Network Monitoring, Gas Transmission Networks and Modelling
The ConCEPT project at the University of Southampton builds on completed elements of several projects, namely Resilient Networks (RESNET (NIA_NGET0053) – an IFI/NIA funded project at University of Manchester), FENCE (Facilitating Enhanced Network Capacity Evaluation, NIA funded project with University of Southampton) TeRMiTE (NIA_NGET0165) ( Transformer Rating Modelling Tool Enhancements, NIA project with University of Southampton) and Improved Transformer Thermal Monitoring (IFI funded project with Southampton Dielectric Consultants). The aim is to take the findings from several of these projects and make them readily implementable.The specific areas from these projects that should be advanced are shown in the attached detailed project proposal.
The overall objectives of this project are:
- to develop understanding of phenomena that could materially affect the operation of transformers in the coming decades
- to explore how greater value that can be extracted from existing data and potentially new sensors to optimise maintenance interventions, and
- ensure that we make the most of the thermal capability of ageing transformers without compromising their reliability.
This project compliments the research that will be undertaken under the NIA funded project Optimised Asset Life and Asset Management of Transformer and Transformer Oil Through Analysis and Modelling.
Specific objectives for each of the work packages in this project are as follows:
WP1 – Installation of sensors on selected transformers to support the other packages of work
WP2 – Investigation of climate issues, this will include investigation of the urban heat island effect and challenge seasonal assumptions of static ratings to ensure transformers are not at risk of optimistic rating enhancements and evaluate the potential accuracy of predictive ratings to mitigate rising temperatures owing to climate change.
WP3 – Leveraging data warehouse resources along with monitoring data to ensure that transformers are operating correctly, specifically using rating software to flag automatically where winding temperature indicators (WTIs) and cooling are not operating correctly so that condition based maintenance interventions can be planned.
WP4 – Use of thermal monitoring to investigate condition and operation of transformers to identify where condition issues should result in reduced rating assumptions to maintain transformer reliability and investigation of any impacts of on-load oil regeneration on ratings
WP5 – Investigation of alternative algorithms to establish whether machine learning tools could replace detailed thermal models for transformer ratings, the same approach will also be applied to dissolved gas analysis (DGA) from online monitoring and in combination identify divergent behaviour within transformer families for early detection of potential faults.
This project will be successful if the work enables the following:
- proposals for seasonal ambient conditions for static ratings taking into account urban heat islanding
- an evaluation of the likely accuracy of predictive ratings
- an indication of reduction of thermal performance resulting from transformer degradation
- a model for assessing WTI performance and identification of defective instruments
- a review of the application of alternative algorithms for transformer condition based on thermal performance
- a view on the likely effects of climate change on static ratings for transformer until the end of the 21st century.