Relevant bibliographies by topics / Grid Optimization Method

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  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Grid Optimization Method'

Author: Grafiati
Published: 28 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Grid Optimization Method"

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Miura, Hiroaki, and Masahide Kimoto. "A Comparison of Grid Quality of Optimized Spherical Hexagonal–Pentagonal Geodesic Grids." Monthly Weather Review 133, no. 10 (October 1, 2005): 2817–33. http://dx.doi.org/10.1175/mwr2991.1.

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Abstract Construction and optimization methods of spherical hexagonal–pentagonal geodesic grids are investigated. The objective is to compare grid structures on common ground. The distinction between two types of hexagonal–pentagonal grids is made. Three conventional grid optimization methods are summarized. In addition, three new optimization methods are proposed. Six desirable conditions for an ideal grid are described, and the grid optimization methods are organized in view of such conditions. Interval uniformity, area uniformity, isotropy, and bisection of cell faces are systematically investigated for optimized grids. There are compensations of preferable grid features in each optimization method, and an optimal method cannot be decided based only on the research of grid features. It is suggested that grid optimization methods should be selected based on research of numerical schemes.
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Li, Dong Liang, Xiao Feng Zhang, Ming Zhong Qiao, and Gang Cheng. "A Short-Term Load Forecasting Method of Warship Based on PSO-SVM Method." Applied Mechanics and Materials 127 (October 2011): 569–74. http://dx.doi.org/10.4028/www.scientific.net/amm.127.569.

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The power load characteristics of warship on a specific task was analyzed,and a task-based forecasting method for warship short-term load forecasting was presented. the new influencing factors of warship power load were used in modeling which is different with the land grid and civilian vessels grid. Theory of particle swarm optimization and Support vector machine was disscused first, and the method of particle swarm optimization was improved to have the ability of adaptive parameter optimization. and the method of support vector machine was improved by the adaptive PSO optimizational method. then a new adaptive short-term load forecasting model was established by the adaptive PSO-SVM method. finally Through simulation results show that the adaptive PSO-SVM method is highly feasible to predict with high accuracy and high generalization capability.
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Wang, Cheng. "Optimization of SVM Method with RBF Kernel." Applied Mechanics and Materials 496-500 (January 2014): 2306–10. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.2306.

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Usually there is no a uniform model to the choice of SVMs kernel function and its parameters for SVM. This paper presents a bilinear grid search method for the purpose of getting the parameter of SVM with RBF kernel, with the approach of combining grid search with bilinear search. Experiment results show that the proposed bilinear grid search has combined both the advantage of moderate training quantity by the bilinear search and of high predict accuracy by the grid search.
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Song, Ying Wei, Jian Liu, Liao Yi Ning, Zhen Tao Han, Hong Liu, and Shi Ju Wang. "Comprehensive Assessment System and Method of Smart Distribution Grid." Advanced Materials Research 860-863 (December 2013): 1901–8. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.1901.

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According to key values and demands of smart distribution grids in China, a smart distribution grid comprehensive assessment index system and method is established. The index system is divided into the demand index set and assessment index set. As the former represents the macroscopic features while the latter represents the microscopic factors of the smart distribution grid. A causal connection exists between them. Also a hierarchical optimization model with combination weight, which is based on the DEMATEL-ANP-anti-entropy weight method and the improved Grey incidence approach, is proposed. The DEMATEL-ANP-anti-entropy weight method is used for demand indices weight analysis, while the improved Grey incidence approach is put forward for the evaluation on assessment indices. Results of applying the proposed method in practical case shows that the proposed method is valuable in following aspects: scientifically assessing the intelligent development level and effectiveness of distribution grid, analyzing and recognizing the grids operation condition and weak link, giving reference to planning and construction of smart distribution grid, and so on. Index Terms Smart distribution grid; comprehensive assessment; index system; combination weight; hierarchical optimization.
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Ingber, Marc S., and Ambar K. Mitra. "Grid optimization for the boundary element method." International Journal for Numerical Methods in Engineering 23, no. 11 (November 1986): 2121–36. http://dx.doi.org/10.1002/nme.1620231110.

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Qian, Lin, Dong Hui Li, Xiao Zhi Wu, Guang Xin Zhu, and Jiang Hui Liu. "Performance Optimization Method on Smart Grid Information Platform." Advanced Materials Research 765-767 (September 2013): 1041–45. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.1041.

