References of "Wehenkel, Louis"      in Complete repository Arts & humanities   Archaeology   Art & art history   Classical & oriental studies   History   Languages & linguistics   Literature   Performing arts   Philosophy & ethics   Religion & theology   Multidisciplinary, general & others Business & economic sciences   Accounting & auditing   Production, distribution & supply chain management   Finance   General management & organizational theory   Human resources management   Management information systems   Marketing   Strategy & innovation   Quantitative methods in economics & management   General economics & history of economic thought   International economics   Macroeconomics & monetary economics   Microeconomics   Economic systems & public economics   Social economics   Special economic topics (health, labor, transportation…)   Multidisciplinary, general & others Engineering, computing & technology   Aerospace & aeronautics engineering   Architecture   Chemical engineering   Civil engineering   Computer science   Electrical & electronics engineering   Energy   Geological, petroleum & mining engineering   Materials science & engineering   Mechanical engineering   Multidisciplinary, general & others Human health sciences   Alternative medicine   Anesthesia & intensive care   Cardiovascular & respiratory systems   Dentistry & oral medicine   Dermatology   Endocrinology, metabolism & nutrition   Forensic medicine   Gastroenterology & hepatology   General & internal medicine   Geriatrics   Hematology   Immunology & infectious disease   Laboratory medicine & medical technology   Neurology   Oncology   Ophthalmology   Orthopedics, rehabilitation & sports medicine   Otolaryngology   Pediatrics   Pharmacy, pharmacology & toxicology   Psychiatry   Public health, health care sciences & services   Radiology, nuclear medicine & imaging   Reproductive medicine (gynecology, andrology, obstetrics)   Rheumatology   Surgery   Urology & nephrology   Multidisciplinary, general & others Law, criminology & political science   Civil law   Criminal law & procedure   Criminology   Economic & commercial law   European & international law   Judicial law   Metalaw, Roman law, history of law & comparative law   Political science, public administration & international relations   Public law   Social law   Tax law   Multidisciplinary, general & others Life sciences   Agriculture & agronomy   Anatomy (cytology, histology, embryology...) & physiology   Animal production & animal husbandry   Aquatic sciences & oceanology   Biochemistry, biophysics & molecular biology   Biotechnology   Entomology & pest control   Environmental sciences & ecology   Food science   Genetics & genetic processes   Microbiology   Phytobiology (plant sciences, forestry, mycology...)   Veterinary medicine & animal health   Zoology   Multidisciplinary, general & others Physical, chemical, mathematical & earth Sciences   Chemistry   Earth sciences & physical geography   Mathematics   Physics   Space science, astronomy & astrophysics   Multidisciplinary, general & others Social & behavioral sciences, psychology   Animal psychology, ethology & psychobiology   Anthropology   Communication & mass media   Education & instruction   Human geography & demography   Library & information sciences   Neurosciences & behavior   Regional & inter-regional studies   Social work & social policy   Sociology & social sciences   Social, industrial & organizational psychology   Theoretical & cognitive psychology   Treatment & clinical psychology   Multidisciplinary, general & others     Showing results 1 to 20 of 318 1 2 3 4 5 6     Bayesian estimates of transmission line outage rates that consider line dependenciesZhou, Kai; Cruise, James R; Dent, Chris J et alin IEEE Transactions on Power Systems (in press)Transmission line outage rates are fundamental to power system reliability analysis. Line outages are infrequent, occurring only about once a year, so outage data are limited. We propose a Bayesian ... [more ▼]Transmission line outage rates are fundamental to power system reliability analysis. Line outages are infrequent, occurring only about once a year, so outage data are limited. We propose a Bayesian hierarchical model that leverages line dependencies to better estimate outage rates of individual transmission lines from limited outage data. The Bayesian estimates have a lower standard deviation than estimating the outage rates simply by dividing the number of outages by the number of years of data, especially when the number of outages is small. The Bayesian model produces more accurate individual line outage rates, as well as estimates of the uncertainty of these rates. Better estimates of line outage rates can improve system risk assessment, outage prediction, and maintenance scheduling. [less ▲]Detailed reference viewed: 19 (2 ULiège) Towards leveraging discrete grid flexibility in chance-constrained power system operation planningKarangelos, Efthymios ; Wehenkel, Louis in Electric Power Systems Research (2020)This paper considers the integration of grid flexibility in the chance-constrained power system operation planning framework. The particular challenge addressed comes from the discrete nature of the ... [more ▼]This paper considers the integration of grid flexibility in the chance-constrained power system operation planning framework. The particular challenge addressed comes from the discrete nature of the respective controls, such as breaker