Customer: RTE France

Electrical utilities around the world are dealing with aging infrastructure. Time based inspection and maintenance cycles are scheduled in order to maximize the usable lifetimes of each asset. Overhead lines, particularly when located in remote areas, are costly to inspect, making asset management difficult and time consuming. For older routes or newly acquired lines, data on the condition of assets is critical but often missing.

Digital Engineering (DE), in partnership with a number of major utilities, has developed an asset health assessment methodology that provides asset condition data in less time and at a reduced expense when compared to traditional asset health assessments.


With nearly 105,000 km of lines in France, RTE’s Transmission Grid is the biggest in Europe. With such a complex transmission system, it is a challenge to understand which assets need to be inspected and/or replaced immediately and which can remain functional for longer.

RTE was interested to see if Digital Engineering’s asset condition assessment could provide accurate condition data routes in its overhead line network.

If RTE could gain a better understanding of the expected condition on a span-by-span basis, it would be able to better optimize its asset management decisions and expenditure.


RTE engaged DE to assess two overhead line routes, as a part of a pilot project, to validate the accuracy and quality of its asset health assessments.

DE started by running its wind-induced wear model, taking into consideration wind speed and direction, turbulence intensity, conductor icing as well as span and conductor configuration. RTE simply provided the positional and mechanical data for the spans to be included in the study. All other data required for the model, for example concerning weather and topography, was collated and processed by DE.

Wind induced wear

In order to provide crucial, span-specific weather information that is not provided by its numerical weather prediction model, DE considered local terrain features such as ground elevation and land-use (e.g. trees and buildings). 

DE used these data sets as input for its aeolian vibration model. One of the routes in this study exhibited a much higher risk of aeolian vibration due to the relatively exposed terrain in the area surrounding the route.


DE also ran its proprietary corrosion model which is based on machine learning techniques and is trained using records of relevant weather variables and pollutants throughout Europe. It takes into consideration weather effects which transport pollutants onto assets. It also looks for the warm and humid ambient weather conditions which accelerate metal corrosion.

Some pollutants stay relatively stable over time, such as atmospheric salinity. For other pollutants, DE considered the most recent and accurate records available. Several spans were identified with a high likelihood for deterioration due to corrosion, particularly on one of the selected lines


After presenting the results of the study, RTE provided DE with defect data from the past couple of years for the two lines. They were categorized as either wind-related, corrosion-related or both.

The model outputs were compared to historical defect records at each line and the recorded defects were aligned with DE’s asset health model predictions for both wind and corrosion.

June 22, 2020

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