References of "Wehenkel, Louis"
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See detailAutomatic learning of fine operating rules for online power system security control
Sun, Hongbin; Zhao, Feng; Wang, Huifang et al

in IEEE Transactions on Neural networks and learning systems (in press)

Fine operating rules for security control and an automatic system for their online discovery were developed to adapt to the development of smart grids. The automatic system uses the real-time system state ... [more ▼]

Fine operating rules for security control and an automatic system for their online discovery were developed to adapt to the development of smart grids. The automatic system uses the real-time system state to determine critical flowgates, and then a continuation power flow-based security analysis is used to compute the initial transfer capability of critical flowgates. Next, the system applies the Monte Carlo simulations to expected short-term operating condition changes, feature selection, and a linear least squares fitting of the fine operating rules. The proposed system was validated both on an academic test system and on a provincial power system in China. The results indicated that the derived rules provide accuracy and good interpretability and are suitable for real-time power system security control. The use of high-performance computing systems enables these fine operating rules to be refreshed online every 15 min. [less ▲]

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See detailCollaborative analysis of multi-gigapixel imaging data using Cytomine
Marée, Raphaël ULg; Rollus, Loïc; Stévens, Benjamin et al

in Bioinformatics (2016)

Motivation: Collaborative analysis of massive imaging datasets is essential to enable scientific discoveries. Results: We developed Cytomine to foster active and distributed collaboration of ... [more ▼]

Motivation: Collaborative analysis of massive imaging datasets is essential to enable scientific discoveries. Results: We developed Cytomine to foster active and distributed collaboration of multidisciplinary teams for large-scale image-based studies. It uses web development methodologies and machine learning in order to readily organize, explore, share, and analyze (semantically and quantitatively) multi-gigapixel imaging data over the internet. We illustrate how it has been used in several biomedical applications. Availability: Cytomine (http://www.cytomine.be/) is freely available under an open-source license from http://github.com/cytomine/. A documentation wiki (http://doc.cytomine.be) and a demo server (http://demo.cytomine.be) are also available. [less ▲]

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See detailOnline Learning for Strong Branching Approximation in Branch-and-Bound
Marcos Alvarez, Alejandro ULg; Wehenkel, Louis ULg; Louveaux, Quentin ULg

E-print/Working paper (2016)

We present an online learning approach to variable branching in branch-and-bound for mixed-integer linear problems. Our approach consists in learning strong branching scores in an online fashion and in ... [more ▼]

We present an online learning approach to variable branching in branch-and-bound for mixed-integer linear problems. Our approach consists in learning strong branching scores in an online fashion and in using them to take branching decisions. More specifically, numerical scores are used to rank the branching candidates. If, for a given variable, the learned approximation is deemed reliable, then the score for that variable is computed thanks to the learned function. If the approximation is not reliable yet, the real strong branching score is used instead. The scores that are computed through the real strong branching procedure are fed to the online learning algorithm in order to improve the approximated function. The experiments show promising results. [less ▲]

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See detailTowards Generic Image Classification using Tree-based Learning: an Extensive Empirical Study
Marée, Raphaël ULg; Geurts, Pierre ULg; Wehenkel, Louis ULg

in Pattern Recognition Letters (2016)

This paper considers the general problem of image classification without using any prior knowledge about image classes. We study variants of a method based on supervised learning whose common steps are ... [more ▼]

This paper considers the general problem of image classification without using any prior knowledge about image classes. We study variants of a method based on supervised learning whose common steps are the extraction of random subwindows described by raw pixel intensity values and the use of ensemble of extremely randomized trees to directly classify images or to learn image features. The influence of method parameters and variants is thoroughly evaluated so as to provide baselines and guidelines for future studies. Detailed results are provided on 80 publicly available datasets that depict very diverse types of images (more than 3800 image classes and over 1.5 million images). [less ▲]

