National Grid Overhead Line Fittings

04 September 2019
Image of overhead lines
In a complex problem we developed a hybrid approach combining statistical inferences from data together with expert judgement. This provided transparency, robustness and reproducibility, giving engineers tools to identify optimal interventions with a high degree of detail.


National Grid is moving towards a data-driven, whole-lifecycle based approach to manage its electricity transmission assets. To realise this change, National Grid required a data driven asset degradation and failure model for overhead line (OHL) fittings to improve the targeting and planning of maintenance and renewal activities. In delivering this, we were required to overcome several challenges: complex asset hierarchies (linear and non-linear assets), sparse historical data, a complex operational environment and diverse asset intervention options.


After extensive engagement with National Grid stakeholders we developed a statistical model for the degradation and failure of individual assets across the transmission network. This related the risk of failure to asset condition, including accounting for possible mis-categorisation of asset condition in manual reviews. We developed a framework for incorporating engineering judgement alongside model outputs to quantify uncertainty in forecasts of asset failure for robust, risk-based decision-making.

We developed a data-pipeline to automatically cleanse and transform inputs and calibrate modelled degradation rates, mis-categorisation probabilities and condition-driven defect probabilities. This framework enabled continuous improvement of the model providing a basis for safe, robustly evidenced refinement of maintenance policy.


Our work provided a significant enhancement to existing health assessment processes. By incorporating the outputs from our statistical analysis into the decision-making process,  we were able to develop a robust quantified framework for calculating current and future asset conditions. This delivers significant financial value by identifying superior intervention options based on improved knowledge of the cost and risks. This will translate into consumer benefits in affordability and reliability of the electricity network.