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In recent years, with the development of economics, Chinese government feels obliged to build a strong smart grid, state grid company starts the construction process of SG-ERP system comprehensively. However, system performance bottleneck gradually has been spotted due to expanding of IT systems. So it has important theoretical value and strong practical significance to do research on smart grid information platform. This paper proposed a framework of performance tuning for large transaction database used in smart grid based on state grid information platform construction, this method will break system performance bottlenecks, improving the service quality of the platform. The results show that the proposed method can greatly increase the quality of system running, and provide a reliable guarantee for the running in strong smart grid.
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Cui, Pengcheng, Bin Li, Jing Tang, Jiangtao Chen, and Youqi Deng. "A modified adjoint-based grid adaptation and error correction method for unstructured grid." Modern Physics Letters B 32, no. 12n13 (May 10, 2018): 1840020. http://dx.doi.org/10.1142/s0217984918400201.

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Grid adaptation is an important strategy to improve the accuracy of output functions (e.g. drag, lift, etc.) in computational fluid dynamics (CFD) analysis and design applications. This paper presents a modified robust grid adaptation and error correction method for reducing simulation errors in integral outputs. The procedure is based on discrete adjoint optimization theory in which the estimated global error of output functions can be directly related to the local residual error. According to this relationship, local residual error contribution can be used as an indicator in a grid adaptation strategy designed to generate refined grids for accurately estimating the output functions. This grid adaptation and error correction method is applied to subsonic and supersonic simulations around three-dimensional configurations. Numerical results demonstrate that the sensitive grids to output functions are detected and refined after grid adaptation, and the accuracy of output functions is obviously improved after error correction. The proposed grid adaptation and error correction method is shown to compare very favorably in terms of output accuracy and computational efficiency relative to the traditional featured-based grid adaptation.
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Zhao, Chunhui, Bin Fan, Jinwen Hu, Zhiyuan Zhang, and Quan Pan. "Matching Algorithm of Statistical Optimization Feature Based on Grid Method." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 37, no. 2 (April 2019): 249–57. http://dx.doi.org/10.1051/jnwpu/20193720249.

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The matching algorithm based on image feature points is widely used in image retrieval, target detection, identification and other image processing fields. Aiming at the problem that the feature matching algorithm has low recall rate, a statistical optimization feature based on grid of the normalized cross correlation function is proposed. The matching main direction difference and scale ratio are introduced to feature matching process, for comprehensively utilizing SIFT feature points' information, such as the main direction, scale and position constrains, to accelerate the solution of matching position constraint under the grid framework, which optimizes the feature matching results and improves the recall rate and comprehensive match performance. Firstly, the nearest neighbor matching feature points of each feature point in the original image are found in the target image, and the initial matching results are obtained. Secondly, the matching main direction difference is used to eliminate most mismatches of the initial matching. Thirdly, the matching images are meshed based on the matching scale ratio information, and the position information of the matching feature points distributed among the grids is gathered statistics. Finally, the normalized cross correlation function of each small grid in the original image is calculated to determine whether the matching in the small grid is correct, and the optimized feature matching results are obtained. The experimental results show that the matching accuracy of the new algorithm is similar to that of classical feature matching algorithms, meanwhile the matching recall rate is increased by more than 10%, and a better comprehensive matching performance is obtained.
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Li, Dong Liang, Xiao Feng Zhang, Ming Zhong Qiao, and Gang Cheng. "An Improved Short-Term Load Forecasting Method of Warship." Applied Mechanics and Materials 127 (October 2011): 575–81. http://dx.doi.org/10.4028/www.scientific.net/amm.127.575.

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The power load characteristics of warship on a specific task was analyzed,and a task-based forecasting method for warship short-term load forecasting was presented. the new influencing factors of warship power load were used in modeling which is different with the land grid and civilian vessels grid. Theory of particle swarm optimization and Support vector machine was disscused first, and the method of particle swarm optimization was improved to have the ability of adaptive parameter optimization. and the method of support vector machine was improved by the adaptive PSO optimizational method. then a new adaptive short-term load forecasting model was established by the adaptive PSO-SVM method. finally Through simulation results show that the adaptive PSO-SVM method is highly feasible to predict with high accuracy and high generalization capability.
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10

Dolinyuk, Stanislav, and Volodimir Bagenov. "Optimization of the grid configuration by the method of contour optimization." Bulletin of NTU "KhPI". Series: Problems of Electrical Machines and Apparatus Perfection. The Theory and Practice 4, no. 2 (December 22, 2020): 30–32. http://dx.doi.org/10.20998/2079-3944.2020.2.06.

