Enhancement of simultaneous network reconfiguration and DG sizing via Hamming dataset approach and firefly algorithm

Muhammad, Munir Azam and Mokhlis, Hazlie and Amin, Adil and Naidu, Kanendra and Franco, John Fredy and Wang, Li and Othman, Mohamadariff (2019) Enhancement of simultaneous network reconfiguration and DG sizing via Hamming dataset approach and firefly algorithm. IET Generation, Transmission & Distribution, 13 (22). pp. 5071-5082. ISSN 1751-8687, DOI https://doi.org/10.1049/iet-gtd.2019.0264.

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Official URL: https://doi.org/10.1049/iet-gtd.2019.0264


Deregulation in the electrical industry has led utility companies to ensure high quality of power supply at the customer side. It is of utmost importance for utility companies to operate at maximum efficiency and minimise voltage deviation and power losses. Distributed network reconfiguration (DNR) and integration of distributed generation (DG) are commonly employed to mitigate power loss and voltage deviation. DNR is a complex combinatorial problem which requires radiality verification. Implicit radiality verification increases computational overhead and may lead to local optima. Whereas, improper selection of DG size poses direct consequences on the distribution network mainly on increased voltage deviation and power losses. Therefore, simultaneous optimal integration of DNR and DG is considered in this study to improve the overall performance of the distribution network. Explicit radiality verification is proposed based on Hamming dataset approach to significantly reduce the search space and the computational time, as well as to improve the quality of the solution. Subsequently, firefly algorithm is applied to attain near-optimal solution for NR and DG size. Four cases are considered to validate the effectiveness of the proposed technique including investigation on small, medium, and large-scale distribution network. The results show that the proposed technique is able to consistently attain near optimal-solutions. © The Institution of Engineering and Technology 2019.

Item Type: Article
Funders: University of Malaya Research Grant (GPF055A-2018)
Uncontrolled Keywords: Complex combinatorial problem; Computational overheads; Distributed networks; Electrical industry; Large-scale distribution; Near-optimal solutions; Network re-configuration; Optimal integration
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 04 Mar 2020 00:57
Last Modified: 04 Mar 2020 00:57
URI: http://eprints.um.edu.my/id/eprint/23945

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