positions defining the topology of the network. We consider a template short-term operation planning problem statement, seeking to enable N-1 secure operation over a distribution of power injections. We use a scenario-based approach to determine a planning decision and rely on theoretical results to compute an upper bound on the probability of being able to meet the N-1 criterion in operation. We also estimate the actual value of this probability through Monte Carlo simulation. Our results indicate that both the bound and the actual value consistently decrease when increasing the size of the considered scenario set, even if the bound is quite conservative. Moreover, we showcase that further from economic efficiency, grid flexibility can lead to gains in operational reliability. [less ▲]Detailed reference viewed: 59 (6 ULiège) Recent Developments in Machine Learning for Energy Systems Reliability ManagementDuchesne, Laurine ; Karangelos, Efthymios ; Wehenkel, Louis in Proceedings of the IEEE (2020)This paper reviews recent works applying machine learning techniques in the context of energy systems reliability assessment and control. We showcase both the progress achieved to date as well as the ... [more ▼]This paper reviews recent works applying machine learning techniques in the context of energy systems reliability assessment and control. We showcase both the progress achieved to date as well as the important future directions for further research, while providing an adequate background in the fields of reliability management and of machine learning. The objective is to foster the synergy between these two fields and speed up the practical adoption of machine learning techniques for energy systems reliability management. We focus on bulk electric power systems and use them as an example, but we argue that the methods, tools, {\it etc.} can be extended to other similar systems, such as distribution systems, micro-grids, and multi-energy systems. [less ▲]Detailed reference viewed: 223 (7 ULiège) Machine Learning for Ranking Day-ahead Decisions in the Context of Short-term Operation PlanningDuchesne, Laurine ; Karangelos, Efthymios ; Sutera, Antonio et alin Electric Power Systems Research (2020)In operation planning, probabilistic reliability assessment consists in evaluating, for various candidate planning decisions, the induced probability of meeting a reliability target and the expected ... [more ▼]In operation planning, probabilistic reliability assessment consists in evaluating, for various candidate planning decisions, the induced probability of meeting a reliability target and the expected operating cost over a certain future time period. In this paper, we propose to exploit Monte-Carlo simulation and machine learning to predict operation costs for various day-ahead unit commitment and economic dispatch decisions and a range of realisations of uncertain loads and renewable generations over the next day. We describe how to generate a database, how to apply supervised machine learning to it, and how to use the learnt proxies to rank candidate day-ahead decisions in terms of the expected operating cost they induce over the next day. We illustrate the approach on the IEEE-RTS96 benchmark where we use the DC power-flow approximation and the N-1 criterion to simulate real-time operation and to generate generation schedules in the day-ahead operation planning stage. [less ▲]Detailed reference viewed: 104 (5 ULiège) An iterative AC-SCOPF approach managing the contingency and corrective control failure uncertainties with a probabilistic guaranteeKarangelos, Efthymios ; Wehenkel, Louis in IEEE Transactions on Power Systems (2019), 34(5), 3780-3790This paper studies an extended formulation of the Security Constrained Optimal Power Flow (SCOPF) problem, which explicitly takes into account the probabilities of contingency events and of potential ... [more ▼]This paper studies an extended formulation of the Security Constrained Optimal Power Flow (SCOPF) problem, which explicitly takes into account the probabilities of contingency events and of potential failures in the operation of post-contingency corrective controls. To manage such threats, we express the requirement that the probability of maintaining all system operational limits, under any circumnstance, should remain acceptably high by means of a chance-constraint. Further, representing power flow as per the full AC model, we propose a heuristic solution approach leveraging state-of-the art methodologies and tools originally developed to tackle the standard, robust-constrained SCOPF statement. We exemplify the properties of our proposal by presenting its application on the three area version of the IEEE-RTS96 benchmark, stressing the interpretability of both the chance-constrained reliability management strategy and of the heuristic algorithm proposed to determine it. This work serves to showcase that the first step on the transition towards probabilistic reliability management can be achieved by suitably adapting presently available operational practices and tools. [less ▲]Detailed reference viewed: 57 (11 ULiège) Static vs dynamic FRR sizing for power systems with increasing amounts of renewablesCauwet, Marie-Liesse; Karangelos, Efthymios ; Wehenkel, Louis et alin IEEE Powertech, Milano, June 2019 (2019, June)This paper investigates the sizing of the Frequency Restoration Reserve (FRR) in a context of increasing penetration of renewable