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See detailgammaMAXT: a fast multiple-testing correction algorithm
Van Lishout, François ULg; Gadaleta, Francesco; Moore, Jason H. et al

in BioData Mining (2015), 8(36),

Background: The purpose of the maxT algorithm is to provide a significance test algorithm that controls the family-wise error rate (FWER) during simultaneous hypothesis testing. However, the requirements ... [more ▼]

Background: The purpose of the maxT algorithm is to provide a significance test algorithm that controls the family-wise error rate (FWER) during simultaneous hypothesis testing. However, the requirements in terms of computing time and memory of this procedure are proportional to the number of investigated hypotheses. The memory issue has been solved in 2013 by Van Lishout’s implementation of MaxT, which makes the memory usage independent from the size of the dataset. This algorithm is implemented in MBMDR-3.0.3, a software that is able to identify genetic interactions, for a variety of SNP-SNP based epistasis models effectively. On the other hand, that implementation turned out to be less suitable for genome-wide interaction analysis studies, due to the prohibitive computational burden. Results: In this work we introduce gammaMAXT, a novel implementation of the maxT algorithm for multiple testing correction. The algorithm was implemented in software MBMDR-4.2.2, as part of the MB-MDR framework to screen for SNP-SNP, SNP-environment or SNP-SNP-environment interactions at a genome-wide level. We show that, in the absence of interaction effects, test-statistics produced by the MB-MDR methodology follow a mixture distribution with a point mass at zero and a shifted gamma distribution for the top 10 % of the strictly positive values. We show that the gammaMAXT algorithm has a power comparable to MaxT and maintains FWER, but requires less computational resources and time. We analyze a dataset composed of 106 SNPs and 1000 individuals within one day on a 256-core computer cluster. The same analysis would take about 104 times longer with MBMDR-3.0.3. Conclusions: These results are promising for future GWAIs.However, the proposed gammaMAXT algorithm offers a general significance assessment and multiple testing approach, applicable to any context that requires performing hundreds of thousands of tests. It offers new perspectives for fast and efficient permutation-based significance assessment in large-scale (integrated) omics studies. [less ▲]

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See detailConduire un projet techno-pédagogique avec trois universités francophones belges
Vincke, Grégoire ULg; Marée, Raphaël ULg; Wehenkel, Louis ULg et al

Poster (2015, November 10)

L’imagerie numérique devient omniprésente dans beaucoup de métiers, dont ceux de la médecine. Sa montée en puissance questionne l’université quant à la meilleure manière d’entraîner les étudiants à lire ... [more ▼]

L’imagerie numérique devient omniprésente dans beaucoup de métiers, dont ceux de la médecine. Sa montée en puissance questionne l’université quant à la meilleure manière d’entraîner les étudiants à lire, interpréter, comparer, contextualiser et discuter ces ressources inédites. A l’université, ces compétences s’acquièrent principalement, aux premiers stades de la formation, dans les cours d’histologie. Dans ce contexte, l’IFRES a initié un projet wallon (Appel Germaine Tillion / Convention n°1318185 http://histoweb.ifres.ulg.ac.be ) dont l’objectif est la transformation de la plate-forme logicielle wallonne Cytomine – http://cytomine.be une solution Web existante d'échange, de visualisation, d'annotation et d’analyse collaborative semi-automatique d'images numérisées – en un système d’activités pédagogiques tirant parti des initiatives partielles existant chez les partenaires et des évolutions techniques les plus récentes. Le projet HistoWeb travaille à cette transformation en s’appuyant, au plan conceptuel, sur l’exploration de la notion d’« écologie d’apprentissage ». Le projet compte parmi ses partenaires 3 universités (ULB, UNamur, Liège) et exige donc de chercher un équilibre entre des développements techno-pédagogiques susceptibles de les intéresser tous et des développements spécifiques et en prise avec les écologies d’apprentissage propres à chaque institution. Le poster présente les manières choisies par l’IFRES pour coordonner l'ensemble du projet, travaillant tantôt individuellement tantôt collectivement avec les différents partenaires. Le poster aura un mot par ailleurs sur la collaboration entre pédagogues et développeurs informatiques. En effet, l’IFRES, sur toute la durée du projet est appelé à travailler avec une équipe de recherche (Systmod) dépourvue de familiarité avec les leviers de l’enseignement et de l’apprentissage. Il évoquera aussi les collaborations espérées d’un passage en Open source. Le poster pourra apporter des éclairages aux questions : - qu’entend-on précisément par collaboration lorsqu’il s’agit de mener de la « recherche » dans un contexte académique ? Il sera ici question de cet équilibre entre convergence et différentiation. - Qui est susceptible de se développer professionnellement ? Les bénéfices tirés du projet par les uns et les autres au niveau pédagogique seront ici évoqués. [less ▲]

