Identification of magnetic deposits in 2-D axisymmetric eddy current models via shape optimization

International audienceThe non-destructive control of steam generators is an essential task for the safe and failure-free operation of nuclear power plants. Due to magnetite particles in the cooling water of the plants, a frequent source for failures are magnetic deposits in the cooling loop of steam generators. From eddy current signals measured inside a U-tube in the steam generator, we propose and analyze a regularized shape optimization algorithm to identify magnetic deposits outside the U-tube with either known or unknown physical properties. Motivated by the cylindrical geometry of the U-tubes we assume an axisymmetric problem setting, reducing Maxwell's equations to a 2-D elliptic eddy current problem. The feasibility of the proposed algorithms is illustrated via numerical examples demonstrating in particular the stability of the method with respect to noise

Time-Frequency characterisation for electric load monitoring

Electric utilities and consumers are increasingly interested in energy monitoring for economic and environmental reasons. A non-intrusive solution may rely on information extracted from the electric consumption measured at a centralized part of a distribution network. The problem at hands consists in the separation of the electric load into its major components. This problem of source separation from one sensor is quite tractable under certain conditions. In this work, the focus is made on the most consuming household appliance in France: the space-heating. It is a sum of an unknown number of pseudo-periodic signals embedded in the global active power. An unsupervised algorithm to determine the space-heating schedule from the global consumption based on the interpretation of the spaceheating signature in the time-frequency domain is proposed. The proposed method conjoins a time-frequency detector and a frequent itemsets extraction. First results on real data are quite satisfying

RJMCMC point process sampler for single sensor source separation : an application to electric load monitoring

This paper presents an original method to separate the residential electric load into its major components. The method is explained in the particular case of space-heating, which is the most consuming electric end-use in France1. This is a source separation problem from a single mixture. The components to be retrieved are square signals characterized by a periodic regulation and a slowly timevarying duty cycles. A point process is used to model the electric load as a configuration of possibly overlapping square signals, given the priors on magnitude, duty cycle variations and the regulation periodicity. This stochastic process is simulated using a Reversible Jump Markov Chain Monte Carlo procedure. A simulated annealing scheme is used to achieve the posterior density maximization. First results on real data provided by Electricité de France are quite encouraging

Caractérisation aveugle de la courbe de charge électrique : Détection, classification et estimation des usages dans les secteurs résidentiel et tertiaire

Residential and tertiary appliances characterization in real conditions from the unique measurement available at the utility service entry (the active and/or reactive power) has been little studied. This thesis investigates new methods and approaches to an entirely non-intrusive characterization of electric appliances. Our aim is to extract several descriptors of the targeted end-uses, given one source mixture of an unknown number of non-stationary signals. This study emphasizes four areas: appliances detection, classification and estimation (consumed energy, magnitude) and the electric load decomposition problem. The proposed techniques are demonstrated with real data including an experimental house and two “real” houses. One our major contribution is a non-intrusive solution of a residential electric load segmentation and mapping the daily consumed energy into its major components (space-heating by convectors, water heater and refrigerators). Ameliorations of some algorithms and their evaluation on large real data are required in order to evaluate the robustness of the proposed methods. As future works, we detail a generic approach using a probabilistic model of the electric load events which addresses the problem of the electric load decomposition (sources separation) in the framework of Bayesian approaches.Le problème de caractérisation non-intrusive des usages dans des conditions réelles à partir de l'unique observation de la courbe de charge (CdC) générale résidentielle et tertiaire disponible (puissance moyenne quotidienne disponible en sortie du compteur) a été peu étudié. L'objectif de la thèse est d'explorer les possibilités et les méthodes permettant une caractérisation aveugle des composantes du mélange observé. Plus précisément, il s'agit d'extraire des descripteurs temporels, énergétiques ou encore événementiels à partir d'un mélange unique de sources non-stationnaires de nombre inconnu. Nous considérons quatre sous-problématiques sous-jacentes à la caractérisation de la CdC : la détection des usages, la classification des signaux de la CdC, l'estimation des paramètres des usages (énergie, amplitude, etc.) et la séparation des sources du mélange observé. Les algorithmes mis en œuvre sont évalués sur les données réelles. Les performances obtenues sont satisfaisantes. Outre les contributions de formalisation et algorithmiques, un des apports marquants de cette étude est une solution non-intrusive pour la segmentation automatique la CdC générale résidentielle et pour la cartographie de l'énergie quotidienne consommée en quatre composantes : le chauffage électrique, le chauffe-eau, le froid alimentaire et les autres usages. Au delà des améliorations algorithmiques et de l'étude de performance à poursuivre, nous proposons en perspective de cette thèse une approche générique pour décomposer une CdC quelconque fondée sur une modélisation stochastique de la série des événements de la CdC

