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Comparison of centralized, distributed and hierarchical model predictive control schemes for electromechanical oscillations damping in large-scale power systems Wang, Da ; Glavic, Mevludin ; Wehenkel, Louis 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 ▲] Detailed reference viewed: 104 (9 ULg)A generic approach for solving nonlinear-discrete security-constrained power flow problems in large-scale systems ; ; 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: 78 (7 ULg)A hybrid human-computer approach for large-scale image-based measurements using web services and machine learning Marée, Raphaël ; Rollus, Loïc ; Stevens, Benjamin et al in Proceedings IEEE International Symposium on Biomedical Imaging (2014, May) We present a novel methodology combining web-based software development practices, machine learning, and spatial databases for computer-aided quantification of regions of interest (ROIs) in large-scale ... [more ▼] We present a novel methodology combining web-based software development practices, machine learning, and spatial databases for computer-aided quantification of regions of interest (ROIs) in large-scale imaging data. We describe our main methodological choices, and then illustrate the benefits of the approach (workload reduction, improved precision, scalability, and traceability) on hundreds of whole-slide images of biological tissue slices in cancer research. [less ▲] Detailed reference viewed: 353 (49 ULg)Exploiting SNP Correlations within Random Forest for Genome-Wide Association Studies Botta, Vincent ; Louppe, Gilles ; Geurts, Pierre et al in PLoS ONE (2014) The primary goal of genome-wide association studies (GWAS) is to discover variants that could lead, in isolation or in combination, to a particular trait or disease. Standard approaches to GWAS, however ... [more ▼] The primary goal of genome-wide association studies (GWAS) is to discover variants that could lead, in isolation or in combination, to a particular trait or disease. Standard approaches to GWAS, however, are usually based on univariate hypothesis tests and therefore can account neither for correlations due to linkage disequilibrium nor for combinations of several markers. To discover and leverage such potential multivariate interactions, we propose in this work an extension of the Random Forest algorithm tailored for structured GWAS data. In terms of risk prediction, we show empirically on several GWAS datasets that the proposed T-Trees method significantly outperforms both the original Random Forest algorithm and standard linear models, thereby suggesting the actual existence of multivariate non-linear effects due to the combinations of several SNPs. We also demonstrate that variable importances as derived from our method can help identify relevant loci. Finally, we highlight the strong impact that quality control procedures may have, both in terms of predictive power and loci identification. [less ▲] Detailed reference viewed: 192 (15 ULg)Optimized look-ahead tree policies: a bridge between look-ahead tree policies and direct policy search Jung, Tobias ; Wehenkel, Louis ; Ernst, Damien et al in International Journal of Adaptive Control and Signal Processing (2014), 28(3-5), 255-289 Direct policy search (DPS) and look-ahead tree (LT) policies are two popular techniques for solving difficult sequential decision-making problems. They both are simple to implement, widely applicable ... [more ▼] Direct policy search (DPS) and look-ahead tree (LT) policies are two popular techniques for solving difficult sequential decision-making problems. They both are simple to implement, widely applicable without making strong assumptions on the structure of the problem, and capable of producing high performance control policies. However, computationally both of them are, each in their own way, very expensive. DPS can require huge offline resources (effort required to obtain the policy) to first select an appropriate space of parameterized policies that works well for the targeted problem, and then to determine the best values of the parameters via global optimization. LT policies do not require any offline resources; however, they typically require huge online resources (effort required to calculate the best decision at each step) in order to grow trees of sufficient depth. In this paper, we propose optimized look-ahead trees (OLT), a model-based policy learning scheme that lies at the intersection of DPS and LT. In OLT, the control policy is represented indirectly through an algorithm that at each decision step develops, as in LT using a model of the dynamics, a small look-ahead tree until a prespecified online budget is exhausted. Unlike LT, the development of the tree is not driven by a generic heuristic; rather, the heuristic is optimized for the target problem and implemented as a parameterized node scoring function learned offline via DPS. We experimentally compare OLT with pure DPS and pure LT variants on optimal control benchmark domains. The results show that the LT-based representation is a versatile way of compactly representing policies in a DPS scheme (which results in OLT being easier to tune and having lower offline complexity than pure DPS); while at the same time, DPS helps to significantly reduce the size of the look-ahead trees that are required to take high-quality