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Dissertations / Theses on the topic "Grid Optimization Method"

1

Nguyen, Vinh Dinh. "A finite element mesh optimization procedure using a thermal expansion analogy." Thesis, Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/101248.

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Finite element optimum meshes are synthesized by the use of thermal expansion principles in conjunction with an analogous temperature field computed from the element strain energy contents. Elements having high strain energy contents are shrunk and those with low strain energy contents are expanded until all elements contain the same amount of strain energy. Deviatoric strain energy is also used in place of the strain energy as the objective function for the optimization method. Both objective functions yield significant improvements of the meshes after only a few iterations. In one test case, the errors in the maximum stresses are reduced by more than 1/3 after 1 iteration. In another test case, the error in the stress concentration factor is reduced by more than 3/4 after 7 iterations.
M.S.
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Acikgoz, Nazmiye. "Adaptive and Dynamic Meshing Methods for Numerical Simulations." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/14521.

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For the numerical simulation of many problems of engineering interest, it is desirable to have an automated mesh adaption tool. This is important especially for problems characterized by anisotropic features and require mesh clustering in the direction of high gradients. Another significant issue in meshing emerges in unsteady simulations with moving boundaries, where the boundary motion has to be accommodated by deforming the computational grid. Similarly, there exist problems where current mesh needs to be adapted to get more accurate solutions. To solve these problems, we propose three novel procedures. In the first part of this work, we present an optimization procedure for three-dimensional anisotropic tetrahedral grids based on metric-driven h-adaptation. Through the use of topological and geometrical operators, the mesh is iteratively adapted until the final mesh minimizes a given objective function. We propose an optimization process based on an ad-hoc application of the simulated annealing technique, which improves the likelihood of removing poor elements from the grid. Moreover, a local implementation of the simulated annealing is proposed to reduce the computational cost. Many challenging unsteady multi-physics problems are characterized by moving boundaries and/or interfaces. When the boundary displacements are large, degenerate elements are easily formed in the grid such that frequent remeshing is required. We propose a new r-adaptation technique that is valid for all types of elements (e.g., triangle, tet, quad, hex, hybrid) and deforms grids that undergo large imposed displacements at their boundaries. A grid is deformed using a network of linear springs composed of edge springs and a set of virtual springs. The virtual springs are constructed in such a way as to oppose element collapsing. Both frequent remeshing, and exact-pinpointing of clustering locations are great challenges of numerical simulations, which can be overcome by adaptive meshing algorithms. Therefore, we conclude this work by defining a novel mesh adaptation technique where the entire mesh is adapted upon application of a force field in order to comply with the target mesh or to get more accurate solutions. The method has been tested for two-dimensional problems of a-priori metric definitions as well as for oblique shock clusterings.
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Moulard, Laurence. "Optimisation de maillages non structurés : applications à la génération, à la correction et à l'adaptation." Université Joseph Fourier (Grenoble), 1994. http://www.theses.fr/1994GRE10173.

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Nous traitons les problèmes lies aux maillages non structures par des méthodes d'optimisation utilisant des algorithmes d'exploration locale. Le principe consiste à partir d'une solution existante et a l'améliorer grâce a des opérations élémentaires. L'intérêt de cette approche est de pouvoir modifier localement la solution initiale pour qu'elle réponde a des contraintes ou des critères qui peuvent évoluer. On évite ainsi la reconstruction couteuse d'un nouveau maillage à chaque nouvelle demande des utilisateurs
Une étude théorique introduit de nouveaux objets, les tétraphores réalisables, en considérant les seules conditions topologiques d'un maillage. Ces objets se construisent facilement à partir de la frontière du domaine à mailler ; il suffit d'ajouter des contraintes géométriques, très simples à tester et pouvant se traduire sous la forme d'un critère à optimiser, pour obtenir un maillage. Des opérations transformant ces tétraphores sont définies. Les algorithmes d'optimisation sont ainsi bien plus efficaces car ils peuvent être appliques sur un ensemble plus vaste que les maillages
Les algorithmes décrits dans cette thèse sont utilisés industriellement. Des résultats sont donnes pour l'optimisation selon des critères géométriques et topologiques, l'adaptation selon un critère de densité, la correction après déformation des frontières et la génération de maillages
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Ni, Marcus. "Automated Hybrid Singularity Superposition and Anchored Grid Pattern BEM Algorithm for the Solution of the Inverse Geometric Problem." Master's thesis, University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5827.

