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MAP/INF630 (3 ECTS): Students will work half a day a week on a transverse project, ie. a case study corresponding to a challenging question either raised by an industrial partner or by a researcher in the domain spanned by the graduate degree.
Presentation of transverse projects will occur on 25th September at 2pm in Alan Turing building (on Polytechnique campus) in room Sophie Germain.
Enedis - contact Frédéric Boutaud frederic.boutaud@enedis.fr
Numerous pictures campaigns had been organized nationwide by Enedis inside secondary substations in order to collect informations about material (manufacturer, model….). During this summer, the first version of an algorithm have been developed to identify texts in label printings (OCR processing) for hight voltage cell unit, LV switchboard and fault detector. The aim of this project is to : - Identify if AI can permess us to identify label printings of transformers (more complexe than others materials). - Set up a deep learning process to classify a big amont of pictures - Join a transverse task force organised arround this topic
Context. Enedis is deploying Linky communicating meter and the program will be ended by 2022. More than 13 million meters are yet installed. Data recorded in each Linky meter provide valuable information about events which appear on electricity grid, which is a new opportunity for Enedis. On the other hand, failures affect the low voltage network leading to power outages, about 40000 a year.
Problem. The problem is the following: how to take advantage of new available data (Linky meter data, weather, network load,…) to detect anomalies on the electrical network and avoid power outages ? In other words, how to develop predictive maintenance to optimize our resources?
Description. The idea is to use data registered by Linky meter (short outages, voltage excursions, surges) and other data considered relevant which need to be identified, in order to build an algorithm based on Artificial Intelligence (AI) allowing to predict and characterize failures. It comes specifically to search correlations between data available and network outages to define failures “signatures”. These signatures would be so recognized by the algorithm and would lead to recommendations for action on the field to correct the anomaly before the incident. More precisely, the study will focus on: - Quick benchmark of use of AI to predict failures in other electrical companies - Critical analysis of the Enedis works undertaken on the subject (method, algorithm,…) - Research of new correlations between some types of failures and data available - Design of an algorithm or improve existing algorithm for predicting failures The student would work in collaboration with Enedis data scientist who started a study on the subject. The student would work physically on the Enedis site of Nanterre with data scientist during the phase of data analysis and data processing (sensitive data). For other part of the study or to analyse non-sensitive data, he will have the possibility to work at Polytechnique. Depending on the first results, this study could lead to a project of several months in order to continue works engaged.
But : apprécier comment l'IA pourrait prendre en compte le volet “émotionnel” des appels clients reçus sur notre Centre d'Appel Dépannage. Des enregistrements réels seront utilisés pour l'apprentissage. Le premier volet de se projet consistera en un état de l'art sur le sujet pour évaluer une première faisabilité sur le cas d'usage proposé.
Google contact Damien Henry damienhenry@google.com, project Google Arts and Culture.
Idemia contact Stéphane Gentric stephane.gentric@idemia.com
Ynsect (start-up; not official partners yet) contact Arturo Escaroz Cetina arturo.escarozcetina@ynsect.com