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
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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.
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