decisions (which results in OLT having lower online complexity than pure LT). Moreover, OLT produces overall better performing policies than pure DPS and pure LT and also results in policies that are robust with respect to perturbations of the initial conditions. [less ▲] Detailed reference viewed: 130 (33 ULg)Power system transient stability preventive and emergency control ; Wehenkel, Louis ; Ernst, Damien et al in Savulescu, Savu (Ed.) Real-Time Stability in Power Systems 2nd Edition (2014) A general approach to real-time transient stability control is described, yielding various complementary techniques: pure preventive, open loop emergency, and closed loop emergency controls. Recent ... [more ▼] A general approach to real-time transient stability control is described, yielding various complementary techniques: pure preventive, open loop emergency, and closed loop emergency controls. Recent progress in terms of a global transient stability constrained optimal power flow are presented, yielding in a scalable nonlinear programming formulation which allows to take near-optimal decisions for preventive control with a computing budget corresponding only to a few runs of standard optimal power flow and time domain simulations. These complementary techniques meet the stringent conditions imposed by the real-life applications. [less ▲] Detailed reference viewed: 70 (3 ULg)A Supervised Machine Learning Approach to Variable Branching in Branch-And-Bound Marcos Alvarez, Alejandro ; Louveaux, Quentin ; Wehenkel, Louis E-print/Working paper (2014) We present in this paper a new approach that uses supervised machine learning techniques to improve the performances of optimization algorithms in the context of mixed-integer programming (MIP). We focus ... [more ▼] We present in this paper a new approach that uses supervised machine learning techniques to improve the performances of optimization algorithms in the context of mixed-integer programming (MIP). We focus on the branch-and-bound (B&B) algorithm, which is the traditional algorithm used to solve MIP problems. In B&B, variable branching is the key component that most conditions the efficiency of the optimization. Good branching strategies exist but are computationally expensive and usually hinder the optimization rather than improving it. Our approach consists in imitating the decisions taken by a supposedly good branching strategy, strong branching in our case, with a fast approximation. To this end, we develop a set of features describing the state of the ongoing optimization and show how supervised machine learning can be used to approximate the desired branching strategy. The approximated function is created by a supervised machine learning algorithm from a set of observed branching decisions taken by the target strategy. The experiments performed on randomly generated and standard benchmark (MIPLIB) problems show promising results. [less ▲] Detailed reference viewed: 176 (3 ULg)Apprentissage par renforcement batch fondé sur la reconstruction de trajectoires artificielles Fonteneau, Raphaël ; ; Wehenkel, Louis et al in Proceedings of the 9èmes Journées Francophones de Planification, Décision et Apprentissage (JFPDA 2014) (2014) Cet article se situe dans le cadre de l’apprentissage par renforcement en mode batch, dont le problème central est d’apprendre, à partir d’un ensemble de trajectoires, une politique de décision optimisant ... [more ▼] Cet article se situe dans le cadre de l’apprentissage par renforcement en mode batch, dont le problème central est d’apprendre, à partir d’un ensemble de trajectoires, une politique de décision optimisant un critère donné. On considère plus spécifiquement les problèmes pour lesquels l’espace d’état est continu, problèmes pour lesquels les schémas de résolution classiques se fondent sur l’utilisation d’approxima- teurs de fonctions. Cet article propose une alternative fondée sur la reconstruction de “trajectoires arti- ficielles” permettant d’aborder sous un angle nouveau les problèmes classiques de l’apprentissage par renforcement batch. [less ▲] Detailed reference viewed: 61 (5 ULg)On the Encoding of Proteins for Disordered Regions Prediction Becker, Julien ; ; Wehenkel, Louis in PLoS ONE (2013) Disordered regions, i.e., regions of proteins that do not adopt a stable three-dimensional structure, have been shown to play various and critical roles in many biological processes. Predicting and ... [more ▼] Disordered regions, i.e., regions of proteins that do not adopt a stable three-dimensional structure, have been shown to play various and critical roles in many biological processes. Predicting and understanding their formation is therefore a key sub-problem of protein structure and function inference. A wide range of machine learning approaches have been developed to automatically predict disordered regions of proteins. One key factor of the success of these methods is the way in which protein information is encoded into features. Recently, we have proposed a systematic methodology to study the relevance of various feature encodings in the context of disulfide connectivity pattern prediction. In the present paper, we adapt this methodology to the problem of predicting disordered regions and assess it on proteins from the 10th CASP competition, as well as on a very large subset of proteins extracted from PDB. Our results, obtained with