65 research outputs found
Delta-Bar-Delta and directed random search algorithms to study capacitor banks switching overvoltages
This paper introduces an approach to analyse transient overvoltages during
capacitor banks switching based on artificial neural networks (ANN). Three
learning algorithms, delta-bar-delta (DBD), extended delta-bar-delta (EDBD)
and directed random search (DRS) were used to train the ANNs. The ANN
training is based on equivalent parameters of the network and therefore, a
trained ANN is applicable to every studied system. The developed ANN is
trained with extensive simulated results and tested for typical cases. The
new algorithms are presented and demonstrated for a partial 39-bus New
England test system. The simulated results show the proposed technique can
accurately estimate the peak values of switching overvoltages
Extended Delta-Bar-Delta Algorithm Application to Evaluate Transmission Lines Overvoltages
In this paper an intelligent approach is introduced to study switching overvolatges during transmission lines energization. In most countries, the main step in the process of power system restoration, following a complete/partial blackout, is energization of primary restorative transmission lines. An artificial neural network (ANN) has been used to evaluate the overvoltages due to transmission lines energization. Three learning algorithms, delta-bar-delta (DBD), extended delta-bar-delta (EDBD) and directed random search (DRS), were used to train the ANNs. Proposed ANN is trained with equivalent circuit parameters of the network as input parameters; therefore developed ANNs have proper generalization capability. The simulated results for 39-bus New England test system, show that the proposed technique can estimate the peak values and duration of switching overvoltages with acceptable accuracy and EDBD algorithm presents best performance
Estimation d'une densité prédictive avec information additionnelle
Dans le contexte de la thĂ©orie bayĂ©sienne et de thĂ©orie de la dĂ©cision, l'estimation d'une densitĂ© prĂ©dictive d'une variable alĂ©atoire occupe une place importante. Typiquement, dans un cadre paramĂ©trique, il y a prĂ©sence dâinformation additionnelle pouvant ĂȘtre interprĂ©tĂ©e sous forme dâune contrainte. Cette thĂšse porte sur des stratĂ©gies et des amĂ©liorations, tenant compte de lâinformation additionnelle, pour obtenir des densitĂ©s prĂ©dictives efficaces et parfois plus performantes que dâautres donnĂ©es dans la littĂ©rature.
Les rĂ©sultats sâappliquent pour des modĂšles avec donnĂ©es gaussiennes avec ou sans une variance connue. Nous dĂ©crivons des densitĂ©s prĂ©dictives bayĂ©siennes pour les coĂ»ts Kullback-Leibler, Hellinger, Kullback-Leibler inversĂ©, ainsi que pour des coĂ»ts du type divergence et Ă©tablissons des liens avec les familles de lois de probabilitĂ© du type \textit{skew--normal}. Nous obtenons des rĂ©sultats de dominance faisant intervenir plusieurs techniques, dont lâexpansion de la variance, les fonctions de coĂ»t duaux en estimation ponctuelle, lâestimation sous contraintes et lâestimation de Stein. Enfin, nous obtenons un rĂ©sultat gĂ©nĂ©ral pour lâestimation bayĂ©sienne dâun rapport de deux densitĂ©s provenant de familles exponentielles.Abstract: In the context of Bayesian theory and decision theory, the estimation of a predictive density of a random variable represents an important and challenging problem. Typically, in a parametric framework, usually there exists some additional information that can be interpreted as constraints. This thesis deals with strategies and improvements that take into account the additional information, in order to obtain effective and sometimes better performing predictive densities than others in the literature. The results apply to normal models with a known or unknown variance. We describe Bayesian predictive densities for Kullback--Leibler, Hellinger, reverse Kullback-Leibler losses as well as for α--divergence losses and establish links with skew--normal densities. We obtain dominance results using several techniques, including expansion of variance, dual loss functions in point estimation, restricted parameter space estimation, and Stein estimation. Finally, we obtain a general result for the Bayesian estimator of a ratio of two exponential family densities
Directional element for faulty feeder identification of high-resistance fault in high-surety power supply systems
Abstract The highâsurety power supply systems are gaining great attention to enhance the reliability of uninterruptable power supplies. A highâresistance fault along a highâsurety power supply feeder results in a low fault current, making the conventional highâsurety power supply protection strategy ineffective. To address this problem, this paper develops a directional fault protection strategy for highâresistance fault detection and faulty feeder identification. Using the intelligent electronic device, the feeder current is sampled and normalised. Then, the faultâimposed component of the current signal is calculated. This component is added to the input of the forced Helmholtz oscillator to increase the sensitivity of the proposed protection scheme for the detection of highâresistance faults. The output of the forced Helmholtz oscillator equation is adopted as the fault detection criterion because it is infinity for reverse faults while it is lower than 1 for forward faults, facilitating the fault detection. The developed strategy is local and can detect and classify both poleâtoâground and poleâtoâpole highâresistance faults. Also, it is effective for both unidirectional and bidirectional converters. The merits of the proposed protection strategy are demonstrated through several fault scenarios using a ±375 V highâsurety power supply system
A Current Limiting Strategy to Improve Fault Ride-Through of Inverter Interfaced Autonomous Microgrids
- âŠ