Ubicación de Generación Distribuida para Minimización de Pérdidas Usando un Algoritmo Genético Híbrido

Narváez, Pablo A; López-Lezama, Jesús M.; Velilla, Esteban

doi:10.4067/s0718-07642015000300016

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…Then, electrical devices may be damaged due to modification in the equivalent load seen by the regulator or compensator. To obtain the maximum advantages of DG's, they must be located in the proper sited, to not interfere with the distribution network and to reduce their limitations about power injection (Grisales Noreña, Restrepo Cuestas, & Jaramillo Ramirez, 2017; Lepadat, Helerea, Abagiu, & Mihai, 2017;López-Lezama, Buitrago, & Villada, 2015;Narváez, López-Lezama, & Velilla, 2015).…”

Section: Voltage Regulationmentioning

confidence: 99%

The Why of Adaptive Protections in Modern Electrical Networks

Montenegro¹,

Gómez-Luna²,

Sáenz³

et al. 2019

Ing. Inv.

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Electrical networks are evolving and taking on more challenges as the inclusion of renewable energy and distributed generation units increase, specially at distribution levels. Big trends of generating electricity with alternative and renewable resources has promoted the formation of distribution networks subsystems or micro grids, capable of supplying their own electric demand and to export energy to the interconnected system, if necessary. However, the effects of these generation units into the network and into the microgrid as well are many, as harmonic distortion, voltage flickers and especially in electrical protections.This paper provides an overview about implementation of renewable energy and distributed generation worldwide, as well as an introduction to microgrids concept and its main impacts and challenges into the electric systems. Finally, the main impacts of microgrid on protection equipments are presented at a distribution level, being adaptive protections one of the solutions to the dynamic changes of the electric system.

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Section: Voltage Regulationmentioning

confidence: 99%

The Why of Adaptive Protections in Modern Electrical Networks

Montenegro¹,

Gómez-Luna²,

Sáenz³

et al. 2019

Ing. Inv.

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“…The GA was defined by a mutation rate of 0.95 with 100 individuals in the population and 40 generations, tournament selection, and rank algorithm is considered [50]. The chromosome is determined by the five parameters of interest and the fitness function by the mean square error (MSE) between the current measured (I exp ) and the one-diode model evaluated at each point of the I-V curve, Equation (3).…”

Section: Extracting Parameters Using Different Exact and Numerical Mementioning

confidence: 99%

Numerical Analysis to Determine Reliable One-Diode Model Parameters for Perovskite Solar Cells

et al. 2018

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Abstract:With the aim to determine the photo-generated current, diode saturation current, ideality factor, shunt, and series resistances related to the one-diode model for p-i-n planar perovskite solar cells, reference cells with active area of approximately 1 cm 2 and efficiencies ranging between 4.6 and 12.2% were fabricated and characterized at standard test conditions. To estimated feasible parameters, the mean square error between the I-V curve data of these cells and the circuital model results were minimized using a Genetic Algorithm combined with the Nelder-Mead method. When considering the optimization process solutions, a numerical sensitivity analysis of the error as a function of the estimated parameters was carried out. Based on the errors behavior that is showed graphically through maps, it was demonstrated that the set of parameters estimated for each cell were reliable, meaningful, and realistic, and being related to errors lower than 9.1 × 10 −9 . Therefore, these results can be considered as global solutions of the optimization process. Moreover, based on the lower errors obtained from the optimization process, it was possible to affirm that the one-diode model is suitable to model the I-V curve of perovskite solar cells. Finally, the estimated parameters suggested that the average ideality factor is close to 2 when the fill factor of the I-V curves is higher than 0.5. Lower fill factors corresponded to ideality that was higher than 3, linked to lower efficiencies, and high loses effects reflected on lower shunt resistances. Lower ideality factor of 1.4 corresponds to the best performing solar cells.

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“…Los Algoritmos Genéticos pertenecen a las metaheuritsticas poblaciones que pueden ser utilizadas para la solución de problemas mono o multiobjetivo (Narváez et al, 2015), (García et al, 2012). En el caso de problemas multiobjetivo se debe tener en cuenta el concepto de dominancia.…”

Section: Algoritmo Genético De Clasificación No-dominada (Nsga Ii)unclassified

Asignación Óptima de Presupuesto para Mejoramiento de la Calidad del Servicio en Sistemas de Distribución usando Algoritmo Genético No-Dominado II (NSGA-II) y un Algoritmo Memético

Rojas-Ríos¹,

López-Lezama²,

Muñóz-Galeano

2016

Inf. tecnol.

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ResumenSe presenta un modelo de asignación óptima de presupuesto para mejoramiento de calidad del servicio en sistemas de distribución. El modelo consiste en un problema multiobjetivo que busca al mismo tiempo minimizar el costo de mantenimiento en sistemas de distribución y maximizar la reducción de la tasa de fallas. Este último objetivo se evalúa a través del indicador SAIFI (Frecuencia Media de Interrupción del Sistema). Para resolver el modelo propuesto se implementaron dos algoritmos poblacionales: Algoritmo Genético No-Dominado II (NSGA-II) y un Algoritmo Memético. Se realizan pruebas con dos sistemas eléctricos reales del Departamento de Antioquia en Colombia de 100 y 200 nodos, mostrando la aplicabilidad del modelo propuesto. Los frentes de Pareto óptimos obtenidos en la solución del problema muestran un set de posibles soluciones que representan un compromiso entre ambos objetivos y le dan al operador de red un estimado de cuánto debe invertir en mantenimiento para lograr un valor deseado del indicador SAIFI. Palabras AbstractThis paper presents an optimal budget assignment model for improving quality service in distribution systems. The model consists on a multi objective problem which aims at minimizing maintenance costs in distribution systems while maximizing the reduction of faults rate. This last objective is measured through the SAIFI indicator (System Average Interruption Frequency Index). To solve the proposed model two algorithms were implemented: NSGAII (Non-Dominated Sorting Genetic Algorithm II) and a Memetic Algorithm. Several tests were performed with two real electrical systems of 100 and 200 nodes in the Department of Antioquia in Colombia, showing the applicability of the proposed approach. The optimal Pareto fronts obtained in the problem solution show a set of available options that represent a trade-off between both objectives and provides the system operator with an estimate of how much to invest in maintenance to achieve a desired value of the SAIFI indicator.

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Ubicación de Generación Distribuida para Minimización de Pérdidas Usando un Algoritmo Genético Híbrido

Cited by 4 publications

References 19 publications

The Why of Adaptive Protections in Modern Electrical Networks

The Why of Adaptive Protections in Modern Electrical Networks

Numerical Analysis to Determine Reliable One-Diode Model Parameters for Perovskite Solar Cells

Asignación Óptima de Presupuesto para Mejoramiento de la Calidad del Servicio en Sistemas de Distribución usando Algoritmo Genético No-Dominado II (NSGA-II) y un Algoritmo Memético

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