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See detailClassifying pairs with trees for supervised biological network inference
Schrynemackers, Marie ULg; Wehenkel, Louis ULg; Madan Babu, Mohan et al

in Molecular Biosystems (2015), 11(8), 2116-2125

Networks are ubiquitous in biology, and computational approaches have been largely investigated for their inference. In particular, supervised machine learning methods can be used to complete a partially ... [more ▼]

Networks are ubiquitous in biology, and computational approaches have been largely investigated for their inference. In particular, supervised machine learning methods can be used to complete a partially known network by integrating various measurements. Two main supervised frameworks have been proposed: the local approach, which trains a separate model for each network node, and the global approach, which trains a single model over pairs of nodes. Here, we systematically investigate, theoretically and empirically, the exploitation of tree-based ensemble methods in the context of these two approaches for biological network inference. We first formalize the problem of network inference as a classification of pairs, unifying in the process homogeneous and bipartite graphs and discussing two main sampling schemes. We then present the global and the local approaches, extending the latter for the prediction of interactions between two unseen network nodes, and discuss their specializations to tree-based ensemble methods, highlighting their interpretability and drawing links with clustering techniques. Extensive computational experiments are carried out with these methods on various biological networks that clearly highlight that these methods are competitive with existing methods. [less ▲]

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See detailZebrafish bone and general physiology are differently affected by hormones or changes in gravity.
Aceto, Jessica ULg; Nourizadeh-Lillabadi, Rasoul; Marée, Raphaël ULg et al

in PLoS ONE (2015), 10(6), 1-42

Teleost fish such as zebrafish (Danio rerio) are increasingly used for physiological, genetic and developmental studies. Our understanding of the physiological consequences of altered gravity in an entire ... [more ▼]

Teleost fish such as zebrafish (Danio rerio) are increasingly used for physiological, genetic and developmental studies. Our understanding of the physiological consequences of altered gravity in an entire organism is still incomplete. We used altered gravity and drug treatment experiments to evaluate their effects specifically on bone formation and more generally on whole genome gene expression. By combining morphometric tools with an objective scoring system for the state of development for each element in the head skeleton and specific gene expression analysis, we confirmed and characterized in detail the decrease or increase of bone formation caused by a 5 day treatment (from 5dpf to 10 dpf) of, respectively parathyroid hormone (PTH) or vitamin D3 (VitD3). Microarray transcriptome analysis after 24 hours treatment reveals a general effect on physiology upon VitD3 treatment, while PTH causes more specifically developmental effects. Hypergravity (3g from 5dpf to 9 dpf) exposure results in a significantly larger head and a significant increase in bone formation for a subset of the cranial bones. Gene expression analysis after 24 hrs at 3g revealed differential expression of genes involved in the development and function of the skeletal, muscular, nervous, endocrine and cardiovascular systems. Finally, we propose a novel type of experimental approach, the "Reduced Gravity Paradigm", by keeping the developing larvae at 3g hypergravity for the first 5 days before returning them to 1g for one additional day. 5 days exposure to 3g during these early stages also caused increased bone formation, while gene expression analysis revealed a central network of regulatory genes (hes5, sox10, lgals3bp, egr1, edn1, fos, fosb, klf2, gadd45ba and socs3a) whose expression was consistently affected by the transition from hyper- to normal gravity. [less ▲]

