This paper is published in Volume-8, Issue-3, 2022
Area
Electrical
Author
Sandeep Kaur, Tejpal Singh
Org/Univ
Baba Banda Singh Bahadur Engineering College, Fatehgarh Sahib, Punjab, India
Pub. Date
26 May, 2022
Paper ID
V8I3-1337
Publisher
Keywords
Power, Distribution, System, Neural Network

Citationsacebook

IEEE
Sandeep Kaur, Tejpal Singh. Power Loss and Voltage Profile in Distribution System by Neural Network, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Sandeep Kaur, Tejpal Singh (2022). Power Loss and Voltage Profile in Distribution System by Neural Network. International Journal of Advance Research, Ideas and Innovations in Technology, 8(3) www.IJARIIT.com.

MLA
Sandeep Kaur, Tejpal Singh. "Power Loss and Voltage Profile in Distribution System by Neural Network." International Journal of Advance Research, Ideas and Innovations in Technology 8.3 (2022). www.IJARIIT.com.

Abstract

The objective of power system operation is to meet the demand at all the locations within the power network as economically and reliably as possible. The traditional electric power generation systems utilize the conventional energy resources, such as fossil fuels, hydro, nuclear etc. for electricity generation. The operation of such traditional generation systems is based on centralized control utility generators, delivering power through an extensive transmission and distribution system, to meet the given demands of widely dispersed users. Nowadays, the justification for the large central-station plants is weakening due to depleting conventional resources, increased transmission and distribution costs, deregulation trends, heightened environmental concerns, and technological advancements. location and size of the DGs before and after radial network reconfiguration are determined using a multi-objective particle swarm optimization technique. In an active distribution network, an ideal network layout with DG coordination eliminates power losses, elevates voltage profiles, and enhances system stability, reliability, and efficiency.