General information
Organisation
The French Alternative Energies and Atomic Energy Commission (CEA) is a key player in research, development and innovation in four main areas :
• defence and security,
• nuclear energy (fission and fusion),
• technological research for industry,
• fundamental research in the physical sciences and life sciences.
Drawing on its widely acknowledged expertise, and thanks to its 16000 technicians, engineers, researchers and staff, the CEA actively participates in collaborative projects with a large number of academic and industrial partners.
The CEA is established in ten centers spread throughout France
Reference
SL-DRT-24-0368
Direction
DRT
Thesis topic details
Category
Technological challenges
Thesis topics
Topology reconstruction of a ramified network by multisensor reflectometry
Contract
Thèse
Job description
Smart Grids aim at monitoring and controlling electric power networks. Many parameters have to be monitored such as production and consumption units, and the integrity of the structure of the interconnection netwotk itself.
Smart grids aim at enhancing the quality of service while protecting people and infrastructures. In this area of research, most algorithms are deployed for taking the human out of the retroaction loops in order to maximize the availability and the reactivity. For that reason, artificial intelligence based algorithms are increasingly incorporated in decision loops.
In that industrial context, we are interested in methods that aim at estimating electrical network topologies. The topology of a network includes the length of the cable lines and their electrical properties, so as the characterictics of the loads that are connected to the networks (production and consumption units), and also potential faults in the network. In the end, the accurate estimation of the topology may be used to monitor the network with more accuracy with the help of a more accurate a priori information.
In order to characterize the topology, we propose to deploy either a single or a distributed set of electric reflectometers. These devices inject signals in the network under test and the study of the reflections gives information back which can be used to reveal the structure of the network. More precisely, every impedance discontinuity along the line wil cause partial reflections of the waves.
Previous works were conducted by our team of researchers on that topology reconstruction topic, by exploiting optimization algorithms coupled to a simulator. We would like to extend these works in two directions. First, we would like to explore a machine learning regressor-based approach in a mono sensor version. Second, we would like to estimate the topology by combining the measurements from multiple sensors, either with already available optimization-based approachs, or by the new machine learning-based approach.
University / doctoral school
Sciences et Technologies de l’Information et de la Communication (STIC)
Paris-Saclay
Thesis topic location
Site
Saclay
Requester
Position start date
01/09/2024
Person to be contacted by the applicant
CARTRON Mickaël mickael.cartron@cea.fr
CEA
DRT/DIN//LIIDE
CEA Saclay
Batiment 565 - Pièce 2061 - PC192
91191 Gif sur Yvette cedex
01 69 08 87 43
Tutor / Responsible thesis director
SOULOUMIAC Antoine antoine.souloumiac@cea.fr
CEA
DRT/DIN//LIIDE
Bât. 565, pièce 2043 - PC192
CEA/SACLAY
01 69 08 49 76
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