Non-parametric stochastic subset optimization utilizing multivariate boundary kernels and adaptive stochastic sampling

Jia, Gaofeng; Taflanidis, Alexandros A.

doi:10.1016/j.advengsoft.2015.06.014

Cited by 11 publications

(3 citation statements)

References 29 publications

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“…La metodología utilizada se ha basado en el análisis de estadísticos de tendencia central y de dispersión, aplicados a las series de datos de mediciones experimentales obtenidas con el prototipo. En este análisis se han utilizado herramientas estadísticas gráficas como histogramas y diagramas de caja (boxplots), y también se han calculado las curvas de densidad de probabilidad de las series utilizando estimación no paramétrica basada en núcleo (Jia and Taflanidis, 2015). Los resultados de incertidumbre de las pruebas están referidos a una distribución gaussiana normal, con factor de cobertura k=2, y un nivel de confianza p=95%.…”

Section: Resultados Experimentales En Medidas De Parámetros Eléctricosunclassified

Sistema Electrónico Inteligente para el Control de la Interconexión entre Equipamiento de Generación Distribuida y la Red Eléctrica

Real-Calvo¹,

Moreno-Munoz²,

Pallares-Lopez³

et al. 2017

Revista Iberoamericana de Automática e Informática Industrial R

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Section: Resultados Experimentales En Medidas De Parámetros Eléctricosunclassified

Sistema Electrónico Inteligente para el Control de la Interconexión entre Equipamiento de Generación Distribuida y la Red Eléctrica

Real-Calvo¹,

Moreno-Munoz²,

Pallares-Lopez³

et al. 2017

Revista Iberoamericana de Automática e Informática Industrial R

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“…where L l and L u are the lower and upper limits of a random sample, respectively. To improve the approximation at the tail of the KDE, an adaptive KDE was investigated by [52,53,[57][58][59] among others, in which an adaptive bandwidth is used in different regions of the sample space instead of a fixed bandwidth.…”

Section: Kernel Density Estimationmentioning

confidence: 99%

Efficient Reliability-Based Inspection Planning for Deteriorating Bridges Using Extrapolation Approaches

et al. 2022

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In this study, inspection planning of deteriorating bridges is optimized to determine the inspection application times and methods based on various objectives. These objectives can be formulated by considering the probabilistic structural performance and service life after inspection and maintenance. Probabilistic structural performance and service life prediction are generally based on the probability of failure (or reliability). However, there are difficulties associated with optimizing inspection planning when a low probability of failure is estimated. In this study, we address inspection planning using extrapolation approaches to efficiently compute a low probability of failure. The inspection planning method proposed in this study determines the inspection application times for a given inspection method. We investigated the applicability of direct Monte Carlo simulation (MCS), subset simulation, and two extrapolation approaches (i.e., kernel density estimation (KDE) and KDE combined with generalized Pareto distribution (GPD)) for inspection planning. The probability of failure for optimum inspection planning was based on the damage detection-based state function and extended service life-based state function. These state functions were formulated by considering damage propagation, damage detection by inspections, and service life extensions by maintenance. Illustrative applications to general examples and an existing bridge are provided to investigate the effects of approaches for computing the failure probability on the accuracy and variation of the optimum inspection application times. Finally, the most appropriate approach for optimum inspection planning was determined considering the accuracy and reliability of the solution, computational efficiency, and the applicability of the probabilistic optimization process. The presented investigations revealed that KDE is more appropriate than MCS and the combination of KDE and GPD for optimum inspection planning.

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“…The methodology used was based on the analysis of central tendency and dispersion statistics applied to the data series of experimental measurements obtained with the prototype. This analysis used graphical statistical tools such as histograms and boxplots, and also calculated the probability density plots of the series using non-parametric estimation based on kernel [26]. The results of the tests of uncertainty are related to a normal Gaussian distribution, with coverage factor k = 2, and a confidence level of p = 95%.…”

Section: Tests For Quality On Electrical Parameters Measurementsmentioning

confidence: 99%

An Embedded System in Smart Inverters for Power Quality and Safety Functionality

et al. 2016

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The electricity sector is undergoing an evolution that demands the development of a network model with a high level of intelligence, known as a Smart Grid. One of the factors accelerating these changes is the development and implementation of renewable energy. In particular, increased photovoltaic generation can affect the network's stability. One line of action is to provide inverters with a management capacity that enables them to act upon the grid in order to compensate for these problems. This paper describes the design and development of a prototype embedded system able to integrate with a photovoltaic inverter and provide it with multifunctional ability in order to analyze power quality and operate with protection. The most important subsystems of this prototype are described, indicating their operating fundamentals. This prototype has been tested with class A protocols according to IEC 61000-4-30 and IEC 62586-2. Tests have also been carried out to validate the response time in generating orders and alarm signals for protections. The highlights of these experimental results are discussed. Some descriptive aspects of the integration of the prototype in an experimental smart inverter are also commented upon.

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Non-parametric stochastic subset optimization utilizing multivariate boundary kernels and adaptive stochastic sampling

Cited by 11 publications

References 29 publications

Sistema Electrónico Inteligente para el Control de la Interconexión entre Equipamiento de Generación Distribuida y la Red Eléctrica

Sistema Electrónico Inteligente para el Control de la Interconexión entre Equipamiento de Generación Distribuida y la Red Eléctrica

Efficient Reliability-Based Inspection Planning for Deteriorating Bridges Using Extrapolation Approaches

An Embedded System in Smart Inverters for Power Quality and Safety Functionality

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