[ Choix optimal des équipements entrant dans l’implémentation d’un réseau électrique en utilisant une des méthodes métaheuristiques: Algorithme génétique ]
Volume 36, Issue 3, June 2022, Pages 706–719
Ndjiya Ngasop1, ERNEST KIATA2, and Dendjeu Steve Arthur3
1 Départment de Physiques, Faculté des Sciences (FS), Université de Ngaoundéré, Ngaoundéré, Cameroon
2 Départment de Physiques Faculté des Sciences (FS) de l’Université de Ngaoundéré, Cameroon
3 Department of Electrical Engineering, Energy and Automation, National School of Agro-Industrial Sciences, University of Ngaoundere, Cameroon
Original language: French
Copyright © 2022 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
With the increasing demand for electrical energy, the design of electrical networks is becoming more and more complex to operate according to standards. The choice of devices for the installation of an electrical network would lead to many consequences such as loss of power, deterioration of the line due to overvoltages, etc. As a result, there are several methods of solving difficult problems, including metaheuristic methods. These methods, which appeared in the 1980s, are inspired by natural systems such as the particle swarm (PSO), the ant colony (ACO) and the genetic algorithm method (AG). The latter is a global research and optimization technique that is based on the mechanisms of natural selection and genetics, which can simultaneouly search for several possible solutions. In this work, it is a question of proposing a progam based on a metaheuristic method which will make it possible to optimally choose the elements of an electrical network. To do this, we first used the parameters of the Cameroonian North Interconnected Network (RIN) the proposed a program based on a genetic algorithm that we simulated with the characteristics of the latter using the MATLAB software in order to choose the best devices (conductors, insulators, pylons) for its implementation.
Author Keywords: electrical network, optimization, metaheuristics, genetic algorithm, implementation.
Volume 36, Issue 3, June 2022, Pages 706–719
Ndjiya Ngasop1, ERNEST KIATA2, and Dendjeu Steve Arthur3
1 Départment de Physiques, Faculté des Sciences (FS), Université de Ngaoundéré, Ngaoundéré, Cameroon
2 Départment de Physiques Faculté des Sciences (FS) de l’Université de Ngaoundéré, Cameroon
3 Department of Electrical Engineering, Energy and Automation, National School of Agro-Industrial Sciences, University of Ngaoundere, Cameroon
Original language: French
Copyright © 2022 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
With the increasing demand for electrical energy, the design of electrical networks is becoming more and more complex to operate according to standards. The choice of devices for the installation of an electrical network would lead to many consequences such as loss of power, deterioration of the line due to overvoltages, etc. As a result, there are several methods of solving difficult problems, including metaheuristic methods. These methods, which appeared in the 1980s, are inspired by natural systems such as the particle swarm (PSO), the ant colony (ACO) and the genetic algorithm method (AG). The latter is a global research and optimization technique that is based on the mechanisms of natural selection and genetics, which can simultaneouly search for several possible solutions. In this work, it is a question of proposing a progam based on a metaheuristic method which will make it possible to optimally choose the elements of an electrical network. To do this, we first used the parameters of the Cameroonian North Interconnected Network (RIN) the proposed a program based on a genetic algorithm that we simulated with the characteristics of the latter using the MATLAB software in order to choose the best devices (conductors, insulators, pylons) for its implementation.
Author Keywords: electrical network, optimization, metaheuristics, genetic algorithm, implementation.
Abstract: (french)
Avec la demande croissante de l’énergie électrique, la conception des réseaux électriques devient de plus en plus complexe à opérer selon les normes. Le mauvais choix des dispositifs pour l’implantation d’un réseau électrique entrainerait de nombreuses conséquences telles que les pertes de puissances, la détérioration de la ligne due aux surtensions, etc. De ce fait, il existe plusieurs méthodes de résolution des problèmes difficiles parmi lesquelles les méthodes métaheuristiques. Ces méthodes apparues dans les années 1980 sont inspirées des systèmes naturels tels que l’essaim des particules (PSO), la colonie des fourmis (ACO) et la méthode d’algorithme génétique (AG). Cette dernière est une technique de recherche et d’optimisation globale qui est basée sur les mécanismes de la sélection naturelle et de la génétique, pouvant rechercher simultanément plusieurs solutions possibles. Dans ce travail, il est question de proposer un programme basé sur une méthode métaheuristique qui permettra de choisir de façon optimale les éléments d’un réseau électrique. Pour ce faire, nous avons d’abord utilisé les paramètres du Réseau Interconnecté Nord (RIN) Camerounais puis, proposé un programme basé sur algorithme génétique que nous avons simulé avec les caractéristiques de ce dernier à l’aide du logiciel MATLAB afin de choisir les meilleurs dispositifs (conducteurs, isolateurs, pylônes) en vue de son implémentation.
Author Keywords: réseau électrique, métaheuristique, optimisation, algorithme génétique, implémentation.
How to Cite this Article
Ndjiya Ngasop, ERNEST KIATA, and Dendjeu Steve Arthur, “Optimal choice of equipment for the implementation of an electrical network using one of the metaheuristic methods: Genetic algorithm,” International Journal of Innovation and Applied Studies, vol. 36, no. 3, pp. 706–719, June 2022.
