A case study on optimizing an electrical distribution network using a genetic algorithm

James Fletcher, Tyrone Fernando, Ho Ching Iu, Mark Reynolds, S. Fani

Research output: Chapter in Book/Conference paperConference paper

3 Citations (Scopus)

Abstract

© 2015 IEEE. This paper presents an evolutionary approach for optimizing the topology of rural electrical distribution networks. The primary objective of this project is to determine if the rural distribution network for a case study has expanded in an optimal manner through finding the shortest weighted path between network customers, thereby establishing the cost. Currently, there are large portions of the distribution network assets in rural areas that are nearing end of life and will need to be replaced in the near future. This presents the opportunity to redesign the routing of the network through the consideration of all customers, with the expectation that the length of the network and thus the level of investment will be reduced. The minimum spanning tree (MST) and genetic algorithm (GA) are used to compute the optimized path throughout a constraint weighted area. The results indicate that the optimized path of the network produces a considerable reduction in the total cost.
Original languageEnglish
Title of host publicationIEEE International Symposium on Industrial Electronics Proceedings
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages20-25
Volume2015-September
ISBN (Print)9781467375542
DOIs
Publication statusPublished - 2015
EventA case study on optimizing an electrical distribution network using a genetic algorithm - Buzios, Rio de Janeiro
Duration: 1 Jan 2015 → …

Conference

ConferenceA case study on optimizing an electrical distribution network using a genetic algorithm
Period1/01/15 → …

Fingerprint

Electric power distribution
Genetic algorithms
Costs
Topology

Cite this

Fletcher, J., Fernando, T., Iu, H. C., Reynolds, M., & Fani, S. (2015). A case study on optimizing an electrical distribution network using a genetic algorithm. In IEEE International Symposium on Industrial Electronics Proceedings (Vol. 2015-September, pp. 20-25). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ISIE.2015.7281437
Fletcher, James ; Fernando, Tyrone ; Iu, Ho Ching ; Reynolds, Mark ; Fani, S. / A case study on optimizing an electrical distribution network using a genetic algorithm. IEEE International Symposium on Industrial Electronics Proceedings. Vol. 2015-September IEEE, Institute of Electrical and Electronics Engineers, 2015. pp. 20-25
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Fletcher, J, Fernando, T, Iu, HC, Reynolds, M & Fani, S 2015, A case study on optimizing an electrical distribution network using a genetic algorithm. in IEEE International Symposium on Industrial Electronics Proceedings. vol. 2015-September, IEEE, Institute of Electrical and Electronics Engineers, pp. 20-25, A case study on optimizing an electrical distribution network using a genetic algorithm, 1/01/15. https://doi.org/10.1109/ISIE.2015.7281437

A case study on optimizing an electrical distribution network using a genetic algorithm. / Fletcher, James; Fernando, Tyrone; Iu, Ho Ching; Reynolds, Mark; Fani, S.

IEEE International Symposium on Industrial Electronics Proceedings. Vol. 2015-September IEEE, Institute of Electrical and Electronics Engineers, 2015. p. 20-25.

Research output: Chapter in Book/Conference paperConference paper

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N2 - © 2015 IEEE. This paper presents an evolutionary approach for optimizing the topology of rural electrical distribution networks. The primary objective of this project is to determine if the rural distribution network for a case study has expanded in an optimal manner through finding the shortest weighted path between network customers, thereby establishing the cost. Currently, there are large portions of the distribution network assets in rural areas that are nearing end of life and will need to be replaced in the near future. This presents the opportunity to redesign the routing of the network through the consideration of all customers, with the expectation that the length of the network and thus the level of investment will be reduced. The minimum spanning tree (MST) and genetic algorithm (GA) are used to compute the optimized path throughout a constraint weighted area. The results indicate that the optimized path of the network produces a considerable reduction in the total cost.

AB - © 2015 IEEE. This paper presents an evolutionary approach for optimizing the topology of rural electrical distribution networks. The primary objective of this project is to determine if the rural distribution network for a case study has expanded in an optimal manner through finding the shortest weighted path between network customers, thereby establishing the cost. Currently, there are large portions of the distribution network assets in rural areas that are nearing end of life and will need to be replaced in the near future. This presents the opportunity to redesign the routing of the network through the consideration of all customers, with the expectation that the length of the network and thus the level of investment will be reduced. The minimum spanning tree (MST) and genetic algorithm (GA) are used to compute the optimized path throughout a constraint weighted area. The results indicate that the optimized path of the network produces a considerable reduction in the total cost.

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Fletcher J, Fernando T, Iu HC, Reynolds M, Fani S. A case study on optimizing an electrical distribution network using a genetic algorithm. In IEEE International Symposium on Industrial Electronics Proceedings. Vol. 2015-September. IEEE, Institute of Electrical and Electronics Engineers. 2015. p. 20-25 https://doi.org/10.1109/ISIE.2015.7281437