The Experts below are selected from a list of 4692 Experts worldwide ranked by ideXlab platform
Jason Frank - One of the best experts on this subject based on the ideXlab platform.
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APPLYING A SPLITTING TECHNIQUE TO ESTIMATE ELECTRICAL GRID RELIABILITY Wander Wadman
2015Co-Authors: Daan Crommelin, Jason FrankAbstract:As intermittent renewable energy penetrates electrical power grids more and more, assessing grid reliability is of increasing concern for grid operators. Monte Carlo simulation is a robust and popular technique to estimate indices for grid reliability, but the involved computational intensity may be too high for typical reliability analyses. We show that various reliability indices can be expressed as expectations depending on the Rare Event Probability of a so-called power curtailment, and explain how to extend a Crude Monte Carlo grid reliability analysis with an existing Rare Event splitting technique. The squared relative error of index estimators can be controlled, whereas orders of magnitude less workload is required than when using an equivalent Crude Monte Carlo method. We show further that a bad choice for the time step size or for the importance function may endanger this squared relative error.
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APPLYING A SPLITTING TECHNIQUE TO ESTIMATE ELECTRICAL GRID RELIABILITY Wander Wadman
2015Co-Authors: Daan Crommelin, Jason FrankAbstract:As intermittent renewable energy penetrates electrical power grids more and more, assessing grid reliability is of increasing concern for grid operators. Monte Carlo simulation is a robust and popular technique to estimate indices for grid reliability, but the involved computational intensity may be too high for typical reliability analyses. We show that various reliability indices can be expressed as expectations depending on the Rare Event Probability of a so-called power curtailment and explain how to extend a Crude Monte Carlo grid reliability analysis with an existing Rare Event splitting technique. The squared relative error of index estimators can be controlled, whereas orders of magnitude less workload is required than when an equivalent Crude Monte Carlo method is used.
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Applying a splitting technique to estimate electrical grid reliability
2013 Winter Simulations Conference (WSC), 2013Co-Authors: Wander Wadman, Daan Crommelin, Jason FrankAbstract:As intermittent renewable energy penetrates electrical power grids more and more, assessing grid reliability is of increasing concern for grid operators. Monte Carlo simulation is a robust and popular technique to estimate indices for grid reliability, but the involved computational intensity may be too high for typical reliability analyses. We show that various reliability indices can be expressed as expectations depending on the Rare Event Probability of a so-called power curtailment, and explain how to extend a Crude Monte Carlo grid reliability analysis with an existing Rare Event splitting technique. The squared relative error of index estimators can be controlled, whereas orders of magnitude less workload is required than when using an equivalent Crude Monte Carlo method. We show further that a bad choice for the time step size or for the importance function may endanger this squared relative error.
Daan Crommelin - One of the best experts on this subject based on the ideXlab platform.
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APPLYING A SPLITTING TECHNIQUE TO ESTIMATE ELECTRICAL GRID RELIABILITY Wander Wadman
2015Co-Authors: Daan Crommelin, Jason FrankAbstract:As intermittent renewable energy penetrates electrical power grids more and more, assessing grid reliability is of increasing concern for grid operators. Monte Carlo simulation is a robust and popular technique to estimate indices for grid reliability, but the involved computational intensity may be too high for typical reliability analyses. We show that various reliability indices can be expressed as expectations depending on the Rare Event Probability of a so-called power curtailment, and explain how to extend a Crude Monte Carlo grid reliability analysis with an existing Rare Event splitting technique. The squared relative error of index estimators can be controlled, whereas orders of magnitude less workload is required than when using an equivalent Crude Monte Carlo method. We show further that a bad choice for the time step size or for the importance function may endanger this squared relative error.
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APPLYING A SPLITTING TECHNIQUE TO ESTIMATE ELECTRICAL GRID RELIABILITY Wander Wadman
2015Co-Authors: Daan Crommelin, Jason FrankAbstract:As intermittent renewable energy penetrates electrical power grids more and more, assessing grid reliability is of increasing concern for grid operators. Monte Carlo simulation is a robust and popular technique to estimate indices for grid reliability, but the involved computational intensity may be too high for typical reliability analyses. We show that various reliability indices can be expressed as expectations depending on the Rare Event Probability of a so-called power curtailment and explain how to extend a Crude Monte Carlo grid reliability analysis with an existing Rare Event splitting technique. The squared relative error of index estimators can be controlled, whereas orders of magnitude less workload is required than when an equivalent Crude Monte Carlo method is used.
