Embedded adaptive mutation evolutionary programming for distributed generation management
Distribution generation (DG) is a widely used term to describe additional supply to a power system network. Normally, DG is installed in distribution network because of its small capacity of power. Number of DGs connected to distribution system has been increasing rapidly as the world heading to inc...
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| Main Authors: | , , , , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2019-10-01.
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| Online Access: | Get fulltext |
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| LEADER | 02478 am a22003373u 4500 | ||
|---|---|---|---|
| 001 | ijeecs19902_12994 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Mohd Zulkefli, Muhammad Fathi |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Musirin, Ismail |e author |
| 700 | 1 | 0 | |a Jelani, Shahrizal |e author |
| 700 | 1 | 0 | |a Helmi Mansor, Mohd |e author |
| 700 | 1 | 0 | |a S. Honnoon, Naeem M. |e author |
| 245 | 0 | 0 | |a Embedded adaptive mutation evolutionary programming for distributed generation management |
| 260 | |b Institute of Advanced Engineering and Science, |c 2019-10-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/19902 | ||
| 520 | |a Distribution generation (DG) is a widely used term to describe additional supply to a power system network. Normally, DG is installed in distribution network because of its small capacity of power. Number of DGs connected to distribution system has been increasing rapidly as the world heading to increase their dependency on renewable energy sources. In order to handle this high penetration of DGs into distribution network, it is crucial to place the DGs at optimal location with optimal size of output. This paper presents the implementation of Embedded Adaptive Mutation Evolutionary Programming technique to find optimal location and sizing of DGs in distribution network with the objective of minimizing real power loss. 69-Bus distribution system is used as the test system for this implementation. From the presented case studies, it is found that the proposed embedded optimization technique successfully determined the optimal location and size of DG units to be installed in the distribution network so that the real power loss is reduced. | ||
| 540 | |a Copyright (c) 2019 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Adaptive mutation, Distributed generation, Embedded optimization, Evolutionary programming | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 16, No 1: October 2019; 364-370 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v16.i1 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/19902/12994 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/19902/12994 |z Get fulltext |