ensembles of extremely randomized trees, highlight a novel feature function encoding the proximity of residues according to their accessibility to the solvent, which is playing the second most important role in the prediction of disordered regions, just after evolutionary information. Furthermore, even though our approach treats each residue independently, our results are very competitive in terms of accuracy with respect to the state-of-the-art. A web-application is available at http://m24.giga.ulg.ac.be:81/x3Disorder. [less ▲] Detailed reference viewed: 14 (4 ULg)Understanding variable importances in forests of randomized trees Louppe, Gilles ; Wehenkel, Louis ; Sutera, Antonio et al in Advances in Neural Information Processing Systems 26 (2013, December) Despite growing interest and practical use in various scientific areas, variable importances derived from tree-based ensemble methods are not well understood from a theoretical point of view. In this work ... [more ▼] Despite growing interest and practical use in various scientific areas, variable importances derived from tree-based ensemble methods are not well understood from a theoretical point of view. In this work we characterize the Mean Decrease Impurity (MDI) variable importances as measured by an ensemble of totally randomized trees in asymptotic sample and ensemble size conditions. We derive a three-level decomposition of the information jointly provided by all input variables about the output in terms of i) the MDI importance of each input variable, ii) the degree of interaction of a given input variable with the other input variables, iii) the different interaction terms of a given degree. We then show that this MDI importance of a variable is equal to zero if and only if the variable is irrelevant and that the MDI importance of a relevant variable is invariant with respect to the removal or the addition of irrelevant variables. We illustrate these properties on a simple example and discuss how they may change in the case of non-totally randomized trees such as Random Forests and Extra-Trees. [less ▲] Detailed reference viewed: 1496 (178 ULg)A rich internet application for remote visualization and collaborative annotation of digital slide images in histology and cytology Marée, Raphaël ; Stevens, Benjamin ; Rollus, Loïc et al in Diagnostic Pathology (2013), 8(S1), 26 This work proposes a new web-based tool to ease collaborative projects in digital histology and cytology. Detailed reference viewed: 156 (48 ULg)Batch mode reinforcement learning based on the synthesis of artificial trajectories Fonteneau, Raphaël ; ; Wehenkel, Louis et al in Annals of Operations Research (2013), 208(1), 383-416 Detailed reference viewed: 84 (21 ULg)Computation of worst operation scenarios under uncertainty for static security management ; Wehenkel, Louis in IEEE Transactions on Power Systems (2013), 28(2), 1697-1705 This paper deals with day-ahead static security assessment with respect to a postulated set of contingencies while taking into account uncertainties about the next day system conditions. We propose a ... [more ▼] This paper deals with day-ahead static security assessment with respect to a postulated set of contingencies while taking into account uncertainties about the next day system conditions. We propose a heuristic approach to compute the worst-case under operation uncertainty for a contingency with respect to overloads. We formulate this problem as a non-convex nonlinear bilevel program that we solve approximately by a heuristic approach which relies on the solution of successive optimal power flow (OPF) and security-constrained optimal power flow (SCOPF) problems of a special type. The method aims at revealing those combinations of uncertainties and contingencies for which the best combination of preventive and corrective actions would not suffice to ensure security. Extensive numerical results on a small, a medium, and a very large system prove the interest of the approach. [less ▲] Detailed reference viewed: 80 (10 ULg)An efficient algorithm to perform multiple testing in epistasis screening Van Lishout, François ; Mahachie John, Jestinah ; Gusareva, Elena et al in BMC Bioinformatics (2013), 14 Background: Research in epistasis or gene-gene interaction detection for human complex traits has grown over the last few years. It has been marked by promising methodological developments, improved ... [more ▼] Background: Research in epistasis or gene-gene interaction detection for human complex traits has grown over the last few years. It has been marked by promising methodological developments, improved translation efforts of statistical epistasis to biological epistasis and attempts to integrate different omics information sources into the epistasis screening to enhance power. The quest for gene-gene interactions poses severe multiple-testing problems. In this context, the maxT algorithm is one technique to control the false-positive rate. However, the memory needed by this algorithm rises linearly with the amount of hypothesis tests. Gene-gene interaction studies will require a memory proportional to the squared number of SNPs. A genome-wide epistasis search would therefore require terabytes of memory. Hence, cache problems are likely to occur, increasing the computation time. In this work we present a new version of maxT, requiring an amount of memory independent