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Université de Grenoble

1. Cavallera, Didier. Contribution au renvoi de tension et à la reconstitution du réseau. Identification des paramètres d'un réseau. Estimation des flux rémanents dans un transformateur : Contribution to the power plant re-energization and the network restoration. Parameters identification of a network. Estimation of the residual flux in a transformer.

Degree: Docteur es, Sciences et technologie industrielles, 2011, Université de Grenoble

Lors de la réalimentation des auxiliaires d’une tranche nucléaire, l’étape la plus à risque est la remise sous tension brusque du transformateur à cause des surtensions. Dans un soucis perpétuel d’amélioration de la modélisation de ces transitoires, les modèles et techniques doivent évoluer. Le but de ces travaux de thèse est de proposer de nouvelles méthodologies permettant d’évaluer les paramètres mal connus de la modélisation. Lors des mises sous tension des lignes électriques, une méthodologie s’appuyant sur l’identification (optimisation ou techniques issues du traitement du signal) permet de déterminer les paramètres variables de la modélisation. Cependant, un des paramètres influents pour les surtensions est le flux rémanent. Face aux problèmes actuels rencontrés pour l’estimer (méthode non directe, dérive, …), une nouvelle méthode basée sur la mesure des flux de fuites du circuit magnétique est proposée. Des mesures réelles utilisant des capteurs de technologie « fluxgate » sont réalisées et permettent d’estimer le flux rémanent.

During the re-energization of the auxiliaries of a nuclear power plant, the more dangerous step is the re-energization of the power transformer, because of the temporary overvoltages. In order to improve the transient modeling, models and techniques may be improved. The purpose of this thesis is to suggest new methodologies to identify the uncertain parameters of the model. When the line re-energization occurs, an identification methodology (optimization or methods using signal processing) allows determining the model variable parameters. However, one of the most important parameters in the overvoltages is the residual flux. Given the actual problems found on estimation strategies (no direct method, derivation,…), a new method established for the leakage flux measurement of the magnetic circuit is proposed. Real measurements using « fluxgate » technology sensors were realized, permitting to estimate the residual flux.

Advisors/Committee Members: Coulomb, Jean-Louis (thesis director).

Subjects/Keywords: Réseau électrique de transport; Transformateur électrique; Renvoi de tension; Reconstitution du réseau; Enclenchement de transformateur; Enclenchement de ligne; Flux rémanent; Mesure de champ magnétique; Flux de fuite magnétique; Circuit magnétique; Commande synchronisée de disjoncteur; Electrical transport network; Electric transformer; Power plant re-energization; Network restoration; Transformer switching; Line switching; Residual flux; Magnetic field measurement; Magnetic leakage flux; Magnetic circuit; Synchronized switching controller for circuit breaker

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Cavallera, D. (2011). Contribution au renvoi de tension et à la reconstitution du réseau. Identification des paramètres d'un réseau. Estimation des flux rémanents dans un transformateur : Contribution to the power plant re-energization and the network restoration. Parameters identification of a network. Estimation of the residual flux in a transformer. (Doctoral Dissertation). Université de Grenoble. Retrieved from http://www.theses.fr/2011GRENT121

Chicago Manual of Style (16th Edition):

Cavallera, Didier. “Contribution au renvoi de tension et à la reconstitution du réseau. Identification des paramètres d'un réseau. Estimation des flux rémanents dans un transformateur : Contribution to the power plant re-energization and the network restoration. Parameters identification of a network. Estimation of the residual flux in a transformer.” 2011. Doctoral Dissertation, Université de Grenoble. Accessed January 19, 2020. http://www.theses.fr/2011GRENT121.

MLA Handbook (7th Edition):

Cavallera, Didier. “Contribution au renvoi de tension et à la reconstitution du réseau. Identification des paramètres d'un réseau. Estimation des flux rémanents dans un transformateur : Contribution to the power plant re-energization and the network restoration. Parameters identification of a network. Estimation of the residual flux in a transformer.” 2011. Web. 19 Jan 2020.

Vancouver:

Cavallera D. Contribution au renvoi de tension et à la reconstitution du réseau. Identification des paramètres d'un réseau. Estimation des flux rémanents dans un transformateur : Contribution to the power plant re-energization and the network restoration. Parameters identification of a network. Estimation of the residual flux in a transformer. [Internet] [Doctoral dissertation]. Université de Grenoble; 2011. [cited 2020 Jan 19]. Available from: http://www.theses.fr/2011GRENT121.