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See detailMachine Learning to Balance the Load in Parallel Branch-and-Bound
Marcos Alvarez, Alejandro ULg; Wehenkel, Louis ULg; Louveaux, Quentin ULg

E-print/Working paper (2015)

We describe in this paper a new approach to parallelize branch-and-bound on a certain number of processors. We propose to split the optimization of the original problem into the optimization of several ... [more ▼]

We describe in this paper a new approach to parallelize branch-and-bound on a certain number of processors. We propose to split the optimization of the original problem into the optimization of several subproblems that can be optimized separately with the goal that the amount of work that each processor carries out is balanced between the processors, while achieving interesting speedups. The main innovation of our approach consists in the use of machine learning to create a function able to estimate the difficulty (number of nodes) of a subproblem of the original problem. We also present a set of features that we developed in order to characterize the encountered subproblems. These features are used as input of the function learned with machine learning in order to estimate the difficulty of a subproblem. The estimates of the numbers of nodes are then used to decide how to partition the original optimization tree into a given number of subproblems, and to decide how to distribute them among the available processors. The experiments that we carry out show that our approach succeeds in balancing the amount of work between the processors, and that interesting speedups can be achieved with little effort. [less ▲]

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See detailPhenotype Classification of Zebrafish Embryos by Supervised Learning
Jeanray, Nathalie ULg; Marée, Raphaël ULg; Pruvot, Benoist et al

in PLoS ONE (2015), 10(1), 01169891-20

Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances ... [more ▼]

Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100 % agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification. [less ▲]

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See detailTowards generic image classification: an extensive empirical study
Marée, Raphaël ULg; Geurts, Pierre ULg; Wehenkel, Louis ULg

E-print/Working paper (2014)

This paper considers the general problem of image classification without using any prior knowledge about image classes. We study variants of a method based on supervised learning whose common steps are ... [more ▼]

This paper considers the general problem of image classification without using any prior knowledge about image classes. We study variants of a method based on supervised learning whose common steps are the extraction of random subwindows described by raw pixel intensity values and the use of ensemble of extremely randomized trees to directly classify images or to learn image features. The influence of method parameters and variants is thoroughly evaluated so as to provide baselines and guidelines for future studies. Detailed results are provided on 80 publicly available datasets that depict very diverse types of images (more than 3800 image classes and over 1.5 million images). [less ▲]

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See detailHistoWeb - Toward a new learning ecology for histology
Vincke, Grégoire ULg; Marée, Raphaël ULg; Wehenkel, Louis ULg et al

Conference (2014, December 16)

HistoWeb targets the transformation of the professional tool Cytomineinto a comprehensive and innovative teaching platform, valuing the notions of learning ecology and new learning dimensions seeking for ... [more ▼]

HistoWeb targets the transformation of the professional tool Cytomineinto a comprehensive and innovative teaching platform, valuing the notions of learning ecology and new learning dimensions seeking for lifelong competencies. The poster was released at the Digital Learning round table, organized by the European Commission around H2020 funding instruments and call 2 "ICT-20 Technologies for better human learning" [less ▲]

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See detailgammaMAXT: a fast multiple-testing correction algorithm
Van Lishout, François ULg; Gadaleta, Francesco ULg; Moore, Jason H. et al

in ERCIM 2014 Abstract Book (2014, November 12)

The purpose of the maxT algorithm (1993) is to control the family-wise error rate (FWER) when assessing significance of multiple tests jointly. However, the requirements in terms of computing time and ... [more ▼]

The purpose of the maxT algorithm (1993) is to control the family-wise error rate (FWER) when assessing significance of multiple tests jointly. However, the requirements in terms of computing time and memory of this procedure are proportional to the number of investigated hypothesis. The memory issue has been solved by Van Lishout’s implementation of maxT (2013), which makes the memory usage independent from the size of the dataset. This algorithm is implemented in MBMDR-3.0.3, a software that is able to identify genetic interactions, for a variety of SNP-SNP based epistasis model,s in an effective way. However, that implementation turned out to be less suitable for genome-wide interaction analysis studies, due to the prohibitive computational burden. Here, we present gammaMAXT, a novel algorithm which is part of MBMDR-4.2.2. We show that, in the abscence of interaction effects, test-statistics produced by the MB-MDR methodology follow a mixture distribution with a point mass at zero and a shifted gamma distribution for the top 10% of the strictly positive values. We show that the gammaMAXT algorithm has a power comparable to maxT and maintains FWER, but requires less computational resources and time. MBMDR-4.2.2 can be downloaded at http://www.statgen.ulg.ac.be. [less ▲]

