Optimisation de la consommation énergétique d'une ligne de métro automatique prenant en compte les aléas de trafic à l'aide d'outils d'intelligence artificielle

Abstract : In 2014, as part of the Climate Plan, EU member countries have committed to reduce by 27% their energy consumption. One of the main focal areas consists in increasing the energy efficiency of urban transports. This thesis aims to propose a methodology to reduce the energy consumption of automatic metro lines while integrating traffic disruptions that occur under normal operating conditions. The principle adopted in this work is to maximize the reuse of electrical energy generated during braking of the train, by other trains running on the line. First part is dedicated to the electrical modeling of an automatic metro line and development of methods to calculate power flows between trains and power substations. Then, optimization algorithms are introduced to perform optimization of the most influential operating parameters in an ideal configuration ignoring traffic fluctuations. Finally, a methodology based on learning simulation data is developed in order to achieve optimization of energy consumption integrating traffic disruptions in real time. This last part will thus purchase the objective to provide a decision support to determine optimal dwell times to be carried out by trains in each station, so as to maximize braking energy recovery.
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Jonathan Lesel. Optimisation de la consommation énergétique d'une ligne de métro automatique prenant en compte les aléas de trafic à l'aide d'outils d'intelligence artificielle. Energie électrique. Ecole nationale supérieure d'arts et métiers - ENSAM, 2016. Français. ⟨NNT : 2016ENAM0018⟩. ⟨tel-01344833⟩

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