Adaptive Embedded Clonal Evolutionary Programming (AECEP) for optimal Distributed Generation (DG) location and sizing in a distribution system

Adaptive Embedded Clonal Evolutionary Programming (AECEP) for optimal Distributed Generation (DG) location and sizing in a distribution system

Distributed Generation (DG) has gained increasing popularity as a viable element of electric power systems. DG as a small scale generation sources located at or near load center is usually deployed within the distribution system. Installation of DG has many positive impacts such as reducing transmis...

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Main Author: Nur Zahirah, Mohd Ali
Format: Thesis
Language:English
English
English
Published: 2013
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/4919/
http://umpir.ump.edu.my/id/eprint/4919/
http://umpir.ump.edu.my/id/eprint/4919/1/Adaptive%20Embedded%20Clonal%20Evolutionary%20Programming%20%28AECEP%29%20for%20optimal%20Distributed%20Generation%20%28DG%29%20location%20and%20sizing%20in%20a%20distribution%20system%20%28Table%20of%20content%29.pdf
http://umpir.ump.edu.my/id/eprint/4919/6/Adaptive%20Embedded%20Clonal%20Evolutionary%20Programming%20%28AECEP%29%20for%20optimal%20Distributed%20Generation%20%28DG%29%20location%20and%20sizing%20in%20a%20distribution%20system%20%28Abstract%29.pdf
http://umpir.ump.edu.my/id/eprint/4919/11/Adaptive%20Embedded%20Clonal%20Evolutionary%20Programming%20%28AECEP%29%20for%20optimal%20Distributed%20Generation%20%28DG%29%20location%20and%20sizing%20in%20a%20distribution%20system%20%28References%29.pdf
id ump-4919
recordtype eprints
spelling ump-49192017-04-03T04:06:45Z http://umpir.ump.edu.my/id/eprint/4919/ Adaptive Embedded Clonal Evolutionary Programming (AECEP) for optimal Distributed Generation (DG) location and sizing in a distribution system Nur Zahirah, Mohd Ali TK Electrical engineering. Electronics Nuclear engineering Distributed Generation (DG) has gained increasing popularity as a viable element of electric power systems. DG as a small scale generation sources located at or near load center is usually deployed within the distribution system. Installation of DG has many positive impacts such as reducing transmission and distribution network congestion, differing costly for upgrading process, and improving the overall system performance by reducing power losses and enhancing voltage profiles. To achieve these positive impacts from DG installation, the DG has to be optimally placed and sized. Since last decade, Artificial Intelligence (AI) methods have been used to solve complex DG problems because in most cases they can provide global or near global solution. The major advantage of the AI methods is that they are relatively versatile for handling various qualitative constraints. AI methods mainly include Artificial Neural Network (ANN), Expert System (ES), Genetic Algorithm (GA), Evolutionary Programming (EP), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). The purpose of this thesis is to presents a new technique namely Adaptive Embedded Clonal Evolutionary Programming (AECEP). The objective of the study is to employ AECEP optimization technique for loss minimization and voltage profile monitoring. First step study started by using a conventional technique as a pre-study of DG location and sizing. The Heuristic Search Technique (HST) was developed to empirically determine the location and sizing of DG for the same purpose. This technique was performed on the IEEE 41-Bus and 69-Bus RDS for several cases in terms of loading conditions. The proposed AECEP was implemented for single DG, two DGs and three DGs installation. The result of the proposed AECEP technique was found in a good agreement with those obtained from the EP and AIS in terms of loss minimization and voltage profile improvement. 2013-04 Thesis NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/4919/1/Adaptive%20Embedded%20Clonal%20Evolutionary%20Programming%20%28AECEP%29%20for%20optimal%20Distributed%20Generation%20%28DG%29%20location%20and%20sizing%20in%20a%20distribution%20system%20%28Table%20of%20content%29.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/4919/6/Adaptive%20Embedded%20Clonal%20Evolutionary%20Programming%20%28AECEP%29%20for%20optimal%20Distributed%20Generation%20%28DG%29%20location%20and%20sizing%20in%20a%20distribution%20system%20%28Abstract%29.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/4919/11/Adaptive%20Embedded%20Clonal%20Evolutionary%20Programming%20%28AECEP%29%20for%20optimal%20Distributed%20Generation%20%28DG%29%20location%20and%20sizing%20in%20a%20distribution%20system%20%28References%29.pdf Nur Zahirah, Mohd Ali (2013) Adaptive Embedded Clonal Evolutionary Programming (AECEP) for optimal Distributed Generation (DG) location and sizing in a distribution system. Masters thesis, Universiti Malaysia Pahang. http://iportal.ump.edu.my/lib/item?id=chamo:75212&theme=UMP2
