References of "Wehenkel, Louis"
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See detailAn iterative AC-SCOPF approach managing the contingency and corrective control failure uncertainties with a probabilistic guarantee
Karangelos, Efthymios ULiege; Wehenkel, Louis ULiege

in IEEE Transactions on Power Systems (in press)

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 ... [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 ▲]

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See detailStatic vs dynamic FRR sizing for power systems with increasing amounts of renewables
Cauwet, Marie-Liesse; Karangelos, Efthymios ULiege; Wehenkel, Louis ULiege et al

in 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 ▲]

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See detailChance-Constrained Outage Scheduling using a Machine Learning Proxy
Dalal, Gal; Gilboa, Elad; Mannor, Shie et al

in IEEE Transactions on Power Systems (2019), On-line early access

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 ... [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 ▲]

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See detailIntegrating facial detection and recognition algorithms into real-life applications
Van Lishout, François ULiege; Dubois, Antoine ULiege; Wang, Menglan Linda ULiege et al

Conference (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 ▲]

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See detailUnit commitment using nearest neighbor as a short-term proxy
Dalal, G.; Gilboa, E.; Mannor, S. et al

in 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 ▲]

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See detailPost-contingency corrective control failure: A risk to neglect or a risk to control?
Karangelos, Efthymios ULiege; Wehenkel, Louis ULiege

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 ▲]

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See detailRandom Subspace with Trees for Feature Selection Under Memory Constraints
Sutera, Antonio ULiege; Châtel, Célia; Louppe, Gilles ULiege et al

in 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 ▲]

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See detailUsing Machine Learning to Enable Probabilistic Reliability Assessment in Operation Planning
Duchesne, Laurine ULiege; Karangelos, Efthymios ULiege; Wehenkel, Louis ULiege

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 ▲]

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See detailProbabilistic Reliability Management Approach and Criteria for Power System Short-term Operational Planning
Karangelos, Efthymios ULiege; Wehenkel, Louis ULiege

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 ▲]

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See detailA Machine Learning-Based Approximation of Strong Branching
Marcos Alvarez, Alejandro ULiege; Louveaux, Quentin ULiege; Wehenkel, Louis ULiege

in INFORMS Journal on Computing (2017), 29(1), 185-195

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 ... [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 ▲]

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See detailA computational model of mid-term outage scheduling for long-term system studies
Marin, Manuel ULiege; Karangelos, Efthymios ULiege; Wehenkel, Louis ULiege

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 ▲]

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See detailMachine Learning of Real-time Power Systems Reliability Management Response
Duchesne, Laurine ULiege; Karangelos, Efthymios ULiege; Wehenkel, Louis ULiege

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 ▲]

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See detailBig data, machine learning, and optimization, for power systems reliability
Wehenkel, Louis ULiege

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.

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See detailRandom subspace with trees for feature selection under memory constraints
Sutera, Antonio ULiege; Châtel, Célia; Louppe, Gilles ULiege et al

Conference (2016, September 12)

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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 (2016), 27(8), 1708-1719

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 detailMettre en place des « tableaux » de bord dans l’étude de l’histologie – Une exploration du potentiel pédagogique des traces d’apprentissage
Verpoorten, Dominique ULiege; Vincke, Grégoire ULiege; Pesesse, Laurence ULiege et al

Conference (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 ▲]

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See detailContext-dependent feature analysis with random forests
Sutera, Antonio ULiege; Louppe, Gilles ULiege; Huynh-Thu, Vân Anh ULiege et al

in Uncertainty In Artificial Intelligence: Proceedings of the Thirty-Two Conference (2016) (2016, June)

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See detailProbabilistic reliability management approach and criteria for power system real-time operation
Karangelos, Efthymios ULiege; Wehenkel, Louis ULiege

in Power Systems Computation Conference (2016, June)

This paper develops a probabilistic approach for power system reliability management in real-time operation where risk is a product of i) the potential occurrence of contingencies, ii) the possible ... [more ▼]

This paper develops a probabilistic approach for power system reliability management in real-time operation where risk is a product of i) the potential occurrence of contingencies, ii) the possible failure of corrective (i.e., post-contingency) control and, iii) the socio-economic impact of service interruptions to end-users. Stressing the spatiotemporal variability of these factors, we argue for reliability criteria assuring a high enough probability of avoiding service interruptions of severe socio-economic impact by dynamically identifying events of nonnegligible implied risk. We formalise the corresponding decision making problem as a chance-constrained two-stage stochastic programming problem, and study its main features on the single area IEEE RTS-96 system. We also discuss how to leverage this proposal for the construction of a globally coherent reliability management framework for long-term system development, midterm asset management, and short-term operation planning. [less ▲]

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See detailComments on: A random forest guided tour
Geurts, Pierre ULiege; Wehenkel, Louis ULiege

in TEST (2016), 25(2), 247-253

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See detailFramework for Threat Based Failure Rates in Transmission System Operation
Perkin, Samuel; Bjornsson, Gudjon; Baldursdottir, Iris et al

in Framework for Threat Based Failure Rates in Transmission System Operation (2016, February)

Reliability of electrical transmission systems isvpresently managed by applying the deterministic N-1 criterion, or some variant thereof. This means that transmission systems are designed with at least ... [more ▼]

Reliability of electrical transmission systems isvpresently managed by applying the deterministic N-1 criterion, or some variant thereof. This means that transmission systems are designed with at least one level of redundancy, regardless of the cost of doing so, or the severity of the risks they mitigate. In an operational context, the N-1 criterion provides a reliability target but it fails to accurately capture the dynamic nature of short-term threats to transmission systems. Ongoing research aims to overcome this shortcoming by proposing new probabilistic reliability criteria. Such new criteria are anticipated to rely heavily on component failure rate calculations. This paper provides a threat modelling framework, using the Icelandic transmission system as an example, highlighting the need for improved data collection and failure rate modelling. The feasibility of using threat credibility indicators to achieve spatio-temporal failure rates, given minimal data, is explored in a case study of the Icelandic transmission system. The paper closes with a discussion on the assumptions and simplifications that are implicitly made in the formulation, and the additional work required for such an approach to be included in existing practices. Specifically, this paper is concerned only with short term and real-time management of electrical transmission systems. [less ▲]

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