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Distribution feeder reconfiguration with distributed generation using backward/forward sweep power flow - grey wolf optimizer

This article presents an effective combination method based on Backward/Forward Sweep Power Flow- Grey Wolf optimizer (BFSPF-GWO) for feeder reconfiguration in a distribution network with the presence of distributed generation (DG). The 33-bus test system by adding five tie line switches is proposed...

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Bibliographic Details
Main Authors: Syed Muhammad Fadli, Syed Drus, Norhafidzah, Mohd Saad, Mohammad Fadhil, Abas, Suliana, Ab Ghani, Norazila, Jaalam, Ali, Abid
Format: Conference or Workshop Item
Language:English
English
Published: Institute of Electrical and Electronics Engineers Inc. 2023
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/38776/1/Distribution%20feeder%20reconfiguration%20with%20distributed%20generation.pdf
http://umpir.ump.edu.my/id/eprint/38776/2/Distribution%20feeder%20reconfiguration%20with%20distributed%20generation%20using%20backward_forward%20sweep%20power%20flow%20-%20grey%20wolf%20optimizer_ABS.pdf
http://umpir.ump.edu.my/id/eprint/38776/
https://doi.org/10.1109/CSPA57446.2023.10087738
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Summary:This article presents an effective combination method based on Backward/Forward Sweep Power Flow- Grey Wolf optimizer (BFSPF-GWO) for feeder reconfiguration in a distribution network with the presence of distributed generation (DG). The 33-bus test system by adding five tie line switches is proposed with the objective functions of minimizing total power losses and improving the voltage profiles. The results reveal a reduction in active and reactive power losses at 71.41% and 67.66%, respectively. The optimal sizing of DG and installation location are identified by installing a 2.26 MW DG at bus 29. The magnitudes of voltage profiles and critical buses in the test system have been improved. The proposed BFSPF-GWO algorithm's performance in DG placement and sizing with feeder reconfiguration has been evaluated by comparing the results with Mixed-integer optimization by GA (MIOGA) and Particle Swarm optimization (PSO).

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