Supervisory hybrid model predictive control for voltage stability of power networks

International audienceEmergency voltage control problems in electric power networks have stimulated the interest for the implementation of online optimal control techniques. Briefly stated, voltage instability stems from the attempt of load dynamics to restore power consumption beyond the capability of the transmission and generation system. Typically, this situation occurs after the outage of one or more components in the network, such that the system cannot satisfy the load demand with the given inputs at a physically sustainable voltage profile. For a particular network, a supervisory control strategy based on model predictive control is proposed, which provides at discrete time steps inputs and set-points to lower-layer primary controllers based on the predicted behavior of a model featuring hybrid dynamics of the loads and the generation system

Commande prédictive des systèmes PWA avec entrées mixtes

International audienc

Modeling and Control of an Aluminium Strip Unwinder-Rewinder

International audienc

Adaptive predictive approach for emergency voltage control of power networks

International audienc

Approche hybride pour la commande prédictive en tension d'un réseau d'énergie électrique

Dans le cadre d'une approche quasi-statique pour les réseaux électriques, des modèles dynamiques non-linéaires permettent de reproduire des phénomènes tels que des écroulements de tension. La présence de variables à valeurs continues et discrètes dans les équations confèrent aux modèles un caractère hybride qui doit être pris en compte dans les méthodes. Pour une approche hybride des réseaux électriques, nous avons proposé une méthode de commande prédictive hybride basée sur un modèle de prédiction affine par morceaux (PWA) qui permet de prévoir le comportement du système dans un futur proche afin de choisir la meilleure action à mener (au sens d'un critère donné). Le modèle de prédiction est obtenu en procédant à une linéarisation formelle des équations non-linéaires. Le caractère hybride se retrouve dans le problème d'optimisation associé à la commande prédictive et nous avons proposé un algorithme d'optimisation mixte par énumération partielle dédié à la commande prédictive des systèmes de la classe PWA. Des résultats ont été obtenus et présentés pour deux réseaux d'études à 4 et 9 nœuds.RENNES1-BU Sciences Philo (352382102) / SudocSudocFranceF

Fast estimation of pollution sources in urban areas using a 3D LS-RBF-FD approach

International audienceSource Term Estimation (STE) is a field of growinginterest in the context of air pollution, both for peopleliving in urban areas and for decision makers. Thus retrievingmaps of sources of pollution in an urban context is a necessity.Since urban pollution mainly depends on car trafficconditions, it is important to develop fast estimation methodsto quickly and enough accurately identify highly-pollutingvehicles. The challenge is high since the problem requires theinversion of distributed models defined on a 3D heterogeneousdomain including complex obstacles. This paper proposes anestimation method based on a flexible Least Squares-RadialBasis Function-Finite Difference (LS-RBF-FD) reduced modelof a advection-diffusion PDE on 3D heterogeneous domainsrepresenting complex urban areas. The STE problem is solvedby using an adjoint-based method relying on the reducedmodel to effectively estimate pollutant sources given a limitednumber of measurements. The paper provides preliminaryresults demonstrating the potential of the proposed approach

A RBF-FD physics-informed machine learning approach to air pollution source estimation

International audienceIn this paper, we propose a source term estimation approach for air pollution monitoring based on a physics-informed machine learning approach using radial basis function- generated finite differences (RBF-FD) approximations, rather than using neural network-based approximations. This approach looks promising for detecting a static pollution source, at a particularly low computing cost and based on a network of fixed or mobile sensors. A 3D case study demonstrates the effectiveness of the approach

Source term estimation: variational method versus machine learning applied to urban air pollution

International audienceSource detection is a field of study gaining interest due to environmental concerns about air quality in populated areas. We developed a machine learning framework inspired by previous works on road traffic estimation, and compared it to a classical variational method under a unidimensional and stationary problem. We tested source reconstruction with datasets coming from 12 and 50 sensors with and without noise. Noise was set to follow a gaussian law with a dependent variance from the maximum measured value of a concentration profile. Both methods are reasonably robust to noise. The results reveal that the Neural Network used here, a multilayer perceptron, performs very well compared to the classical 3D-Var method

Hazardous Atmospheric Dispersion in Urban Areas: a Deep Learning Approach for Emergency Pollution Forecast

International audienceToday, Computational Fluid Dynamics approaches have a high level of spatial/temporalaccuracy in modelling atmospheric transport and dispersion in very complex environments.Several numerical models require, however, heavy computational resourcesand prolonged simulation time up to several days. This time constraint is specificallycrucial for intervention planning in case of accidental or malevolent toxic releasesin a city. In this paper, we propose to use synthetic data generated by a realistic 3-D transport/dispersion simulator, to train a learning framework called MCxM. Thelatter relies on a sequence of masking and correction operations to progressivelyapply the spatial constraints and underlying physics of transport and dispersion. Thelearning phase uses the urban geometry of the French city Grenoble.We then test theeffectiveness of the trained MCxM in a different French city: Paris. The results showthat the MCxMs forecasts are virtually instantaneous and generalize successfully tounseen conditions