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

Munir Azam Muhammad, Hazlie Mokhlis, Adil Amin, Kanendra Naidu, John Fredy Franco, Li Wang, Mohamadariff Othman

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)5071-5082
Number of pages12
JournalIET Generation, Transmission and Distribution
Issue number22
Publication statusPublished - 2019 Nov 19

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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