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Recent trends of the most used metaheuristic techniques for distribution network reconfiguration

Quintero Duran, Michell Josep; Candelo Becerra, John Edwin; Sousa Santos, Vladimir (JOURNAL OF Engineering Science and Technology Review, 2017-11-11)

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 ...
Solar radiation prediction for dimensioning photovoltaic systems using artificial neural networks

Noriega Angarita, Eliana Maria; Sousa Santos, Vladimir; Quintero Duran, Michell Josep; Gil Arrieta, Cesar Javier (International Journal of Engineering and Technology, 2016)

This paper presents a prediction model of solar radiation for dimensioning photovoltaic generation systems in the Atlantic Coast of Colombia, using artificial neural networks. As a case of study is presented the municipality ...

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Recent trends of the most used metaheuristic techniques for distribution network reconfiguration
...
Quintero Duran, Michell Josep | 2017-11-11

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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Solar radiation prediction for dimensioning photovoltaic systems using artificial neural networks
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Noriega Angarita, Eliana Maria | 2016

This paper presents a prediction model of solar radiation for dimensioning photovoltaic generation systems in the Atlantic Coast of Colombia, using artificial neural networks. As a case of study is presented the municipality "El Carmen de Bolivar" located in this region. To obtain the model, the average data of daily temperature, relative humidity and solar radiation from the last ten years, reported by weather stations in this city were used. Six neural networks were designed with six variants of input variables (temperature, humidity and month) and the output variable (solar radiation). The best result was obtained using all input variables. In the training process, the correlation index (R) between solar radiation estimated by the model and the recorded data was 0.8. In validating the correlation index was 0.77.

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Listar Artículos científicos por autor "Quintero Duran, Michell Josep"

Recent trends of the most used metaheuristic techniques for distribution network reconfiguration ﻿

Solar radiation prediction for dimensioning photovoltaic systems using artificial neural networks ﻿

Envíos recientes

Recent trends of the most used metaheuristic techniques for distribution network reconfiguration

Solar radiation prediction for dimensioning photovoltaic systems using artificial neural networks

Recent trends of the most used metaheuristic techniques for distribution network reconfiguration

Solar radiation prediction for dimensioning photovoltaic systems using artificial neural networks