| Issue |
E3S Web Conf.
Volume 152, 2020
2019 International Conference on Power, Energy and Electrical Engineering (PEEE 2019)
|
|
|---|---|---|
| Article Number | 03001 | |
| Number of page(s) | 5 | |
| Section | Power Electronics and Transmission Technology | |
| DOI | https://doi.org/10.1051/e3sconf/202015203001 | |
| Published online | 14 February 2020 | |
Computational Intelligence Based Technique for Multi-DG Installation in Transmission System
1
Faculty of Electrical Engineering, Universiti Teknologi MARA, 40000 Shah Alam, Selangor, Malaysia
2
Faculty of Engineering, Technology & Built Environment, UCSI University, 1, Jalan Puncak Menara Gading, Taman Connaught, 56000 Kuala Lumpur, Malaysia
3
Department of Electrical & Electronics, College of Engineering, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysia
* Corresponding author: shahrizal@ucsiuniversity.edu.my
The increasing electricity demand in transmission system has caused the power transmission system experiencing stress condition. This phenomenon has forced the system to need urgent additional supply to maintain system adequacy, in particular the de-regulated power system environment. Distributed generation (DG) has been identified as one of the possible solutions to address this issue. DG installation has the capability to reduce transmission loss and improve the voltage profile. This paper presents evolutionary programming (EP) technique for optimizing the sizing and locations in DG installation. In this study, several DGs have been installed to address the voltage profile improvement and loss minimization; implemented on the IEEE 30-Bus Reliability Test System (RTS). Results obtained from the study revealed that, installation of multi-DGs in a transmission system has significantly minimized the transmission loss along with voltage profile improvement.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.