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Applying a splitting technique to estimate electrical grid reliability
2013 Winter Simulations Conference (WSC), 2013Co-Authors: Wander Wadman, Daan Crommelin, Jason FrankAbstract:As intermittent renewable energy penetrates electrical power grids more and more, assessing grid reliability is of increasing concern for grid operators. Monte Carlo simulation is a robust and popular technique to estimate indices for grid reliability, but the involved computational intensity may be too high for typical reliability analyses. We show that various reliability indices can be expressed as expectations depending on the Rare Event Probability of a so-called power curtailment, and explain how to extend a Crude Monte Carlo grid reliability analysis with an existing Rare Event splitting technique. The squared relative error of index estimators can be controlled, whereas orders of magnitude less workload is required than when using an equivalent Crude Monte Carlo method. We show further that a bad choice for the time step size or for the importance function may endanger this squared relative error.
Wander Wadman - One of the best experts on this subject based on the ideXlab platform.
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Applying a splitting technique to estimate electrical grid reliability
2013 Winter Simulations Conference (WSC), 2013Co-Authors: Wander Wadman, Daan Crommelin, Jason FrankAbstract:As intermittent renewable energy penetrates electrical power grids more and more, assessing grid reliability is of increasing concern for grid operators. Monte Carlo simulation is a robust and popular technique to estimate indices for grid reliability, but the involved computational intensity may be too high for typical reliability analyses. We show that various reliability indices can be expressed as expectations depending on the Rare Event Probability of a so-called power curtailment, and explain how to extend a Crude Monte Carlo grid reliability analysis with an existing Rare Event splitting technique. The squared relative error of index estimators can be controlled, whereas orders of magnitude less workload is required than when using an equivalent Crude Monte Carlo method. We show further that a bad choice for the time step size or for the importance function may endanger this squared relative error.
Jeanmarc Bourinet - One of the best experts on this subject based on the ideXlab platform.
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Reliability-based sensitivity estimators of Rare Event Probability in the presence of distribution parameter uncertainty
Reliability Engineering and System Safety, 2018Co-Authors: Vincent Chabridon, Jeanmarc Bourinet, Mathieu Balesdent, Jerome Morio, Nicolas GaytonAbstract:This paper aims at presenting sensitivity estimators of a Rare Event Probability in the context of uncertain distribution parameters (which are often not known precisely or poorly estimated due to limited data). Since the distribution parameters are also affected by uncertainties, a possible solution consists in considering a second probabilistic uncertainty level. Then, by propagating this bi-level uncertainty, the failure Probability becomes a random variable and one can use the mean estimator of the distribution of the failure probabilities (i.e. the " predictive failure Probability " , PFP) as a new measure of safety. In this paper, the use of an augmented framework (composed of both basic variables and their Probability distribution parameters) coupled with an Adaptive Importance Sampling strategy is proposed to get an efficient estimation strategy of the PFP. Consequently, double-loop procedure is avoided and the computational cost is decreased. Thus, sensitivity estimators of the PFP are derived with respect to some deterministic hyper-parameters parametrizing a priori modeling choice. Two cases are treated: either the uncertain distribution parameters follow an unbounded Probability law, or a bounded one. The method efficiency is assessed on two different academic test-cases and a real space system computer code (launch vehicle stage fallback zone estimation).
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Rare Event Probability estimation with adaptive support vector regression surrogates
Reliability Engineering & System Safety, 2016Co-Authors: Jeanmarc BourinetAbstract:Abstract Assessing Rare Event probabilities still suffers from its computational cost despite some available methods widely accepted by researchers and engineers. For low to moderately high dimensional problems and under the assumption of a smooth limit-state function, adaptive strategies based on surrogate models represent interesting alternative solutions. This paper presents such an adaptive method based on support vector machine surrogates used in regression. The key idea is to iteratively construct surrogates which quickly explore the safe domain and focus on the limit-state surface in its final stage. Highly accurate surrogates are constructed at each iteration by minimizing an estimation of the leave-one-out error with the cross-entropy method. Additional training points are generated with the Metropolis–Hastings algorithm modified by Au and Beck and a local kernel regression is made over a subset of the known data. The efficiency of the method is tested on examples featuring various challenges: a highly curved limit-state surface at a single most probable failure point, a smooth high-dimensional limit-state surface and a parallel system.
