Jan 2017
Electricity Transmission
Development of fittings analysis model
Jan 2017
Jan 2018
National Grid Electricity Transmission
Fan Li
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Network Innovation Allowance
Overhead Lines, Modelling, Asset Management, Condition Monitoring and Electricity Transmission Networks
The current project is designed to maximise the utilisation of the OHL fittings condition information that is collected by National Grid, in order to gain better understanding on the future deterioration, and enable better long term planning. At the same time, it will evaluate the suitability of current asset management practices and make recommendations for new developments and improvements. The project scope include the following phases:

  1. Analyse the condition information collected on a span-by-span, and component-by-component basis, and Explore the evidence in support (or otherwise) of the fittings investment strategy.
  2. Review the process and support validation of the formula for assigning Asset Health Index (AHI) scores from condition assessments.
  3. Compare the condition profile (risk profile) of OHL fittings with National Grid’s deterioration model and explore different model types and forms as appropriate.
  4. Study the interactions between different asset strategies, namely: fittings investment strategy, conductor investment strategy, and targeted fittings. Provide recommendations in terms of best practices for balancing cost, risk and process complexity for different strategy and policy options.

The anticipated project deliverables are:

  1. A study facilitated by a summary report detailing the latest understandings to the end of life behaviours of fittings components using the up-to-date condition assessment information and engineering expertise.
  2. A robust asset health model to facilitate the asset health process in a quantified and consistent manner, achieved after review, validate and refine the current asset health index assignment process.
  3. A data-driven deterioration model and compare it with National Grid’s existing deterioration model. At the same time a test trial for a number of models that are used in the infrastructure industry.
  4. Case studies for the interactions of the models and the investment strategies, taking into consideration of cost, risk, performance, as well as process complications and regulatory implications. This will also include insights and recommendations to National Grid’s existing process and strategies.
  5. A comprehensive final report to summarise the findings and recommendations, as well as data and modelling details.
  6. A project database contains the integrated, cleansed source data and the results of the analyses.

The objectives of the project is to develop data driven models and draw new insights into the overhead line fittings system, in order to deliver values to both National Grid asset management business and the UK electricity consumers. The project will develop methodologies of enhanced level of utilisation of the condition information recorded by National Grid at the span level and component level, which is a valuable dataset of both research interest and long term planning. This project will also examine the impact of the new understandings to the asset management practices in terms of cost, risk, performance, regulation, as well as process complexities.

As a research project, this project will take an exploratory approach using modelling and data science. The following criteria are expected to be met at the end of the project:

  • Increased utilisation of the more granulised and up-to-date information from the innovative condition assessment methods for overhead line fittings.
  • Increased/quantitative understandings of the overhead line fittings system, including end of life, condition/AHI classification, and future degradation behaviours.
  • Increased transparency and consistency in the asset health assignment process and investment strategies.
  • Quantitative models developed to understand the above and also can be used for National Grid’s asset management practices.
  • Optimised solutions based on studies on the interactions/optioneerings among different investment strategies, by taking into consideration of key investment drivers, and engaging with key stakeholders.