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Attribué Procédure négociée Réf: 418557-2026

Network inspection automation

description Objet du marché

RTE is the French transmission system operator. As a public utility company, its mission is to operate, maintain and develop the network of high and ultra-high voltage lines. RTE must guarantee the proper operation and safety of the electricity network, while ensuring free and fair access to all users of this network. It transports electricity between power suppliers (French and European) and consumers, whether they are electricity distributors or manufacturers directly connected to the transmission network. As part of the R&D Asset Management Programme, the Network Inspection Automation roadmap is working on improving the efficiency of network inspection with the use of automated data acquisition and analysis. To achieve its objectives, RTE defined 2 work package : Work package1 - data acquisition with helicopters & Work package 2 - automated detection of equipment and anomalies.

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view_agenda Allotissement (2 lots)

01 Work package 1 - Data acquisition with helicopters Actif expand_more
Code CPV
tag 73000000 — Services de recherche et développement et services de conseil connexes
schedule Durée : 36 mois

Work package 1 - Data acquisition with helicopters aims to cover a wide range of needs in terms of data acquisition to foster the development of automated inspection
solutions:
- Conduct acquisition flights above power lines or substations with helicopters to acquire a wide range of
data: photographs, LiDAR data, IR, IV, videos, 360° photo, etc.
- Test different flight configurations to acquire…

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02 Work package 2 - Automated detection of equipment and anomalies Actif expand_more
Code CPV
tag 73000000 — Services de recherche et développement et services de conseil connexes
schedule Durée : 36 mois

Work package 2 - Automated detection of equipment and anomalies:
-Developing specific machine learning models to meet RTE's needs (anomaly detection, equipment recognition, 360° photos/videos, LIDAR data processing);
-Increasing the maturity of AI data processing: data volumes, infrastructures, model training and testing, potential drifts, etc.;
-Provide input for the development of a data/AI ind…

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