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See detailTrajectory-Based Supplementary Damping Control for Power System Electromechanical Oscillations
Wang, Da ULg; Glavic, Mevludin ULg; Wehenkel, Louis ULg

in IEEE Transactions on Power Systems (2014), 29(6), 2835-2845

This paper considers a trajectory-based approach to determine control signals superimposed to those of existing controllers so as to enhance the damping of electromechanical oscillations. This approach is ... [more ▼]

This paper considers a trajectory-based approach to determine control signals superimposed to those of existing controllers so as to enhance the damping of electromechanical oscillations. This approach is framed as a discrete-time, multi-step optimization problem which can be solved by model-based and/or by learning-based methods. This paper proposes to apply a model-free tree-based batch mode Reinforcement Learning (RL) algorithm to perform such a supplementary damping control based only on information collected from observed trajectories of the power system. This RL-based supplementary damping control scheme is first implemented on a single generator and then several possibilities are investigated for extending it to multiple generators. Simulations are carried out on a 16-generators medium size power system model, where also possible benefits of combining this RL-based control with Model Predictive Control (MPC) are assessed. [less ▲]

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See detailHistoWeb - Transforming a professional bioimages platform into a learning tool for histology
QUATRESOOZ, Pascale ULg; Defaweux, Valérie ULg; Rollus, Loïc ULg et al

Scientific conference (2014, October 30)

In 2012, medical studies in Belgium has undergone an important reform that leads to a rocketing rise of the number of students. The University of Liège took this renewed context as an opportunity to ... [more ▼]

In 2012, medical studies in Belgium has undergone an important reform that leads to a rocketing rise of the number of students. The University of Liège took this renewed context as an opportunity to thoroughly revamp its teaching methods in histology, extending CYTOMINE [1] (http://cytomine.be : a web-based, image storage, annotation, and analysis platform) with new pedagogical features. [less ▲]

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See detailRandom forests with random projections of the output space for high dimensional multi-label classification
Joly, Arnaud ULg; Geurts, Pierre ULg; Wehenkel, Louis ULg

in Machine Learning and Knowledge Discovery in Databases (2014, September 15)

We adapt the idea of random projections applied to the out- put space, so as to enhance tree-based ensemble methods in the context of multi-label classification. We show how learning time complexity can ... [more ▼]

We adapt the idea of random projections applied to the out- put space, so as to enhance tree-based ensemble methods in the context of multi-label classification. We show how learning time complexity can be reduced without affecting computational complexity and accuracy of predictions. We also show that random output space projections may be used in order to reach different bias-variance tradeoffs, over a broad panel of benchmark problems, and that this may lead to improved accuracy while reducing significantly the computational burden of the learning stage. [less ▲]

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See detailAn AC OPF-based Heuristic Algorithm for Optimal Transmission Switching
Capitanescu, Florin; Wehenkel, Louis ULg

in Proceedings of the 18th Power Systems Computation Conference (2014, August)

This paper focuses on reducing generators dispatch cost by means of transmission line switching. The problem is formulated as a mixed-integer nonlinear program (MINLP) optimal power flow (OPF). A scalable ... [more ▼]

