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Start Over Topic mathematical optimization Publication Year Range Last 10 years Journal informacion tecnologica
3 results

1. Estimación de Parámetros del Suelo de dos Capas usando el Algoritmo de Optimización de Mapeo Media-Varianza.

Author

Villa-Acevedo, Walter M., Rodríguez-Serna, Johnatan M., and Saldarriaga-Loaiza, Juan D.

Subjects
*SOIL classification, *MATHEMATICAL optimization, *TECHNICAL literature, *SOILS, *MEASUREMENT errors, *ESTIMATION theory
Abstract

This paper presents an analysis of applying the optimization of mean-variance mapping technique in the problem of estimating the parameters of the soil model of two horizontal layers. This problem consists of determining the parameters of the soil model from experimental measurements of apparent resistivity obtained with Wenner method, minimizing the mean square error between values of the experimental and theoretical resistivity curves, which are calculated with mathematical expressions and soil parameters acquired through the optimization of mean-variance mapping technique. Several tests were carried out with resistivity measurements that correspond to different soil types, the results were contrasted with those reported in the technical literature and metaheuristics implemented. In conclusion, according to the results, the performance of the optimization of mean-variance mapping technique was found to be superior than other analyzed optimization techniques. [ABSTRACT FROM AUTHOR]

Published
2019
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2. Optimal budget assignment for service quality improvement in distribution systems using non-Dominated sorting genetic algorithm II (NSGAII) and memetic algorithm

Author

Nicolás Muñoz-Galeano, Luis F Rojas-Ríos, and Jesús M. López-Lezama

Subjects
Mathematical optimization, Algoritmo genético, Computer science, Strategy and Management, Pareto principle, Sorting, Memetic algorithm, NSGAII, Geotechnical Engineering and Engineering Geology, Optimización, Industrial and Manufacturing Engineering, Computer Science Applications, Distribution system, Reduction (complexity), Set (abstract data type), General Energy, Genetic algorithm, Algoritmo memético, Maintenance budget, Presupuesto de mantenimiento, Problem solution, Food Science
Abstract

RESUMEN: Se 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. ABSTRACT: This 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.

Published
2016

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

Author

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

Subjects
Mathematical optimization, Electric losses, Computer science, Strategy and Management, Subroutine, Industrial and Manufacturing Engineering, Neural networks (Computer science), Reduction (complexity), Genetic algorithm, Distribución de energía eléctrica, algoritmos genéticos, Local search (optimization), Perdidas eléctricas, Fitness function, Artificial neural network, Electric power distribution, business.industry, generación distribuida, Genetic algorithms, AC power, Geotechnical Engineering and Engineering Geology, redes neuronales artificiales, Computer Science Applications, General Energy, Redes neurales (computadores), Distributed generation, business, Food Science
Abstract

RESUMEN: En este artículo se presenta una metodología para la ubicación óptima de Generación Distribuida (GD) usando un algoritmo genético híbrido. En este caso la función objetivo es la reducción de pérdidas. El algoritmo propuesto incorpora una red neuronal artificial para evaluar la función de adaptación y una búsqueda local que permite al algoritmo explorar un espacio de búsqueda más amplio. La contribución principal del artículo es la combinación de técnicas metaheurísticas con técnicas de inteligencia artificial para resolver un problema de optimización multi-modal y no convexo. La metodología propuesta es probada en un sistema de 34 barras mostrando que la ubicación apropiada de las unidades de GD permite reducir las pérdidas activas y mejorar el perfil de tensiones. ABSTRACT: This paper presents a methodology for optimal Distributed Generation (DG) allocation using a hybrid genetic algorithm. In this case, the objective function is the minimization of power losses. The proposed algorithm involves an artificial neural network to evaluate the fitness function and a local search subroutine that allows the algorithm to explore a wider search space, and eventually, escape from local optimal solutions. The proposed approach was tested on a 34-bus system showing that the proper allocation of DG units allows the reduction of active power losses and the improvement of voltage profile. COL0010477

Published
2015