An importance sampling technique for probabilistic security assessment in power systems with large amounts of wind power

2016 ◽  
Vol 131 ◽  
pp. 11-18 ◽  
Author(s):  
Camille Hamon ◽  
Magnus Perninge ◽  
Lennart Söder
Keyword(s):  
Power Systems ◽  
Wind Power ◽  
Importance Sampling ◽  
Sampling Technique ◽  
Security Assessment ◽  
Importance Sampling Technique

Reliability Assessment of Power System Using Importance Sampling Technique Based on Layer Optimization Simulation

2011 ◽  
Vol 88-89 ◽  
pp. 554-558 ◽  
Author(s):  
Bin Wang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Power System ◽  
Importance Sampling ◽  
System Reliability ◽  
Sampling Method ◽  
Sampling Technique ◽  
Test System ◽  
Importance Sampling Method ◽  
Importance Sampling Technique

An improved importance sampling method with layer simulation optimization is presented in this paper. Through the solution sequence of the components’ optimum biased factors according to their importance degree to system reliability, the presented technique can further accelerate the convergence speed of the Monte-Carlo simulation. The idea is that the multivariate distribution’ optimization of components in power system is transferred to many steps’ optimization based on importance sampling method with different optimum biased factors. The practice is that the components are layered according to their importance degree to the system reliability before the Monte-Carlo simulation, the more forward, the more important, and the optimum biased factors of components in the latest layer is searched while the importance sampling is carried out until the demanded accuracy is reached. The validity of the presented is verified using the IEEE-RTS79 test system.

scholarly journals Bayesian using Importance Sampling Technique of Weibull Regression with Type II Censored Data

2021 ◽  
Vol 2 (3) ◽  
pp. 10-18
Author(s):  
Mohammed Ahmed Al omari
Keyword(s):  
Importance Sampling ◽  
Censored Data ◽  
Shape Parameter ◽  
Sampling Technique ◽  
Type Ii ◽  
Jeffreys Prior ◽  
Weibull Regression ◽  
Type Ii Censored Data ◽  
The Mean ◽  
Importance Sampling Technique

Keeping in view the Bayesian approach, the study aims to develop methods through the utilization of Jeffreys prior and modified Jeffreys prior to the covariate obtained by using the Importance sampling technique. For maximum likelihood estimator, covariate parameters, and the shape parameter of Weibull regression distribution with the censored data of Type II will be estimated by the study. It is shown that the obtained estimators in closed forms are not available, but through the usage of appropriate numerical methods, they can be solved. The mean square error is the criterion of comparison. With the use of simulation, performances of these three estimates are assessed, bearing in mind different censored percentages, and various sizes of the sample.

Importance sampling on coalescent histories. II: Subdivided population models

2004 ◽  
Vol 36 (2) ◽  
pp. 434-454 ◽  
Author(s):  
Maria De Iorio ◽  
Robert C. Griffiths
Keyword(s):  
Importance Sampling ◽  
Dna Sequences ◽  
Sampling Technique ◽  
Population Models ◽  
Gene Trees ◽  
Gene Frequencies ◽  
Sample Distribution ◽  
Subdivided Population ◽  
Importance Sampling Technique ◽  
Ancestral Inference

De Iorio and Griffiths (2004) developed a new method of constructing sequential importance-sampling proposal distributions on coalescent histories of a sample of genes for computing the likelihood of a type configuration of genes in the sample by simulation. The method is based on approximating the diffusion-process generator describing the distribution of population gene frequencies, leading to an approximate sample distribution and finally to importance-sampling proposal distributions. This paper applies that method to construct an importance-sampling algorithm for computing the likelihood of samples of genes in subdivided population models. The importance-sampling technique of Stephens and Donnelly (2000) is thus extended to models with a Markov chain mutation mechanism between gene types and migration of genes between subpopulations. An algorithm for computing the likelihood of a sample configuration of genes from a subdivided population in an infinitely-many-alleles model of mutation is derived, extending Ewens's (1972) sampling formula in a single population. Likelihood calculation and ancestral inference in gene trees constructed from DNA sequences under the infinitely-many-sites model are also studied. The Griffiths-Tavaré method of likelihood calculation in gene trees of Bahlo and Griffiths (2000) is improved for subdivided populations.

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