it is believed that it was struck.