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A method for solving the inverse geometrical problem is presented by reconstructing the unknown subsurface cavity geometry using boundary element methods, a genetic algorithm, and Nelder-Mead non-linear simplex optimization. The heat conduction problem is solved utilizing the boundary element method, which calculates the difference between the measured temperature at the exposed surface and the computed temperature under the current update of the unknown subsurface flaws and cavities. In a first step, clusters of singularities are utilized to solve the inverse problem and to identify the location of the centroid(s) of the subsurface cavity(ies)/flaw(s). In a second step, the reconstruction of the estimated cavity(ies)/flaw(s) geometry(ies) is accomplished by utilizing an anchored grid pattern upon which cubic spline knots are restricted to move in the search for unknown geometry. Solution of the inverse problem is achieved using a genetic algorithm accelerated with the Nelder-Mead non-linear simplex. To optimize the cubic spline interpolated geometry, the flux (Neumann) boundary conditions are minimized using a least squares functional. The automated algorithm successfully reconstructs single and multiple subsurface cavities within two dimensional mediums. The solver is also shown to accurately predict cavity geometries with random noise in the boundary condition measurements. Subsurface cavities can be difficult to detect based on their location. By applying different boundary conditions to the same geometry, more information is supplied at the boundary, and the subsurface cavity is easily detected despite its low heat signature effect at the boundaries. Extensions to three-dimensional applications are outlined.
M.S.M.E.
Masters
Mechanical and Aerospace Engineering
Engineering and Computer Science
Mechanical Engineering; Thermo-Fluids
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Khan, Kashif. "A distributed computing architecture to enable advances in field operations and management of distributed infrastructure." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/a-distributed-computing-architecture-to-enable-advances-in-field-operations-and-management-of-distributed-infrastructure(a9181e99-adf3-47cb-93e1-89d267219e50).html.

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Distributed infrastructures (e.g., water networks and electric Grids) are difficult to manage due to their scale, lack of accessibility, complexity, ageing and uncertainties in knowledge of their structure. In addition they are subject to loads that can be highly variable and unpredictable and to accidental events such as component failure, leakage and malicious tampering. To support in-field operations and central management of these infrastructures, the availability of consistent and up-to-date knowledge about the current state of the network and how it would respond to planned interventions is argued to be highly desirable. However, at present, large-scale infrastructures are “data rich but knowledge poor”. Data, algorithms and tools for network analysis are improving but there is a need to integrate them to support more directly engineering operations. Current ICT solutions are mainly based on specialized, monolithic and heavyweight software packages that restrict the dissemination of dynamic information and its appropriate and timely presentation particularly to field engineers who operate in a resource constrained and less reliable environments. This thesis proposes a solution to these problems by recognizing that current monolithic ICT solutions for infrastructure management seek to meet the requirements of different human roles and operating environments (defined in this work as field and central sides). It proposes an architectural approach to providing dynamic, predictive, user-centric, device and platform independent access to consistent and up-to-date knowledge. This architecture integrates the components required to implement the functionalities of data gathering, data storage, simulation modelling, and information visualization and analysis. These components are tightly coupled in current implementations of software for analysing the behaviour of networks. The architectural approach, by contrast, requires they be kept as separate as possible and interact only when required using common and standard protocols. The thesis particularly concentrates on engineering practices in clean water distribution networks but the methods are applicable to other structural networks, for example, the electricity Grid. A prototype implementation is provided that establishes a dynamic hydraulic simulation model and enables the model to be queried via remote access in a device and platform independent manner.This thesis provides an extensive evaluation comparing the architecture driven approach with current approaches, to substantiate the above claims. This evaluation is conducted by the use of benchmarks that are currently published and accepted in the water engineering community. To facilitate this evaluation, a working prototype of the whole architecture has been developed and is made available under an open source licence.
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Mahajan, Ashvin. "Grid and solution adaptation via direct optimization methods." [Ames, Iowa : Iowa State University], 2006.

