New Multi Objective Approach for Optimal Network Reconfiguration in Electrical Distribution Systems Using Modified Ant Colony Algorithm
American Journal of Electrical Power and Energy Systems
Volume 8, Issue 5, September 2019, Pages: 120-126
Received: Sep. 16, 2019;
Accepted: Oct. 8, 2019;
Published: Oct. 20, 2019
Views 408 Downloads 144
Arouna Oloulade, Electrotechnic, Telecommunications and Informatics Laboratory (LETIA), University of Abomey-Calavi, Abomey-Calavi, Benin
Adolphe Imano Moukengue, Electronic, Electrotechnic, Automatic, Telecommunications Laboratory (LEEAT), University of Douala, Douala, Cameroun
Richard Agbokpanzo, Polytechnic School of Abomey-Calavi, University of Abomey-Calavi, Abomey-Calavi, Benin
Antoine Vianou, Laboratory of Thermophysic Characterization of Materials and Energy Mastering, University of Abomey-Calavi, Cotonou, Benin
Herman Tamadaho, Electrotechnic, Telecommunications and Informatics Laboratory (LETIA), University of Abomey-Calavi, Abomey-Calavi, Benin
Ramanou Badarou, Polytechnic School of Abomey-Calavi, University of Abomey-Calavi, Abomey-Calavi, Benin
The losses in networks of Beninese Electrical Energy Company (SBEE) are very high and therefore constitute a concern for the operators. This work consisted in finding an optimal topology of a 41 nodes real network of SBEE by Modified Ant Colony Algorithms (MACA) in order to reduce the losses and ensure a continuous power supply to the customers in case of occurrence disturbances on any branch of this network. With technological breakthrough of Automation and Supervision Systems (SCADA), the operation of distribution networks can be ensured remotely in real time with the aim of minimizing losses, eliminating equipment overload and improving reliability. The criteria of technical performance improvement formulated under operating constraints are solved by Modified Ant Colony Algorithm (MACA) which is association of ant system and fuzzy logic on the Matlab platform. The best results obtained show the effectiveness and efficiency of this method. The SBEE's HVA networks can then be reconfigured automatically to significantly improve their continuity of supply and reliability. The improved results obtained after tests on a standard 33-nodes and a real 41 nodes networks show the robustness and accuracy of this MACA algorithm.
Adolphe Imano Moukengue,
New Multi Objective Approach for Optimal Network Reconfiguration in Electrical Distribution Systems Using Modified Ant Colony Algorithm, American Journal of Electrical Power and Energy Systems.
Vol. 8, No. 5,
2019, pp. 120-126.
H. K. Hazaa, H. H. Abdullah, M. H. Yasen, and S. S. Mustafa "Evaluation of electric energy losses in kirkuk distribution elecric system area", Iraqi J Electr Electron Eng, vol. 7, n° %12, pp. 144-150, 2011.
B. Gotzig, «Recherche du schéma optimal d'exploitation électrique,» PhD Thesis, Institut National Polytechnique de Grenoble, 1997.
A. Jain, A. R. Gupta, and A. Kumar, "An efficient method for D-STATCOM placement in radial distribution system", IEEE 6th India International Conference on Power Electronics (IICPE), pp. 1-6, 2014.
DD/SBEE, «Rapport d'analyse des incidents sur le réseau HTA (Période de Juillet à Septembre),» oct. 2017.
A. Merlin, «"Search for minimal-loss operating spanning tree configuration for an urban power distribution system",» Proc 5th PSCC, vol. 1, pp. 1-18, 1975.
J. Li, "Reconfiguration of power networks based on graph-the-oretic algorithm", 2010.
V. Tudor, "Optimal loss Reduction of distribution networks using a refined genetic algorithm", Sci. Bull. C Electr. Eng., vol. 72, n° %13, pp. 29-38, 2010.
Nagy S. A. and I. S. IBRAHIM., "Network Reconfiguration for Loss Reduction in Electrical Distribution System Using Genetic Algorithm", Arab Journal of Nuclear Science and Applications, India Journal of Science and Technology, vol. 46, pp. 78-87, 2013.
Vizhiy S. ARUUL, Santhi R. K. and Nachiappan ALAMELU, "Biogeography based Optimization for Multi-Objective Reconfiguration Problem in Distribution Networks", Indian Journal of Science and Technology, n° 19, pp. 48-52, 2016.
Tamer M. KHALIL SELIM, Alexander GORPINICH and Irena WASIAK, “A selective particle swarm optimization for large scale practical distribution system reconfiguration", June 2015.
Tung TRAN, «"Distribution Network Reconfiguration Using One RankCucckoo Search Algorithm",» GMSARN International Journal, vol. 9, n° %111, 2016.
Abhiraj T. K and P. Aravindhababu, "Dragonfly Optimization based Reconfiguration for Voltage Profile Enhancement in Distribution Systems", International Journal of Computer Application, vol. 158, n° %12, pp. 48-52, 2017.
Abrar TANJUNG, "Reconfiguration of Power Supply System Distribution 20 kV PT PLN (Persero) Dumai Area Case", JOP Conference Series: Earth and Environmental Science, n° %1175, juil 2017.
D. Shirmohammadi and H. W. Hong, "Reconfiguration of distribution networks for resistive line losses reduction", IEEE Trans. Power Deliv, vol. 4, n° %12, pp. 1492-1498, 1989.
A. Oloulade, A. Moukengue Imano, A. Vianou, and H. Tamadaho,"Optimization of the number, size and placement of D-STATCOM in radial distribution network using Ant Colony Algorithm", 2018.
H. B. Tolabi, M. H. Ali, and M. Rizwan, "Simultaneous reconfiguration optimal placement of D-STATCOM and photovolaic array in a distribution system based on fuzzy-ACO approch", IEEE Trans Sustain Energy, vol. 6, n° %11, pp. 210-218, 2015.
A. Oloulade, A. Moukengue Imano, A. Vianou, and H. Tamadaho, "Optimzation multi-critère du placement d'un D-STATCOM dans un réseau de distribution par les Colonies de Fourmis", 2018.
R. K. Jacob et V. Malathi, "Optimization Reconfiguration of Power Distribution Systems", 2015.
R. Srinivasa Rao, "Optimization of Distribution Network Configuration for Loss Reduction Using Artificial Bee Colony Algorithm", International Journal of Electrical and Computer Engineering, vol. 2, n° %19, 2008.