On Latin hypercube sampling for structural reliability analysis

Olsson, Anders; Sandberg, Göran; Dahlblom, Ola

doi:10.1016/s0167-4730(02)00039-5

Cited by 607 publications

(265 citation statements)

References 19 publications

Supporting

Mentioning

230

Contrasting

Unclassified

Order By: Relevance

“…The scenario-generation procedure using Latin hypercube sampling (LHS) [8] and a fast-backward scenario reduction algorithm are also described in this section. The uncertainties in the number of departing EVs in a specific fleet are assumed to follow normal distribution.…”

Section: Charging Coordination Methods Considering Uncertainty In Ev Dmentioning

confidence: 99%

See 1 more Smart Citation

Stochastic Charging Coordination Method for Electric Vehicle (EV) Aggregator Considering Uncertainty in EV Departures

Kim¹,

Kong²,

Joo³

2016

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

-This paper presents a stochastic method for an electric vehicle (EV) aggregator to coordinate EV charging schedule considering uncertainty in EV departures. The EV aggregator is responsible for managing the charging schedule of EVs while participating in the electricity markets. The managed EV charging can provide additional revenues to the aggregator from regulation market participation and charging cost reductions to EV owners. The aggregator needs to coordinate the charging schedule considering various uncertain factors such as electricity market prices and the stochastic characteristics of EVs. In this paper, the EV charging scheduling problem incorporating uncertainty in EV departures is formulated as a stochastic optimization problem. A stochastic optimization method is used to solve the EV charging scheduling problem. Latin hypercube sampling (LHS) and a scenario-reduction method are also applied to reduce the computational efforts of the proposed method. The results of a numerical example are presented to show the effectiveness of the proposed stochastic EV charging coordination method.

show abstract

Section: Charging Coordination Methods Considering Uncertainty In Ev Dmentioning

confidence: 99%

“…LHS with a Cholesky decomposition can be used to improve the accuracy of the simulation by reducing the possible correlations among the random variable samples [8]. This paper adopts LHS method with Cholesky decomposition as a sampling technique to obtain the random numbers for the generation of EV-departure scenarios.…”

Section: Ev Departure Scenario Generationmentioning

confidence: 99%

Stochastic Charging Coordination Method for Electric Vehicle (EV) Aggregator Considering Uncertainty in EV Departures

Kim¹,

Kong²,

Joo³

2016

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

show abstract

“…The Latin hypercube sampling was first presented in 1979 by McKay et al [48] while its implementation into reliability analysis was extensively presented by Olsson et al [49]. It was widely used as an efficiency improvement tool of different importance sampling methods and it also found use in the sample set preparation for ANN training ( [33]).…”

Section: Ann Training Set Improvement Techniquesmentioning

confidence: 99%

Review and application of Artificial Neural Networks models in reliability analysis of steel structures

et al. 2015

View full text Add to dashboard Cite

“…Cumulative Distribution Function was used as parameter to compare data stability in LHS method and SRS method [8]. The third phase is calculation phase using MSC NASTRAN to obtain pipe deflection.…”

Section: Iconets Conference Proceedingsmentioning

confidence: 99%

“…An evaluation was carried to this program using stress and deflection value resulted in MSC NASTRAN [2]. LHS method was used to increase sampling eficiency and computation time could be reduced approximately by 50% [8]. The objective of sampling is to reduce the variance in process of mean estimation.…”

Section: Introductionmentioning

confidence: 99%

Comparison Of Uncertainty Methods For Pipe Deflection Calculation

Wahanani¹

2016

KEn

View full text Add to dashboard Cite

Reliability of pipe structure is one aspect to be considered in reactor safety analysis. MSC NASTRAN is a computer code that can be used to calculate pipe deflection for reliability evaluation. MSC PATRAN can be used to generate input for this code. Uncertainty evaluation needs to be done in the input variable to understand uncertainty range in the analysis results.A computer code for evaluating structure reliability has been developed in our previous study. The code has implemented latin hypercube sampling (LHS) to assess uncertainty in the input variable, such as load and modulus of elasticity. In this study, comparison of two uncertainty methods, i.e. simple random sampling (SRS) and LHS, was carried out for the developed software. The comparison was subjected to pipe deflection calculation using 100 samples. Comparison analysis shows that LHS method produces a robust mean of variance for all sample size. The results also confirm that variance of pipe deflection using LHS is smaller by 3% than SRS one. It can be concluded that LHS is appropriate to be implemented for uncertainty analysis in the developed code.

show abstract

On Latin hypercube sampling for structural reliability analysis

Cited by 607 publications

References 19 publications

Stochastic Charging Coordination Method for Electric Vehicle (EV) Aggregator Considering Uncertainty in EV Departures

Stochastic Charging Coordination Method for Electric Vehicle (EV) Aggregator Considering Uncertainty in EV Departures

Review and application of Artificial Neural Networks models in reliability analysis of steel structures

Comparison Of Uncertainty Methods For Pipe Deflection Calculation

Contact Info

Product

Resources

About