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...
Published in: | Indonesian Journal of Electrical Engineering and Computer Science |
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Institute of Advanced Engineering and Science
2017
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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 |
format |
Article |
accesstype |
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record_format |
scopus |
collection |
Scopus |
_version_ |
1809677908438941696 |