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Howlett, John David. "Size Function Based Mesh Relaxation." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd761.pdf.

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Jacquot, Paulin. "Game theory and Optimization Methods for Decentralized Electric Systems." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLX101/document.

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Dans le contexte de transition vers un système électrique décentralisé et intelligent, nous abordons le problème de la gestion des flexibilités de consommation électriques. Nous développons différentes méthodes basées sur l'optimisation distribuée et la théorie des jeux.Nous commençons par adopter le point de vue d'un opérateur central en charge de la gestion des flexibilités de plusieurs agents. Nous présentons un algorithme distribué permettant le calcul des profils de consommations des agents optimaux pour l'opérateur.Cet algorithme garantit la confidentialité des agents~: les contraintes individuelles, ainsi que le profil individuel de consommation de chaque agent, ne sont jamais révélés à l'opérateur ni aux autres agents.Ensuite, nous adoptons dans un second modèle une vision plus décentralisée et considérons un cadre de théorie des jeux pour la gestion des flexibilités de consommation.Cette approche nous permet en particulier de modéliser les comportements stratégiques des consommateurs.Dans ce cadre, une classe de jeux adéquate est donnée par les jeux de congestion atomiques fractionnables.Nous obtenons plusieurs résultats théoriques concernant les équilibres de Nash dans cette classe de jeux, et nous quantifions l'efficacité de ces équilibres en établissant des bornes supérieures sur le prix de l'anarchie.Nous traitons la question du calcul décentralisé des équilibres de Nash dans ce contexte en étudiant les conditions et les vitesses de convergence des algorithmes de meilleure réponse et de gradient projeté.En pratique un opérateur peut faire face à un très grand nombre de joueurs, et calculer les équilibres d'un jeu de congestion dans ce cas est difficile.Afin de traiter ce problème, nous établissons des résultats sur l'approximation d'un équilibre dans les jeux de congestion et jeux agrégatifs avec un très grand nombre de joueurs et en présence de contraintes couplantes.Ces résultats, obtenus dans le cadre des inégalités variationnelles et sous certaines hypothèses de monotonie, peuvent être utilisés pour calculer un équilibre approché comme solution d'un problème de petite dimension.Toujours dans la perspective de modéliser un très grand nombre d'agents, nous considérons des jeux de congestion nonatomiques avec contraintes couplantes et avec une infinité de joueurs hétérogènes~: ce type de jeux apparaît lorsque les caractéristiques d'une population sont décrites par une fonction de distribution paramétrique.Sous certaines hypothèses de monotonie, nous prouvons que les équilibres de Wardrop de ces jeux, définis comme solutions d'une inégalité variationnelle de dimension infinie, peuvent être approchés par des équilibres de Wardrop symétriques de jeux annexes, solutions d'inégalités variationnelles de petite dimension.Enfin, nous considérons un modèle de jeu pour l'étude d'échanges d'électricité pair-à-pair au sein d'une communauté de consommateurs possédant des actifs de production électrique renouvelable.Nous étudions les équilibres généralisés du jeu obtenu, qui caractérisent les échanges possibles d'énergie et les consommations individuelles.Nous comparons ces équilibres avec la solution centralisée minimisant le coût social, et nous évaluons l'efficacité des équilibres via la notion de prix de l'anarchie
In the context of smart grid and in the transition to decentralized electric systems, we address the problem of the management of distributed electric consumption flexibilities. We develop different methods based on distributed optimization and game theory approaches.We start by adopting the point of view of a centralized operator in charge of the management of flexibilities for several agents. We provide a distributed and privacy-preserving algorithm to compute consumption profiles for agents that are optimal for the operator.In the proposed method, the individual constraints as well as the individual consumption profile of each agent are never revealed to the operator or the other agents.Then, in a second model, we adopt a more decentralized vision and consider a game theoretic framework for the management of consumption flexibilities.This approach enables, in particular, to take into account the strategic behavior of consumers.Individual objectives are determined by dynamic billing mechanisms, which is motivated by the modeling of congestion effects occurring on time periods receiving a high electricity load from consumers.A relevant class of games in this framework is given by atomic splittable congestion games.We obtain several theoretical results on Nash equilibria for this class of games, and we quantify the efficiency of those equilibria by providing bounds on the price of anarchy.We address the question of the decentralized computation of equilibria in this context by studying the conditions and rates of convergence of the best response and projected gradients algorithms.In practice an operator may deal with a very large number of players, and evaluating the equilibria in a congestion game in this case will be difficult.To address this issue, we give approximation results on the equilibria in congestion and aggregative games with a very large number of players, in the presence of coupling constraints.These results, obtained in the framework of variational inequalities and under some monotonicity conditions, can be used to compute an approximate equilibrium, solution of a small dimension problem.In line with the idea of modeling large populations, we consider nonatomic congestion games with coupling constraints, with an infinity of heterogeneous players: these games arise when the characteristics of a population are described by a parametric density function.Under monotonicity hypotheses, we prove that Wardrop equilibria of such games, given as solutions of an infinite dimensional variational inequality, can be approximated by symmetric Wardrop equilibria of auxiliary games, solutions of low dimension variational inequalities.Again, those results can be the basis of tractable methods to compute an approximate Wardrop equilibrium in a nonatomic infinite-type congestion game.Last, we consider a game model for the study of decentralized peer-to-peer energy exchanges between a community of consumers with renewable production sources.We study the generalized equilibria in this game, which characterize the possible energy trades and associated individual consumptions.We compare the equilibria with the centralized solution minimizing the social cost, and evaluate the efficiency of equilibria through the price of anarchy
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Donnot, Benjamin. "Deep learning methods for predicting flows in power grids : novel architectures and algorithms." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS060/document.

