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Computational Intelligence Based Technique for Multi-DG Installation in Transmission System

Computer programming; Electric power transmission; Installation; Transmissions; Wave transmission; Electricity demands; Loss minimization; Power transmission systems; Reliability test system; System environment; Transmission loss; Transmission systems; Voltage profile improvement; Electric load loss

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Bibliographic Details
Main Authors: Sahilahudin M.F.F., Musirin I., Jelani S., Mansor M.H.
Other Authors: 57215061260
Format: Conference Paper
Published: EDP Sciences 2023
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spelling my.uniten.dspace-255732023-05-29T16:11:07Z Computational Intelligence Based Technique for Multi-DG Installation in Transmission System Sahilahudin M.F.F. Musirin I. Jelani S. Mansor M.H. 57215061260 8620004100 57193388570 56372667100 Computer programming; Electric power transmission; Installation; Transmissions; Wave transmission; Electricity demands; Loss minimization; Power transmission systems; Reliability test system; System environment; Transmission loss; Transmission systems; Voltage profile improvement; Electric load loss 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. � 2020 The Authors, published by EDP Sciences. Final 2023-05-29T08:11:06Z 2023-05-29T08:11:06Z 2020 Conference Paper 10.1051/e3sconf/202015203001 2-s2.0-85079747539 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079747539&doi=10.1051%2fe3sconf%2f202015203001&partnerID=40&md5=150993c6039e26f35910456273332968 https://irepository.uniten.edu.my/handle/123456789/25573 152 3001 All Open Access, Gold EDP Sciences Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Computer programming; Electric power transmission; Installation; Transmissions; Wave transmission; Electricity demands; Loss minimization; Power transmission systems; Reliability test system; System environment; Transmission loss; Transmission systems; Voltage profile improvement; Electric load loss
author2 57215061260
author_facet 57215061260
Sahilahudin M.F.F.
Musirin I.
Jelani S.
Mansor M.H.
format Conference Paper
author Sahilahudin M.F.F.
Musirin I.
Jelani S.
Mansor M.H.
spellingShingle Sahilahudin M.F.F.
Musirin I.
Jelani S.
Mansor M.H.
Computational Intelligence Based Technique for Multi-DG Installation in Transmission System
author_sort Sahilahudin M.F.F.
title Computational Intelligence Based Technique for Multi-DG Installation in Transmission System
title_short Computational Intelligence Based Technique for Multi-DG Installation in Transmission System
title_full Computational Intelligence Based Technique for Multi-DG Installation in Transmission System
title_fullStr Computational Intelligence Based Technique for Multi-DG Installation in Transmission System
title_full_unstemmed Computational Intelligence Based Technique for Multi-DG Installation in Transmission System
title_sort computational intelligence based technique for multi-dg installation in transmission system
publisher EDP Sciences
publishDate 2023
_version_ 1806425548818219008
score 13.222552

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