Improving asset management of the Belgian electrical grid with a data-driven approach

Published on: January 10, 2019

When Belgian electricity grid operator Elia embarked on its Asset Management Excellence program, the ambitions were very high. The company wanted to optimize the asset maintenance process, reduce OPEX, CAPEX and maintenance workload, and at the same time guarantee the safety and the quality of the grid. To have an accurate view on all its assets, and to be able to make data-driven decisions, Elia turned to Kapernikov for help.

Kapernikov helped Elia to obtain an accurate overview of the condition of all its network equipment and to anticipate any risk of failure. As data consultants, we applied several database analysis techniques to support the company with its asset management strategy.

Collect and consolidate data

First of all, Elia needed a complete overview of the condition of its vast network of assets. The data about the company’s assets was scattered across various systems and needed to be retrieved and consolidated.

First, we organized the asset data according to technology, age, function on the grid, failure history, and executed maintenance. To allow the data to be automatically updated, we created a data lake, a type of data repository that offers flexible data processing.

Predictive and conditional maintenance

After collecting, cleaning and linking all of the data, we developed data visualizations in order to have a good understanding of the asset network and its failures. With this knowledge, we were able to extend the lifetime of Elia’s power transformers by 20%. The data also gave us insight into the expected workload for the assets that needed to be replaced.

We used data mining techniques to enrich the forecasts for each piece of equipment, according to its lifetime condition, installation, maintenance, etc. This way, we were able to attribute a health index to each asset. This health index reflects the condition of this asset and is an important criterion for conditional maintenance. Applying conditional maintenance on the underground cable fleet will enable Elia to save several millions of euros in the coming years.

Supporting strategic decisions

A data-driven analysis of this scale supports Elia in its strategic decisions and allows the company to better manage and understand the risks of equipment failure. Possible incidents can be anticipated and avoided. It also allows Elia to plan and optimize the financial and human capital that is needed for asset maintenance and replacement.

We are proud to support a forward-looking company like Elia in its efforts to tackle future challenges. Kapernikov is looking forward to helping similar companies with large numbers of assets.