Eddy current tomography of deposits in steam generator

International audienc

Modèle polynomial par morceaux muni de transitions régulières - Application à la modélisation de signaux transitoires électriques

National audienceA smooth transition model is introduced and studied. Such a model extend piecewise regression ones by introducing smooth transition functions achieving transition from a segment to another in the neighborhood of each rupture. The joint estimation of the parameters and of the number of segment is an ill-posed problem. A regularization is performed through a hierarchical Bayesian framework. As standard Bayesian estimates can not be computed analytically, a reversible-jump MCMC algorithm is derived to sample the parameters according to their posterior distribution. This method is applied to the modeling of real-world electrical transient

Stochastic model for blind source separation of electrical uses from a single sensor

Une méthode d'estimation des composantes élémentaires de la consommation quotidienne du chauffage électrique (puissance active) est proposée. Le problème générique est celui de la séparation aveugle de sources non-stationnaires de nombre de composantes inconnu, à partir d'une seule observation. Ce problème a fait l'objet de nombreuses études depuis les années 2000. Les méthodes proposées dans la littérature sont principalement des méthodes à base de dictionnaire. Elles s'appuient sur la parcimonie des sources dans un domaine judicieusement choisi [1, 2, 3]. La décomposition de la courbe de charge électrique présente deux particularités qui justifient l'introduction d'une méthode dédiée en s'inspirant des travaux antérieurs. En effet, des sources similaires, par exemple des convecteurs, peuvent se superposer localement ou sur tout le domaine d'observation. De plus, les composantes du mélange présentent des caractéristiques morphologiques qui permettent de les discriminer. Mais, les composantes d'une même classe d'appareils (chauffage par exemple) ont la même forme. La méthode de séparation de sources proposée utilise un modèle probabiliste du mélange, dont les primitives sont définies à partir d'un dictionnaire de formes (temporelles) pré-établies. Un échantillonneur du type MCMC utilisé permet d'obtenir une solution au sens du maximum a posteriori. Le modèle est détaillé dans le cas du chauffage électrique dont les composantes sont des sources localement stationnaires et de même forme. Il sera généralisé à d'autres composantes

Bayesian curve fitting for transient signals by using smooth transition regression models

International audienceThis communication addresses the problem of fitting time series with smooth transition regression models. These models are of interest to characterize transient signals in the context of system monitoring and diagnosis. Within this modelling, time series are segmented by sequences of piecewise constant polynomial regression models. Moreover, smooth transitions between each segment are obtained by introducing some smooth, monotically increasing parametric transition functions. It allows one to give a synthetic representation of signals composed by smooth transitions between different regimes. However, the estimation of the parameters of these models appears to be an ill-posed problem. Direct optimization algorithms are not robust enough with regard to the initial parameters guess. Therefore, to achieve parameter estimation, we introduce a Bayesian framework. Appropriate priors for the unknown model parameters are introduced to penalize a data-driven criterion built from the likelihood of the observations. As the resulting posterior probability distributions does not admit closed-form analytical expressions, Markov Chain Monte Carlo (MCMC) sampling methods are derived to obtain the standard Bayesian estimators of the model parameters. Results are shown for synthetic and real appliance load monitoring data