Council of Science Editors:

Cavallera D. Contribution au renvoi de tension et à la reconstitution du réseau. Identification des paramètres d'un réseau. Estimation des flux rémanents dans un transformateur : Contribution to the power plant re-energization and the network restoration. Parameters identification of a network. Estimation of the residual flux in a transformer. [Doctoral Dissertation]. Université de Grenoble; 2011. Available from: http://www.theses.fr/2011GRENT121

2. Lee, Jihang. Controlling Ionic Transport in RRAM for Memory and Neuromorphic Computing Applications.

Degree: PhD, Materials Science and Engineering, 2018, University of Michigan

Resistive random-access memory, based on a simple two-terminal device structure, has attracted tremendous interest recently for applications ranging from non-volatile data storage to neuromorphic computing. Resistive switching (RS) effects in RRAM devices originate from internal, microscopic ionic migration and the associated electrochemical processes which modify the materials’ chemical composition and subsequently their electrical and other physical properties. Therefore, controlling the internal ionic transport and redox reaction processes, ideally at the atomic scale, is necessary to optimize the device performance for practical applications with large-size arrays. In this thesis we present our efforts in understanding and controlling the ionic processes in RRAM devices. This thesis presents a comprehensive study on the fundamental understanding on physical mechanism of the ionic processes and the optimization of materials and device structures to achieve desirable device performance based on theoretical calculations and experimental engineering. First, I investigate the electronic structure of Ta2O5 polymorphs, a resistive switching material, and the formation and interaction of oxygen vacancies in amorphous Ta2O5, an important mobile defect responsible for the resistive switching process, using first-principles calculations. Based on the understanding of the fundamental properties of the switching material and the defect, we perform detailed theoretical and experimental analyses that reveal the dynamic vacancy charge transition processes, further helping the design and optimization of the oxide-based RRAM devices. Next, we develop a novel structure including engineered nanoporous graphene to control the internal ionic transport and redox reaction processes at the atomic level, leading to improved device performance. We demonstrate that the RS characteristics can be systematically tuned by inserting a graphene layer with engineered nanopores at a vacancy-exchange interface. The amount of vacancies injected in the switching layer and the size of the conducting filaments can be effectively controlled by the graphene layer working as an atomically-thin ion-blocking material in which ionic transports/reactions are allowed only through the engineered nanosized openings. Lastly, better incremental switching characteristics with improved linearity are obtained through optimization of the switching material density. These improvements allow us to build RRAM crossbar networks for data clustering analysis through unsupervised, online learning in both neuromorphic applications and arithmetic applications in which accurate vector-matrix multiplications are required. We expect the optimization approaches and the optimized devices can be used in other machine learning and arithmetic computing systems, and broaden the range of problems RRAM based network can solve. Advisors/Committee Members: Kioupakis, Emmanouil (committee member), Lu, Wei (committee member), Zhong, Zhaohui (committee member), Poudeu-Poudeu, Pierre Ferdinand (committee member).

Subjects/Keywords: Resistive Random Access Memory (RRAM); Memristor; Neuromorphic Computing; Transition Metal Oxide; Ionic Transport; Artificial Neural Network; Electrical Engineering; Engineering (General); Materials Science and Engineering; Engineering

…6 Figure 1.6: Schematic illustration showing how neural network can be mapped onto a… …vii Figure 1.7: Schematic illustration of an artificial neural network based on a RRAM… …Electrons transport by getting trapped and detrapped between different defect sites and the ix… …Optical-microscope image of the device illuminated by a green-light laser during electrical… …oxygen ions only transport through a nanopore created in the graphene layer, forming a CF with… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Lee, J. (2018). Controlling Ionic Transport in RRAM for Memory and Neuromorphic Computing Applications. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/146119

Chicago Manual of Style (16th Edition):

Lee, Jihang. “Controlling Ionic Transport in RRAM for Memory and Neuromorphic Computing Applications.” 2018. Doctoral Dissertation, University of Michigan. Accessed January 19, 2020. http://hdl.handle.net/2027.42/146119.

MLA Handbook (7th Edition):

Lee, Jihang. “Controlling Ionic Transport in RRAM for Memory and Neuromorphic Computing Applications.” 2018. Web. 19 Jan 2020.

Vancouver:

Lee J. Controlling Ionic Transport in RRAM for Memory and Neuromorphic Computing Applications. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2020 Jan 19]. Available from: http://hdl.handle.net/2027.42/146119.

Council of Science Editors:

Lee J. Controlling Ionic Transport in RRAM for Memory and Neuromorphic Computing Applications. [Doctoral Dissertation]. University of Michigan; 2018. Available from: http://hdl.handle.net/2027.42/146119

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