Reconfiguration of electrical networks by an Ant Colony Optimization algorithm

Scenna, F.; Anaut, Daniel O.; Passoni, Lucía Isabel; Meschino, Gustavo J.

doi:10.1109/tla.2013.6502858

Cited by 18 publications

(5 citation statements)

References 37 publications

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“…De esta manera se produce una nueva población de posibles soluciones, la cual reemplazará a la anterior verificando la propiedad de que la población siguiente posea mejores características en comparación con la población anterior. Así, a lo largo de diversas iteraciones o generaciones del AG, se espera que las buenas características se hereden (mantengan) a través de las poblaciones mejorando así la factibilidad de las soluciones (Scenna, Anaut, Passoni, & Meschino, 2013).…”

Section: Introductionunclassified

Algoritmo genético para el problema logístico de asignación de la mochila (Knapsack Problem)

Enríquez¹,

Sánchez‐Partida²,

Morales³

2017

RCS

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Resumen. El presente trabajo aborda el caso de estudio de una empresa comercializadora nacional mediante el Problema de la Mochila o Knapsack (KP). Este problema logístico consiste en determinar los productos a almacenar que tengan la mejor relación costo-beneficio para la empresa sin afectar su capacidad de almacenamiento, por lo cual es un problema Binario Multi-criterio del tipo NP-duro. Si bien existen herramientas exactas que pueden resolver algunas instancias de este problema en la práctica pueden tener un costo significativo para la empresa limitando su adquisición. Debido a esto se desarrolló una propuesta de acceso libre de un Algoritmo Genético (AG) para resolver el KP de una manera factible para la empresa. El AG utiliza operadores de cruzamiento aleatorio, cruzamiento uniforme y mutación. El desempeño del AG fue evaluado con un valor óptimo, mostrando un porcentaje de error menor al 4% en comparación de una herramienta comercial exacta.

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Section: Introductionunclassified

Algoritmo genético para el problema logístico de asignación de la mochila (Knapsack Problem)

Enríquez¹,

Sánchez‐Partida²,

Morales³

2017

RCS

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“…Scenna et al identified a wide range of Ant Search algorithms in [34]. According to transition rules, virtual ants are guided considering heuristic movements.…”

Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

Recent Trends of the Most Used Metaheuristic Techniques for Distribution Network Reconfiguration

Quintero-Duran

Candelo-Becerra

Sousa

2017

JESTR

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Distribution network reconfiguration (DNR) continues to be a good option to reduce technical losses in a distribution power grid. However, this non-linear combinatorial problem is not easy to assess by exact methods when solving for large distribution networks, which requires large computational times. For solving this type of problem, some researchers prefer to use metaheuristic techniques due to convergence speed, near-optimal solutions, and simple programming. Some literature reviews specialize in topics concerning the optimization of power network reconfiguration and try to cover most techniques. Nevertheless, this does not allow detailing properly the use of each technique, which is important to identify the trend. The contributions of this paper are three-fold. First, it presents the objective functions and constraints used in DNR with the most used metaheuristics. Second, it reviews the most important techniques such as particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO), immune algorithms (IA), and tabu search (TS). Finally, this paper presents the trend of each technique from 2011 to 2016. This paper will be useful for researchers interested in knowing the advances of recent approaches in these metaheuristics applied to DNR in order to continue developing new best algorithms and improving solutions for the topic.

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“…In the technical literature there are some examples of optimization algorithms applied to the grid reconfiguration problem. For example in [6] ACO algorithm is used for grid reconfiguration according to the following criteria: reduction power losses, balancing loads, service restoration after a fault and minimization of voltage deviation.…”

Section: Overview Of Reconfiguration Algorithmsmentioning

confidence: 99%

Multi-layer agent-based architecture for smart grid reconfiguration

Sanz

Fernández

Rodríguez

et al. 2014

IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society

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Smart grids are an essential tool for efficient management and monitoring of electric power. In this area, distributed programming techniques help to optimize the production and distribution of electrical energy in order to balance supply and demand to ensure better services. In this paper an agent-based distributed architecture for supervision and control of Smart Grids is proposed. The information comes from the equipment installed in the secondary substations of distribution networks of medium and low voltage. It is exchanged through a real-time middleware and processed through a multiagent system. One of the most important tasks implemented in the multi-agent system focuses on fault detection and automatic network reconfiguration. This process allows minimizing power losses and loading balancing in the distribution network. To accomplish this task Particle Swarm Optimization (PSO) algorithm is applied.

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Reconfiguration of electrical networks by an Ant Colony Optimization algorithm

Cited by 18 publications

References 37 publications

Algoritmo genético para el problema logístico de asignación de la mochila (Knapsack Problem)

Algoritmo genético para el problema logístico de asignación de la mochila (Knapsack Problem)

Recent Trends of the Most Used Metaheuristic Techniques for Distribution Network Reconfiguration

Multi-layer agent-based architecture for smart grid reconfiguration

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