This paper focuses on reducing generators dispatch cost by means of transmission line switching. The problem is formulated as a mixed-integer nonlinear program (MINLP) optimal power flow (OPF). A scalable heuristic algorithm is proposed to break-down the complexity of the problem due to the huge combinatorial space. The algorithm aims at providing the sequence of lines to be removed from service, one at the time, until no further decrease in the dispatch cost can be obtained. It identifies the line candidate for removal at each step by exploiting the (continuously relaxed values of) lines breaker statuses at the solution of a relaxed OPF problem. The algorithm thus relies on solving a sequence of OPF problems formulated as nonlinear programs (NLPs). The effectiveness of the approach is demonstrated on the IEEE118-bus system. Results show that the approach can provide good quality sub-optimal solutions with relatively small computational effort and by removing only few lines from service. [less ▲]

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See detailAdvanced optimization methods for power systems
Panciatici, Patrick; Campi, M.C.; Garatti, S. et al

in Proceedings of the 18th Power Systems Computation Conference (2014, August)

Power system planning and operation offers multitudinous opportunities for optimization methods. In practice, these problems are generally large-scale, non-linear, subject to uncertainties, and combine ... [more ▼]

Power system planning and operation offers multitudinous opportunities for optimization methods. In practice, these problems are generally large-scale, non-linear, subject to uncertainties, and combine both continuous and discrete variables. In the recent years, a number of complementary theoretical advances in addressing such problems have been obtained in the field of applied mathematics. The paper introduces a selection of these advances in the fields of non-convex optimization, in mixedinteger programming, and in optimization under uncertainty. The practical relevance of these developments for power systems planning and operation are discussed, and the opportunities for combining them, together with high-performance computing and big data infrastructures, as well as novel machine learning and randomized algorithms, are highlighted. [less ▲]

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See detailComparison of centralized, distributed and hierarchical model predictive control schemes for electromechanical oscillations damping in large-scale power systems
Wang, Da ULg; Glavic, Mevludin ULg; Wehenkel, Louis ULg

in International Journal of Electrical Power & Energy Systems (2014), 58

The paper investigates the feasibility of applying Model Predictive Control (MPC) as a viable strategy to damp wide-area electromechanical oscillations in large-scale power systems. First a fully ... [more ▼]

The paper investigates the feasibility of applying Model Predictive Control (MPC) as a viable strategy to damp wide-area electromechanical oscillations in large-scale power systems. First a fully centralized MPC scheme is considered, and its performances are evaluated first in ideal conditions and then by considering state estimation errors and communication delays. This scheme is further extended into a distributed scheme with the aim of making it more viable for very large-scale or multi-area systems. Finally, a robust hierarchical multi-area MPC scheme is proposed, introducing a second layer of MPC based controllers at the level of individual power plants and transmission lines. Simulations are carried out using a 70-bus test system. The results reveal all three MPC schemes as viable solutions to supplement existing controllers in order to improve the system performance in terms of damping. The hierarchical scheme is the one combining the best performances in nominal conditions and the best robustness with respect to partial component failures and various modeling and measurement errors. [less ▲]

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See detailA generic approach for solving nonlinear-discrete security-constrained power flow problems in large-scale systems
Platbrood, Ludovic; Capitanescu, Florin; Crisciu, Horia et al

in IEEE Transactions on Power Systems (2014), 29(3), 1194-1203

This paper proves the practicality of an iterative algorithm for solving realistic large-scale SCOPF problems. This algorithm is based on the combination of a contingency filtering scheme, used to ... [more ▼]

This paper proves the practicality of an iterative algorithm for solving realistic large-scale SCOPF problems. This algorithm is based on the combination of a contingency filtering scheme, used to identify the binding contingencies at the optimum, and a network compression method, used to reduce the complexity of the post-contingency models included in the SCOPF formulation. We show that by combining these two complementary ideas, it is possible to solve in a reasonable time SCOPF problems on large power system models with a large number of contingencies. Unlike most results reported for large-scale SCOPF problems, our algorithm uses a non-linear AC network model in both pre-contingency and post-contingency states, optimizes both active/reactive powers flows jointly, and treats the discrete variables. The proposed algorithm is implemented with state-of-the-art solvers and applied to two systems: a national grid with 2563 buses and 1297 contingencies, and a model of the European transmission network with 9241 buses and 12000 contingencies. [less ▲]

Detailed reference viewed: 75 (7 ULg)