generation. More precisely, we propose (i) a probabilistic method that ... [more ▼]This paper investigates the sizing of the Frequency Restoration Reserve (FRR) in a context of increasing penetration of renewable generation. More precisely, we propose (i) a probabilistic method that mimics the current Belgian TSO (Elia) practices and (ii) a Monte-Carlo based procedure that evaluates the corresponding reliability of the system in terms of down/upward reserves activation, wind curtailment and load shedding. Using this method over the IEEE-RTS96 testcase, the impact of wind penetration - low, moderate, high - is studied. In particular, static (annual and seasonal) and dynamic (weekly and hourly) FRR sizing approaches are defined and compared. It turns out that the hourly sizing method is the most robust. It also appears that FRR requirements for upward reserves are almost not impacted by the high wind penetration whereas the downward reserves increase significantly with the wind penetration. Our implementations rely on Julia, Cplex and R and are available in open source. [less ▲]Detailed reference viewed: 51 (3 ULiège) Chance-Constrained Outage Scheduling using a Machine Learning ProxyDalal, Gal; Gilboa, Elad; Mannor, Shie et alin IEEE Transactions on Power Systems (2019), On-line early accessOutage scheduling aims at defining, over a horizon of several months to years, when different components needing maintenance should be taken out of operation. Its objective is to minimize operation-cost ... [more ▼]Outage scheduling aims at defining, over a horizon of several months to years, when different components needing maintenance should be taken out of operation. Its objective is to minimize operation-cost expectation while satisfying reliability- related constraints. We propose a data-driven distributed chance- constrained optimization formulation for this problem. To tackle tractability issues arising in large networks, we use machine learning to build a proxy for predicting outcomes of power system operation processes in this context. On the IEEE-RTS79 and IEEE-RTS96 networks, our solution obtains cheaper and more reliable plans than other candidates. All our code (matlab) is publicly available at https://github.com/galdl/outage scheduling. [less ▲]Detailed reference viewed: 56 (1 ULiège) Integrating facial detection and recognition algorithms into real-life applicationsVan Lishout, François ; Dubois, Antoine ; Wang, Menglan Linda et alConference (2018, June 27)Performances of facial detection and recognition algorithms on publicly available datasets do not always reflect their true effectiveness in practical real-life applications. Parameters such as distance ... [more ▼]Performances of facial detection and recognition algorithms on publicly available datasets do not always reflect their true effectiveness in practical real-life applications. Parameters such as distance to camera, blur or pose, which vary across datasets, have an important impact on performances. Furthermore, computing speed may also be a key factor for applications requiring real-time decisions. In our department, we work on an application localizing any registered user present in the building in real-time (we also provide an application allowing users to manage their privacy), based only on a few pictures automatically taken during the registration process. In this work, we first compare four open-source facial detection algorithms on the WIDER FACE dataset and on an independent one constructed in our department with volunteers, containing images having a large variation in terms of size, pose, illumination and level of blur. We show that Single Stage Headless Face Detector (SSH) leads to way better precision- recall performances, but is about twice slower than the second best method Faster R-CNN. Second, we compare three open-source facial recognition algorithms on the MegaFace dataset and on our above mentioned one. The latter shows to be much more challenging for all methods, suggesting that publications comparing methods on the former may display performances that cannot be achieved in real-life contexts. We show that InsightFace leads to slightly better precision-recall performances than Dlib, but is about three time slower than the latter. [less ▲]Detailed reference viewed: 148 (21 ULiège) Unit commitment using nearest neighbor as a short-term proxyDalal, G.; Gilboa, E.; Mannor, S. et alin 20th Power Systems Computation Conference, PSCC 2018 (2018, June)We devise the Unit Commitment Nearest Neighbor (UCNN) algorithm to be used as a proxy for quickly approximating outcomes of short-term decisions, to make tractable hierarchical long-term assessment and ... [more ▼]We devise the Unit Commitment Nearest Neighbor (UCNN) algorithm to be used as a proxy for quickly approximating outcomes of short-term decisions, to make tractable hierarchical long-term assessment and planning for large power systems. Experimental results on updated versions of IEEE-RTS79 and IEEE-RTS96 show high accuracy measured on operational cost, achieved in runtimes that are lower in several orders of magnitude than the traditional approach. © 2018 Power Systems Computation Conference. [less ▲]Detailed reference viewed: 35 (1 ULiège) Post-contingency corrective control failure: A risk to neglect or a risk to control?Karangelos, Efthymios ; Wehenkel, Louis in Proc of 2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 (2018, June)This paper