from the number of genetic effects to be investigated. This algorithm was implemented in C++ in our epistasis screening software MBMDR-3.0.3. We evaluate the new implementation in terms of memory efficiency and speed using simulated data. The software is illustrated on real-life data for Crohn's disease. Results: In the case of a binary (affected/unaffected) trait, the parallel workflow of MBMDR-3.0.3 analyzes all gene-gene interactions with a dataset of 100,000 SNPs typed on 1000 individuals within 4 days and 9 hours, using 999 permutations of the trait to assess statistical significance, on a cluster composed of 10 blades, containing each four Quad-Core AMD Opteron Processor 2352 2.1 GHz. In the case of a continuous trait, a similar run takes 9 days. Our program found 14 SNP-SNP interactions with a multiple-testing corrected p-value of less than 0.05 on real-life Crohn's disease data. Conclusions: Our software is the first implementation of the MB-MDR methodology able to solve large-scale SNP-SNP interactions problems within a few days, without using much memory, while adequately controlling the type I error rates. A new implementation to reach genome-wide epistasis screening is under construction. In the context of Crohn's disease, MBMDR-3.0.3 could identify epistasis involving regions that are well known in the field and could be explained from a biological point of view. This demonstrates the power of our software to find relevant phenotype-genotype higher-order associations. [less ▲] Detailed reference viewed: 72 (21 ULg)On the Relevance of Sophisticated Structural Annotations for Disulfide Connectivity Pattern Prediction Becker, Julien ; ; Wehenkel, Louis in PLoS ONE (2013), 8(2), 56621 Disulfide bridges strongly constrain the native structure of many proteins and predicting their formation is therefore a key sub-problem of protein structure and function inference. Most recently proposed ... [more ▼] Disulfide bridges strongly constrain the native structure of many proteins and predicting their formation is therefore a key sub-problem of protein structure and function inference. Most recently proposed approaches for this prediction problem adopt the following pipeline: first they enrich the primary sequence with structural annotations, second they apply a binary classifier to each candidate pair of cysteines to predict disulfide bonding probabilities and finally, they use a maximum weight graph matching algorithm to derive the predicted disulfide connectivity pattern of a protein. In this paper, we adopt this three step pipeline and propose an extensive study of the relevance of various structural annotations and feature encodings. In particular, we consider five kinds of structural annotations, among which three are novel in the context of disulfide bridge prediction. So as to be usable by machine learning algorithms, these annotations must be encoded into features. For this purpose, we propose four different feature encodings based on local windows and on different kinds of histograms. The combination of structural annotations with these possible encodings leads to a large number of possible feature functions. In order to identify a minimal subset of relevant feature functions among those, we propose an efficient and interpretable feature function selection scheme, designed so as to avoid any form of overfitting. We apply this scheme on top of three supervised learning algorithms: k-nearest neighbors, support vector machines and extremely randomized trees. Our results indicate that the use of only the PSSM (position-specific scoring matrix) together with the CSP (cysteine separation profile) are sufficient to construct a high performance disulfide pattern predictor and that extremely randomized trees reach a disulfide pattern prediction accuracy of on the benchmark dataset SPX+, which corresponds to +3.2% improvement over the state of the art. A web-application is available at http://m24.giga.ulg.ac.be:81/x3CysBridges. [less ▲] Detailed reference viewed: 48 (15 ULg)Extremely Randomized Trees and Random Subwindows for Image Classification, Annotation, and Retrieval Marée, Raphaël ; Wehenkel, Louis ; Geurts, Pierre in Criminisi, A; Shotton, J (Eds.) Decision Forests in Computer Vision and Medical Image Analysis, Advances in Computer Vision and Pattern Recognition (2013) We present a unified framework involving the extraction of random subwindows within images and the induction of ensembles of extremely randomized trees. We discuss the specialization of this framework for ... [more ▼] We present a unified framework involving the extraction of random subwindows within images and the induction of ensembles of extremely randomized trees. We discuss the specialization of this framework for solving several general problems in computer vision, ranging from image classification and segmentation to content-based image retrieval and interest point detection. The methods are illustrated on various applications and datasets from the biomedical domain [less ▲] Detailed reference viewed: 439 (63 ULg)Gene regulatory network inference from systems genetics data using tree-based methods Huynh-Thu, Vân Anh ; Wehenkel, Louis ; Geurts, Pierre in de la Fuente, Alberto (Ed.) Gene Network Inference - Verification of Methods for Systems Genetics Data (2013) One of the pressing open problems of computational systems biology is the elucidation