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Cette thèse porte sur les problèmes de sécurité sur le réseau électrique français exploité par RTE, le Gestionnaire de Réseau de Transport (GRT). Les progrès en matière d'énergie durable, d'efficacité du marché de l'électricité ou de nouveaux modes de consommation poussent les GRT à exploiter le réseau plus près de ses limites de sécurité. Pour ce faire, il est essentiel de rendre le réseau plus "intelligent". Pour s'attaquer à ce problème, ce travail explore les avantages des réseaux neuronaux artificiels. Nous proposons de nouveaux algorithmes et architectures d'apprentissage profond pour aider les opérateurs humains (dispatcheurs) à prendre des décisions que nous appelons " guided dropout ". Ceci permet de prévoir les flux électriques consécutifs à une modification volontaire ou accidentelle du réseau. Pour se faire, les données continues (productions et consommations) sont introduites de manière standard, via une couche d'entrée au réseau neuronal, tandis que les données discrètes (topologies du réseau électrique) sont encodées directement dans l'architecture réseau neuronal. L’architecture est modifiée dynamiquement en fonction de la topologie du réseau électrique en activant ou désactivant des unités cachées. Le principal avantage de cette technique réside dans sa capacité à prédire les flux même pour des topologies de réseau inédites. Le "guided dropout" atteint une précision élevée (jusqu'à 99% de précision pour les prévisions de débit) tout en allant 300 fois plus vite que des simulateurs de grille physiques basés sur les lois de Kirchoff, même pour des topologies jamais vues, sans connaissance détaillée de la structure de la grille. Nous avons également montré que le "guided dropout" peut être utilisé pour classer par ordre de gravité des évènements pouvant survenir. Dans cette application, nous avons démontré que notre algorithme permet d'obtenir le même risque que les politiques actuellement mises en œuvre tout en n'exigeant que 2 % du budget informatique. Le classement reste pertinent, même pour des cas de réseau jamais vus auparavant, et peut être utilisé pour avoir une estimation globale de la sécurité globale du réseau électrique
This thesis addresses problems of security in the French grid operated by RTE, the French ``Transmission System Operator'' (TSO). Progress in sustainable energy, electricity market efficiency, or novel consumption patterns push TSO's to operate the grid closer to its security limits. To this end, it is essential to make the grid ``smarter''. To tackle this issue, this work explores the benefits of artificial neural networks. We propose novel deep learning algorithms and architectures to assist the decisions of human operators (TSO dispatchers) that we called “guided dropout”. This allows the predictions on power flows following of a grid willful or accidental modification. This is tackled by separating the different inputs: continuous data (productions and consumptions) are introduced in a standard way, via a neural network input layer while discrete data (grid topologies) are encoded directly in the neural network architecture. This architecture is dynamically modified based on the power grid topology by switching on or off the activation of hidden units. The main advantage of this technique lies in its ability to predict the flows even for previously unseen grid topologies. The "guided dropout" achieves a high accuracy (up to 99% of precision for flow predictions) with a 300 times speedup compared to physical grid simulators based on Kirchoff's laws even for unseen contingencies, without detailed knowledge of the grid structure. We also showed that guided dropout can be used to rank contingencies that might occur in the order of severity. In this application, we demonstrated that our algorithm obtains the same risk as currently implemented policies while requiring only 2% of today's computational budget. The ranking remains relevant even handling grid cases never seen before, and can be used to have an overall estimation of the global security of the power grid
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10