proposes a methodology for assessing the risk implied by the potential failure of post-contingency corrective controls. We express such risk in terms of service interruption socio-economic ... [more ▼]This paper proposes a methodology for assessing the risk implied by the potential failure of post-contingency corrective controls. We express such risk in terms of service interruption socio-economic severity to the system end-consumers and argue for considering its magnitude not only in absolute terms, but most importantly in relation to a spectrum of socioeconomic metrics fully describing the operation of an electrical power system as per the applicable reliability management approach (presently based on the N-l criterion). We showcase the proposed methodology by presenting its application through case studies on the single area version of the IEEE-RTS96 benchmark. Our analysis establishes that the proposed assessment scope is quite informative in distinguishing whether the risk implied by the potential failure of post-contingency corrective control is noteworthy or negligible. © 2018 IEEE. [less ▲]Detailed reference viewed: 90 (14 ULiège) Random Subspace with Trees for Feature Selection Under Memory ConstraintsSutera, Antonio ; Châtel, Célia; Louppe, Gilles et alin Storkey, Amos; Perez-Cruz, Fernando (Eds.) Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (2018)Dealing with datasets of very high dimension is a major challenge in machine learning. In this paper, we consider the problem of feature selection in applications where the memory is not large enough to ... [more ▼]Dealing with datasets of very high dimension is a major challenge in machine learning. In this paper, we consider the problem of feature selection in applications where the memory is not large enough to contain all features. In this setting, we propose a novel tree-based feature selection approach that builds a sequence of randomized trees on small subsamples of variables mixing both variables already identified as relevant by previous models and variables randomly selected among the other variables. As our main contribution, we provide an in-depth theoretical analysis of this method in infinite sample setting. In particular, we study its soundness with respect to common definitions of feature relevance and its convergence speed under various variable dependance scenarios. We also provide some preliminary empirical results highlighting the potential of the approach. [less ▲]Detailed reference viewed: 76 (24 ULiège) Using Machine Learning to Enable Probabilistic Reliability Assessment in Operation PlanningDuchesne, Laurine ; Karangelos, Efthymios ; Wehenkel, Louis in Power Systems Computation Conference 2018 Proceedings (2018)In the context of operation planning, probabilistic reliability assessment essentially boils down to predicting, efficiently and with sufficient accuracy, various economic and reliability indicators ... [more ▼]In the context of operation planning, probabilistic reliability assessment essentially boils down to predicting, efficiently and with sufficient accuracy, various economic and reliability indicators reflecting the expected performance of the system over a certain look-ahead horizon, so as to guide the operation planner in his decision-making. In order to speed-up the crude Monte Carlo approach, which would entail a very large number of heavy computations, we propose in this paper an approach combining Monte Carlo simulation, machine learning and variance reduction techniques such as control variates. We provide an extensive case study testing this approach on the three-area IEEE-RTS96 benchmark, in the context of day-ahead operation planning while using a security constrained optimal power flow model to simulate real-time operation according to the N-1 criterion. From this case study, we can conclude that the proposed approach allows to reduce the number of heavy computations by about an order of magnitude, without sacrificing accuracy. [less ▲]Detailed reference viewed: 207 (34 ULiège) Probabilistic Reliability Management Approach and Criteria for Power System Short-term Operational PlanningKarangelos, Efthymios ; Wehenkel, Louis in Probabilistic Reliability Management Approach and Criteria for Power System Short-term Operational Planning (2017, August)This paper develops a probabilistic decision making framework for reliability management in the short-term operational planning context. We build upon our recent work, which proposed a probabilistic ... [more ▼]This paper develops a probabilistic decision making framework for reliability management in the short-term operational planning context. We build upon our recent work, which proposed a probabilistic reliability management approach and criterion (RMAC) for the latest decision making opportunity of real-time system operation. Here, we transpose the RMAC to the preceding problem instance of short-term operational planning, wherein i) risk is aggravated by the uncertainty on power injections and weather conditions, and, ii) the problem scope concerns choosing strategic' actions (e.g., starting additional generating units, granting outage requests for maintenance, etc.) to facilitate decision making during the forthcoming real-time system operation. To anticipate on the latter, we formalize the notion of a real-time proxy' as a simplified model of the real-time decision making context, adequately accurate for the purpose of operational