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Optimal sizing of distributed generation by using quantum-inspired evolutionary programming

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Main Authors: Zuhaila, Mat Yasin, Titik Khawa, Abdul Rahman, Prof. Dr., Ismail, Musirin, Dr., Siti Rafidah, Abd Rahim
Other Authors: yzuhaila@hotmail.com
Format: Working Paper
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE) 2011
Subjects:
Distributed generation
Loss minimization
Quantum mechanics
Quantum-inspired evolutionary programming
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/10443
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spelling my.unimap-104432011-10-21T02:57:59Z Optimal sizing of distributed generation by using quantum-inspired evolutionary programming Zuhaila, Mat Yasin Titik Khawa, Abdul Rahman, Prof. Dr. Ismail, Musirin, Dr. Siti Rafidah, Abd Rahim yzuhaila@hotmail.com takitik@streamyx.com ismailbm@salam.uitm.edu.my Distributed generation Loss minimization Quantum mechanics Quantum-inspired evolutionary programming Link to publisher's homepage at http://ieeexplore.ieee.org/ The paper proposes a novel evolutionary programming inspired by quantum mechanics, called a quantum-inspired evolutionary programming (QIEP). The proposed algorithm consists of three levels, quantum individuals, quantum groups and quantum universes. The proposed algorithm is implemented to determine the optimal sizing of distributed generation (DG) for loss minimization at the optimal location. The location of the distributed generation was identified using the sensitivity indices. In order to demonstrate its performance, comparative studies are performed with conventional evolutionary programming in terms of loss minimization and computation time. The installation of single DG and multiple DG also presented and the results shows better improvement in terms of loss minimization and voltage profile. The proposed study was conducted on the IEEE 69-bus test system. 2011-01-09T05:06:07Z 2011-01-09T05:06:07Z 2010-06-23 Working Paper p. 468-473 978-1-4244-7128-7 http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5559163 http://hdl.handle.net/123456789/10443 en Proceedings of the 4th International Power Engineering and Optimization Conference (PEOCO) 2010 Institute of Electrical and Electronics Engineers (IEEE)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Distributed generation
Loss minimization
Quantum mechanics
Quantum-inspired evolutionary programming
spellingShingle Distributed generation
Loss minimization
Quantum mechanics
Quantum-inspired evolutionary programming
Zuhaila, Mat Yasin
Titik Khawa, Abdul Rahman, Prof. Dr.
Ismail, Musirin, Dr.
Siti Rafidah, Abd Rahim
Optimal sizing of distributed generation by using quantum-inspired evolutionary programming
description Link to publisher's homepage at http://ieeexplore.ieee.org/
author2 yzuhaila@hotmail.com
author_facet yzuhaila@hotmail.com
Zuhaila, Mat Yasin
Titik Khawa, Abdul Rahman, Prof. Dr.
Ismail, Musirin, Dr.
Siti Rafidah, Abd Rahim
format Working Paper
author Zuhaila, Mat Yasin
Titik Khawa, Abdul Rahman, Prof. Dr.
Ismail, Musirin, Dr.
Siti Rafidah, Abd Rahim
author_sort Zuhaila, Mat Yasin
title Optimal sizing of distributed generation by using quantum-inspired evolutionary programming
title_short Optimal sizing of distributed generation by using quantum-inspired evolutionary programming
title_full Optimal sizing of distributed generation by using quantum-inspired evolutionary programming
title_fullStr Optimal sizing of distributed generation by using quantum-inspired evolutionary programming
title_full_unstemmed Optimal sizing of distributed generation by using quantum-inspired evolutionary programming
title_sort optimal sizing of distributed generation by using quantum-inspired evolutionary programming
publisher Institute of Electrical and Electronics Engineers (IEEE)
publishDate 2011
url http://dspace.unimap.edu.my/xmlui/handle/123456789/10443
_version_ 1643789904882171904
score 13.18916

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