Weiss, Christian. "Data locality optimizations for multigrid methods on structured grids." [S.l. : s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=963751441.

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Books on the topic "Grid Optimization Method"

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Hesthaven, J. S. A wavelet optimized adaptive multi-domain method. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1997.

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Chakrabortty, Aranya. Control and optimization methods for electric smart grids. Edited by Ilic Marija D. 1951-. New York: Springer, 2012.

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Chakrabortty, Aranya, and Marija D. Ilić, eds. Control and Optimization Methods for Electric Smart Grids. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1605-0.

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Crespo, Luis G. Optimization of systems with uncertainty: Initial developments for performance, robustness and reliability based designs. Hampton, VA: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 2002.

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Diskin, Boris. Solving upwind-biased discretizations II: Multigrid solver using semicoarsening. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1999.

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ZnO bao mo zhi bei ji qi guang, dian xing neng yan jiu. Shanghai Shi: Shanghai da xue chu ban she, 2010.

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Oldfield, Chad. An adjoint method augmented with grid sensitivities for aerodynamic optimization. 2006.

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Oldfield, Chad. An adjoint method augmented with grid sensitivities for aerodynamic optimization. 2006.

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Chakrabortty, Aranya, and Marija D. Ilić. Control and Optimization Methods for Electric Smart Grids. Springer, 2011.

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Chakrabortty, Aranya, and Marija D. Ilić. Control and Optimization Methods for Electric Smart Grids. Springer, 2014.

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Book chapters on the topic "Grid Optimization Method"

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Viuda, José M., Juan J. Guerra, and A. Abbas. "Use of ONERA Grid Optimization Method at CASA." In Multiblock Grid Generation, 79–85. Wiesbaden: Vieweg+Teubner Verlag, 1993. http://dx.doi.org/10.1007/978-3-322-87881-6_12.

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Tatsis, Vasileios A., and Konstantinos E. Parsopoulos. "Experimental Sensitivity Analysis of Grid-Based Parameter Adaptation Method." In Heuristics for Optimization and Learning, 335–46. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58930-1_22.

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Hu, Zesheng, Jun Lu, Xingxing Wang, Zhiqiang Xu, Gangjun Gong, and Yun Wang. "Intelligent Park Load Scheduling Optimization Method Considering Heat-Power Linkage." In Proceedings of PURPLE MOUNTAIN FORUM 2019-International Forum on Smart Grid Protection and Control, 789–800. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9779-0_64.

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Li, Shaopeng, Bin Zhou, Hongxiang Pan, and Feng Liang. "Micro-grid Dispatch Decision-Making Method Based on Adjustable Robust Optimization Algorithm." In Proceedings of 2020 International Top-Level Forum on Engineering Science and Technology Development Strategy and The 5th PURPLE MOUNTAIN FORUM (PMF2020), 146–60. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9746-6_12.

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Tinh, Tran Thanh, Dang Thai Son, and Nguyen Anh Thi. "Development of a Three Dimensional Euler Solver Using the Finite Volume Method on a Multiblock Structured Grid." In Modeling, Simulation and Optimization of Complex Processes, 283–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25707-0_23.

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López, José Iván, Marina Brovka, José Marı́a Escobar, José Manuel Cascón, and Rafael Montenegro. "An Optimization Based Method for the Construction of 2D Parameterizations for Isogeometric Analysis with T-Splines." In New Challenges in Grid Generation and Adaptivity for Scientific Computing, 91–112. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-06053-8_5.

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Liu, Ning, and Fengjuan Wang. "Dynamic Strategy Based Optimization Method for Inventory Problem: Case Study in Guizhou Power Grid." In Advances in Intelligent Systems and Computing, 1261–69. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1837-4_103.