Jerome Morio - One of the best experts on this subject based on the ideXlab platform.
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Reliability-based sensitivity estimators of Rare Event Probability in the presence of distribution parameter uncertainty
Reliability Engineering and System Safety, 2018Co-Authors: Vincent Chabridon, Jeanmarc Bourinet, Mathieu Balesdent, Jerome Morio, Nicolas GaytonAbstract:This paper aims at presenting sensitivity estimators of a Rare Event Probability in the context of uncertain distribution parameters (which are often not known precisely or poorly estimated due to limited data). Since the distribution parameters are also affected by uncertainties, a possible solution consists in considering a second probabilistic uncertainty level. Then, by propagating this bi-level uncertainty, the failure Probability becomes a random variable and one can use the mean estimator of the distribution of the failure probabilities (i.e. the " predictive failure Probability " , PFP) as a new measure of safety. In this paper, the use of an augmented framework (composed of both basic variables and their Probability distribution parameters) coupled with an Adaptive Importance Sampling strategy is proposed to get an efficient estimation strategy of the PFP. Consequently, double-loop procedure is avoided and the computational cost is decreased. Thus, sensitivity estimators of the PFP are derived with respect to some deterministic hyper-parameters parametrizing a priori modeling choice. Two cases are treated: either the uncertain distribution parameters follow an unbounded Probability law, or a bounded one. The method efficiency is assessed on two different academic test-cases and a real space system computer code (launch vehicle stage fallback zone estimation).
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Probabilistic Safety Analysis of the Collision Between a Space Debris and a Satellite with an Island Particle Algorithm
Springer Optimization and Its Applications, 2017Co-Authors: Christelle Vergé, Jerome Morio, Pierre Del Moral, Juan Carlos Dolado PérezAbstract:Collision between satellites and space debris seldom happens, but the loss of a satellite by collision may have catastrophic consequences both for the satellite mission and for the space environment. To support the decision to trigger o a collision avoidance manoeuver, an adapted tool is the determination of the collision Probability between debris and satellite. This Probability estimation can be performed with Rare Event simulation techniques when Monte Carlo techniques are not enough accurate. In this chapter, we focus on analyzing the inuence of dierent simulation parameters (such as the drag coecient) that are set for to simplify the simulation, on the collision Probability estimation. A bad estimation of these simulation parameters can strongly modify Rare Event Probability estimations. We design here a new island particle Markov chain Monte Carlo algorithm to determine the parameters that, in case of bad estimation, tend to increase the collision Probability value. This algorithm also gives an estimate of the collision Probability maximum taking into account the likelihood of the parameters. The principles of this statistical technique are described throughout this chapter.
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Rare Event Probability estimation in the presence of epistemic uncertainty on input Probability distribution parameters
Methodology and Computing in Applied Probability, 2016Co-Authors: Mathieu Balesdent, Jerome Morio, Loic BrevaultAbstract:The accurate estimation of Rare Event probabilities is a crucial problem in engineering to characterize the reliability of complex systems. Several methods such as Importance Sampling or Importance Splitting have been proposed to perform the estimation of such Events more accurately (i.e., with a lower variance) than crude Monte Carlo method. However, these methods assume that the Probability distributions of the input variables are exactly defined (e.g., mean and covariance matrix perfectly known if the input variables are defined through Gaussian laws) and are not able to determine the impact of a change in the input distribution parameters on the Probability of interest. The problem considered in this paper is the propagation of the input distribution parameter uncertainty defined by intervals to the Rare Event Probability. This problem induces intricate optimization and numerous Probability estimations in order to determine the upper and lower bounds of the Probability estimate. The calculation of these bounds is often numerically intractable for Rare Event Probability (say 10−5), due to the high computational cost required. A new methodology is proposed to solve this problem with a reduced simulation budget, using the adaptive Importance Sampling. To this end, a method for estimating the Importance Sampling optimal auxiliary distribution is proposed, based on preceding Importance Sampling estimations. Furthermore, a Kriging-based adaptive Importance Sampling is used in order to minimize the number of evaluations of the computationally expensive simulation code. To determine the bounds of the Probability estimate, an evolutionary algorithm is employed. This algorithm has been selected to deal with noisy problems since the Importance Sampling Probability estimate is a random variable. The efficiency of the proposed approach, in terms of accuracy of the found results and computational cost, is assessed on academic and engineering test cases.
