@thesis{thesis,
		author={Makkulau  Andi and Marulinami  Richold and Yunaini  Ishvandono},
		title ={Penerapan Metode Binary Particle Swarm Optimazition Pada Optimasi Rekonfigurasi Jaringan Distribusi 20kV},
		year={2021},
		url={http://156.67.221.169/3859/},
		abstract={Artificial Intelligence has often been used in developing various sciences such as improvisation programs for network reconfiguration problems. Network reconfiguration is one way to optimize energy flow by opening and closing switches in the distribution network. This study discusses the distribution network configuration for power losses and improves the voltage profile on the NR 7 feeder in Medan. The method used for this research is Binary Particle Swarm Optimization which is a modified development of Particle Swarm Optimization which is designed to solve optimization problems in discrete combinations. This research was tested on a 20kV distribution system in Medan, namely the NR7 feeder. The simulation results from the initial conditions on the NR7 feeder obtained losses of 15.05 kW and a minimum voltage profile of 0.99241 pu or 19.8482 kV. The initial conditions of the open switch in this distribution system are switches 87, 88, 89, 90, 91, 92, 93, 94, 95, 96 and 97. After reconfiguring the network using Binary Particle Swarm Optimization new combinations of open switches are obtained, namely 4, 9, 10, 18, 20, 26, 29, 31, 35, 39, 80 , 49, 96, and 97 so that the results obtained can reduce losses up to 27,17% and can improve the voltage profile 0.144%.}
		}