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Piasecki, S., R. Szmurlo, J. Rabkowski, and M. P. Kazmierkowski. "A Method of Design and Optimization for SiC-Based Grid-Connected AC-DC Converters." In Advances in Data Analysis with Computational Intelligence Methods, 395–412. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67946-4_18.

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Zhu, Xiaohui, Lu Ji, Huijing Bi, and Xiaobo Zhao. "Research on Intelligent Optimization Method of Grid Communication Server Based on Support Vector Machine." In Advances in Intelligent Information Hiding and Multimedia Signal Processing, 323–31. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6420-2_40.

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Yu, Yongjin, Kecheng Zhao, and Yubin Wang. "Computation of the Three-Dimensional Electric Field Using Particle Swarm Optimization Charge Simulation Method." In Proceedings of PURPLE MOUNTAIN FORUM 2019-International Forum on Smart Grid Protection and Control, 923–34. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9783-7_75.

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Conference papers on the topic "Grid Optimization Method"

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Cao, Yonglei. "Resource Optimization Scheduling Method for Smart Grid." In 2019 11th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). IEEE, 2019. http://dx.doi.org/10.1109/icmtma.2019.00112.

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Khorasani, Ali Mohades, Marjan Goodarzi, and Majid Forghani-elahabad. "Particle Swarm Optimization Method in Optimization of Grid Shell Structures." In CNMAC 2019 - XXXIX Congresso Nacional de Matemática Aplicada e Computacional. SBMAC, 2020. http://dx.doi.org/10.5540/03.2020.007.01.0416.

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Venayagamoorthy, Ganesh Kumar. "A dynamic optimization method for a smart grid." In Energy Society General Meeting. IEEE, 2010. http://dx.doi.org/10.1109/pes.2010.5589824.

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Nordin, Nur Dalilah, and Hasimah Abdul Rahman. "An optimization method for designing stand alone photovoltaic system using iterative method." In 2015 IEEE International Conference on Smart Energy Grid Engineering (SEGE). IEEE, 2015. http://dx.doi.org/10.1109/sege.2015.7324586.

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Gao, Kun, and Lifeng Xi. "A uniform parallel optimization method for data mining grid." In First International Workshop. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1577389.1577390.

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Muller, Zdenek, Miroslav Muller, Valeria Tuzikova, Josef Tlusty, Martin Cernan, Yuval Beck, and Gady Golan. "Novel method of optimization of losses in power grid." In 2016 IEEE International Conference on the Science of Electrical Engineering (ICSEE). IEEE, 2016. http://dx.doi.org/10.1109/icsee.2016.7806044.

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Zhang, Xiaochen, Dongmei Yang, Wei Du, Yonghua Chen, and Guoxin He. "Research on Operation Optimization Method of Regional Integrated Energy System." In 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia). IEEE, 2019. http://dx.doi.org/10.1109/isgt-asia.2019.8881447.

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Huang, Tianxiao, Tianzhi Cao, Ziqiang Ran, Binghui Wang, and Qing Liu. "Parameter Optimization Method of MMC Controls Based on Firefly Algorithm." In 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia). IEEE, 2019. http://dx.doi.org/10.1109/isgt-asia.2019.8881551.

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Liu, Ke-yan, Wanxing Sheng, Xiaoli Meng, and Yongmei Liu. "Decentralized voltage optimization and coordinated method in smart distribution grid." In 2015 IEEE Power & Energy Society General Meeting. IEEE, 2015. http://dx.doi.org/10.1109/pesgm.2015.7286105.

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Chai, Jiyong, Jingxiao Li, Ying Zhang, Chengxiong Mao, Jiming Lu, Man Jiang, and Youping Xu. "Intelligent optimization method of PSS parameters based on grid equivalence." In 2011 International Conference on Electrical and Control Engineering (ICECE). IEEE, 2011. http://dx.doi.org/10.1109/iceceng.2011.6057702.

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Reports on the topic "Grid Optimization Method"

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Michalski, Anatoli I., Pavel Grigoriev, and Vasiliy P. Gorlischev. R programs for splitting abridged fertility data into a fine grid of ages using the quadratic optimization method. Rostock: Max Planck Institute for Demographic Research, January 2018. http://dx.doi.org/10.4054/mpidr-tr-2018-002.

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