Multiple DG planning considering distribution loss and penetration level using EMEFA-ANN method

This paper presents the implementation of multiple distributed generations planning in distribution system using computational intelligence technique. A pre-developed computational intelligence optimization technique named as Embedded Meta EP-Firefly Algorithm (EMEFA) was utilized to determine distr...

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Published in:Indonesian Journal of Electrical Engineering and Computer Science
Main Author: Rahim S.R.A.; Musirin I.; Othman M.M.; Hussain M.H.
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science 2017
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027706513&doi=10.11591%2fijeecs.v7.i1.pp1-8&partnerID=40&md5=22ac4d4edcbaa9c7d806282e9c1f3000
id 2-s2.0-85027706513
spelling 2-s2.0-85027706513
Rahim S.R.A.; Musirin I.; Othman M.M.; Hussain M.H.
Multiple DG planning considering distribution loss and penetration level using EMEFA-ANN method
2017
Indonesian Journal of Electrical Engineering and Computer Science
7
1
10.11591/ijeecs.v7.i1.pp1-8
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027706513&doi=10.11591%2fijeecs.v7.i1.pp1-8&partnerID=40&md5=22ac4d4edcbaa9c7d806282e9c1f3000
This paper presents the implementation of multiple distributed generations planning in distribution system using computational intelligence technique. A pre-developed computational intelligence optimization technique named as Embedded Meta EP-Firefly Algorithm (EMEFA) was utilized to determine distribution loss and penetration level for the purpose of distributed generation (DG) installation. In this study, the Artificial Neural Network (ANN) was used in order to solve the complexity of the multiple DG concepts. EMEFA-ANN was developed to optimize the weight of the ANN to minimize the mean squared error. The proposed method was validated on IEEE 69 Bus distribution system with several load variations scenario. The case study was conducted based on the multiple unit of DG in distribution system by considering the DGs are modeled as type I which is capable of injecting real power. Results obtained from the study could be utilized by the utility and energy commission for loss reduction scheme in distribution system. © 2017 Institute of Advanced Engineering and Science. All rights reserved.
Institute of Advanced Engineering and Science
25024752
English
Article

author Rahim S.R.A.; Musirin I.; Othman M.M.; Hussain M.H.
spellingShingle Rahim S.R.A.; Musirin I.; Othman M.M.; Hussain M.H.
Multiple DG planning considering distribution loss and penetration level using EMEFA-ANN method
author_facet Rahim S.R.A.; Musirin I.; Othman M.M.; Hussain M.H.
author_sort Rahim S.R.A.; Musirin I.; Othman M.M.; Hussain M.H.
title Multiple DG planning considering distribution loss and penetration level using EMEFA-ANN method
title_short Multiple DG planning considering distribution loss and penetration level using EMEFA-ANN method
title_full Multiple DG planning considering distribution loss and penetration level using EMEFA-ANN method
title_fullStr Multiple DG planning considering distribution loss and penetration level using EMEFA-ANN method
title_full_unstemmed Multiple DG planning considering distribution loss and penetration level using EMEFA-ANN method
title_sort Multiple DG planning considering distribution loss and penetration level using EMEFA-ANN method
publishDate 2017
container_title Indonesian Journal of Electrical Engineering and Computer Science
container_volume 7
container_issue 1
doi_str_mv 10.11591/ijeecs.v7.i1.pp1-8
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027706513&doi=10.11591%2fijeecs.v7.i1.pp1-8&partnerID=40&md5=22ac4d4edcbaa9c7d806282e9c1f3000
description This paper presents the implementation of multiple distributed generations planning in distribution system using computational intelligence technique. A pre-developed computational intelligence optimization technique named as Embedded Meta EP-Firefly Algorithm (EMEFA) was utilized to determine distribution loss and penetration level for the purpose of distributed generation (DG) installation. In this study, the Artificial Neural Network (ANN) was used in order to solve the complexity of the multiple DG concepts. EMEFA-ANN was developed to optimize the weight of the ANN to minimize the mean squared error. The proposed method was validated on IEEE 69 Bus distribution system with several load variations scenario. The case study was conducted based on the multiple unit of DG in distribution system by considering the DGs are modeled as type I which is capable of injecting real power. Results obtained from the study could be utilized by the utility and energy commission for loss reduction scheme in distribution system. © 2017 Institute of Advanced Engineering and Science. All rights reserved.
publisher Institute of Advanced Engineering and Science
issn 25024752
language English
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