of the topology of gene regulatory networks (GRNs). In an attempt to solve this problem, the idea of systems genetics ... [more ▼] One of the pressing open problems of computational systems biology is the elucidation of the topology of gene regulatory networks (GRNs). In an attempt to solve this problem, the idea of systems genetics is to exploit the natural variations that exist between the DNA sequences of related individuals and that can represent the randomized and multifactorial perturbations necessary to recover GRNs. In this chapter, we present new methods, called GENIE3-SG-joint and GENIE3- SG-sep, for the inference of GRNs from systems genetics data. Experiments on the artificial data of the StatSeq benchmark and of the DREAM5 Systems Genetics challenge show that exploiting jointly expression and genetic data is very helpful for recovering GRNs, and one of our methods outperforms by a large extent the official best performing method of the DREAM5 challenge. [less ▲] Detailed reference viewed: 135 (22 ULg)Whither probabilistic security management for real-time operation of power systems ? Karangelos, Efthymios ; ; Wehenkel, Louis in Bulk Power System Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid (IREP), 2013 IREP Symposium (2013) This paper investigates the stakes of introducing probabilistic approaches for the management of power system’s security. In real-time operation, the aim is to arbitrate in a rational way between ... [more ▼] This paper investigates the stakes of introducing probabilistic approaches for the management of power system’s security. In real-time operation, the aim is to arbitrate in a rational way between preventive and corrective control, while taking into account i) the prior probabilities of contingencies, ii) the possible failure modes of corrective control actions, iii) the socio-economic consequences of service interruptions. This work is a first step towards the construction of a globally coherent decision making framework for security management from long-term system expansion, via mid-term asset management, towards short-term operation planning and real-time operation. [less ▲] Detailed reference viewed: 105 (11 ULg)Scenario Trees and Policy Selection for Multistage Stochastic Programming Using Machine Learning ; Ernst, Damien ; Wehenkel, Louis in INFORMS Journal on Computing (2013), 25(3), 488-501 In the context of multistage stochastic optimization problems, we propose a hybrid strategy for generalizing to nonlinear decision rules, using machine learning, a finite data set of constrained vector ... [more ▼] In the context of multistage stochastic optimization problems, we propose a hybrid strategy for generalizing to nonlinear decision rules, using machine learning, a finite data set of constrained vector-valued recourse decisions optimized using scenario-tree techniques from multistage stochastic programming. The decision rules are based on a statistical model inferred from a given scenario-tree solution and are selected by out-of-sample simulation given the true problem. Because the learned rules depend on the given scenario tree, we repeat the procedure for a large number of randomly generated scenario trees and then select the best solution (policy) found for the true problem. The scheme leads to an ex post selection of the scenario tree itself. Numerical tests evaluate the dependence of the approach on the machine learning aspects and show cases where one can obtain near-optimal solutions, starting with a “weak” scenario-tree generator that randomizes the branching structure of the trees. [less ▲] Detailed reference viewed: 90 (21 ULg)Contingency ranking with respect to overloads in very large power systems taking into account uncertainty, preventive, and corrective actions ; ; et al in IEEE Transactions on Power Systems (2013) This paper deals with day-ahead security management with respect to a postulated set of contingencies, while taking into account uncertainties about the next day generation/load scenario. In order to help ... [more ▼] This paper deals with day-ahead security management with respect to a postulated set of contingencies, while taking into account uncertainties about the next day generation/load scenario. In order to help the system operator in decision making under uncertainty, we aim at ranking these contingencies into four clusters according to the type of control actions needed to cover the worst uncertainty pattern of each contingency with respect to branch overload. To this end we use a fixed point algorithm that loops over two main modules: a discrete bi-level program (BLV) that computes the worst-case scenario, and a special kind of security constrained optimal power flow (SCOPF) which computes optimal preventive/corrective actions to cover the worst-case. We rely on a DC grid model, as the large number of binary variables, the large size of the problem, and the stringent computational requirements preclude the use of existing mixed integer nonlinear programming (MINLP) solvers. Consequently we solve the SCOPF using a mixed integer linear programming (MILP) solver while the BLV is decomposed into a series of MILPs. We provide numerical results with our approach on a very large European system model with 9241 buses and 5126 contingencies. [less ▲] Detailed reference viewed: 67 (8 ULg) |
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