Topology reconstruction of a ramified network by multisensor reflectometry

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.