planning decision making. Stating a first proposal for such a proxy, we mathematically formulate the RMAC for short-term operational planning as a multi-stage stochastic decision making problem and demonstrate its main features by case studies on a modified version of the single area IEEE RTS-96 system. [less ▲]Detailed reference viewed: 141 (14 ULiège) A Machine Learning-Based Approximation of Strong BranchingMarcos Alvarez, Alejandro ; Louveaux, Quentin ; Wehenkel, Louis in INFORMS Journal on Computing (2017), 29(1), 185-195We present in this paper a new generic approach to variable branching in branch-and-bound for mixed- integer linear problems. Our approach consists in imitating the decisions taken by a good branching ... [more ▼]We present in this paper a new generic approach to variable branching in branch-and-bound for mixed- integer linear problems. Our approach consists in imitating the decisions taken by a good branching strategy, namely strong branching, with a fast approximation. This approximated function is created by a machine learning technique from a set of observed branching decisions taken by strong branching. The philosophy of the approach is similar to reliability branching. However, our approach can catch more complex aspects of observed previous branchings in order to take a branching decision. The experiments performed on randomly generated and MIPLIB problems show promising results. [less ▲]Detailed reference viewed: 320 (19 ULiège) A computational model of mid-term outage scheduling for long-term system studiesMarin, Manuel ; Karangelos, Efthymios ; Wehenkel, Louis in PowerTech Manchester 2017 Proceedings (2017)This paper presents a computational model of the mid-term outage scheduling process of electric power transmis- sion assets, to be used in long-term studies such as mainte- nance policy assessments and ... [more ▼]This paper presents a computational model of the mid-term outage scheduling process of electric power transmis- sion assets, to be used in long-term studies such as mainte- nance policy assessments and system development studies, while accounting for the impact of outage schedules on short-term system operation. We propose a greedy algorithm that schedules the outages one by one according to their impact on system operation estimated via Monte-Carlo simulations. The model is implemented in JULIA and applied to the IEEE RTS-96. [less ▲]Detailed reference viewed: 75 (9 ULiège) Machine Learning of Real-time Power Systems Reliability Management ResponseDuchesne, Laurine ; Karangelos, Efthymios ; Wehenkel, Louis in PowerTech Manchester 2017 Proceedings (2017)In this paper we study how supervised machine learning could be applied to build simplified models of real-time (RT) reliability management response to the realization of uncertainties. The final ... [more ▼]In this paper we study how supervised machine learning could be applied to build simplified models of real-time (RT) reliability management response to the realization of uncertainties. The final objective is to import these models into look-ahead operation planning under uncertainties. Our response models predict in particular the real-time reliability management costs and the resulting reliability level of the system. We tested our methodology on the IEEE-RTS96 benchmark. Among the supervised learning algorithms tested, extremely randomized trees, kernel ridge regression and neural networks appear to be the best methods for this application. Furthermore, by using feature “importances” computed by tree-based ensemble methods, we were able to extract the most relevant variables to predict the response of real-time reliability management, and thus obtain a better understanding of the system properties. [less ▲]Detailed reference viewed: 297 (52 ULiège) Big data, machine learning, and optimization, for power systems reliabilityWehenkel, Louis Scientific conference (2016, November 09)How to combine physical models with observational data for ensuring power systems reliability, by leveraging simulation, optimisation, and machine learning.Detailed reference viewed: 211 (16 ULiège) Random subspace with trees for feature selection under memory constraintsSutera, Antonio ; Châtel, Célia; Louppe, Gilles et alConference (2016, September 12)Detailed reference viewed: 282 (36 ULiège) Automatic learning of fine operating rules for online power system security controlSun, Hongbin; Zhao, Feng; Wang, Huifang et alin IEEE Transactions on Neural Networks and Learning Systems (2016), 27(8), 1708-1719Fine 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 ▲]Detailed reference viewed: 83 (5 ULiège) Mettre en place des « tableaux » de bord dans l’étude de l’histologie – Une exploration du potentiel pédagogique des traces d’apprentissageVerpoorten, Dominique ; Vincke, Grégoire ; Pesesse, Laurence et alConference (2016, June 06)La communication synthétise les résultats d'un questionnaire visant à estimer les attentes d'enseignants en histologie en matière de visualisations de traces d'apprentissage laissées par leurs étudiants ... [more ▼]La communication synthétise les résultats d'un questionnaire visant à estimer les attentes d'enseignants en histologie en matière de visualisations de traces d'apprentissage laissées par leurs étudiants lorsqu'ils travaillent avec un outil spécialisé (Cytomine) permettant une interaction avec des coupes histologiques numérisées. [less ▲]Detailed reference viewed: 114 (32 ULiège)