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
English
English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Nur Zahirah, Mohd Ali
Adaptive Embedded Clonal Evolutionary Programming (AECEP) for optimal Distributed Generation (DG) location and sizing in a distribution system
description Distributed Generation (DG) has gained increasing popularity as a viable element of electric power systems. DG as a small scale generation sources located at or near load center is usually deployed within the distribution system. Installation of DG has many positive impacts such as reducing transmission and distribution network congestion, differing costly for upgrading process, and improving the overall system performance by reducing power losses and enhancing voltage profiles. To achieve these positive impacts from DG installation, the DG has to be optimally placed and sized. Since last decade, Artificial Intelligence (AI) methods have been used to solve complex DG problems because in most cases they can provide global or near global solution. The major advantage of the AI methods is that they are relatively versatile for handling various qualitative constraints. AI methods mainly include Artificial Neural Network (ANN), Expert System (ES), Genetic Algorithm (GA), Evolutionary Programming (EP), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). The purpose of this thesis is to presents a new technique namely Adaptive Embedded Clonal Evolutionary Programming (AECEP). The objective of the study is to employ AECEP optimization technique for loss minimization and voltage profile monitoring. First step study started by using a conventional technique as a pre-study of DG location and sizing. The Heuristic Search Technique (HST) was developed to empirically determine the location and sizing of DG for the same purpose. This technique was performed on the IEEE 41-Bus and 69-Bus RDS for several cases in terms of loading conditions. The proposed AECEP was implemented for single DG, two DGs and three DGs installation. The result of the proposed AECEP technique was found in a good agreement with those obtained from the EP and AIS in terms of loss minimization and voltage profile improvement.
format Thesis
author Nur Zahirah, Mohd Ali
author_facet Nur Zahirah, Mohd Ali
author_sort Nur Zahirah, Mohd Ali
title Adaptive Embedded Clonal Evolutionary Programming (AECEP) for optimal Distributed Generation (DG) location and sizing in a distribution system
title_short Adaptive Embedded Clonal Evolutionary Programming (AECEP) for optimal Distributed Generation (DG) location and sizing in a distribution system
title_full Adaptive Embedded Clonal Evolutionary Programming (AECEP) for optimal Distributed Generation (DG) location and sizing in a distribution system
title_fullStr Adaptive Embedded Clonal Evolutionary Programming (AECEP) for optimal Distributed Generation (DG) location and sizing in a distribution system
title_full_unstemmed Adaptive Embedded Clonal Evolutionary Programming (AECEP) for optimal Distributed Generation (DG) location and sizing in a distribution system
title_sort adaptive embedded clonal evolutionary programming (aecep) for optimal distributed generation (dg) location and sizing in a distribution system
publishDate 2013
url http://umpir.ump.edu.my/id/eprint/4919/
http://umpir.ump.edu.my/id/eprint/4919/
http://umpir.ump.edu.my/id/eprint/4919/1/Adaptive%20Embedded%20Clonal%20Evolutionary%20Programming%20%28AECEP%29%20for%20optimal%20Distributed%20Generation%20%28DG%29%20location%20and%20sizing%20in%20a%20distribution%20system%20%28Table%20of%20content%29.pdf
http://umpir.ump.edu.my/id/eprint/4919/6/Adaptive%20Embedded%20Clonal%20Evolutionary%20Programming%20%28AECEP%29%20for%20optimal%20Distributed%20Generation%20%28DG%29%20location%20and%20sizing%20in%20a%20distribution%20system%20%28Abstract%29.pdf
http://umpir.ump.edu.my/id/eprint/4919/11/Adaptive%20Embedded%20Clonal%20Evolutionary%20Programming%20%28AECEP%29%20for%20optimal%20Distributed%20Generation%20%28DG%29%20location%20and%20sizing%20in%20a%20distribution%20system%20%28References%29.pdf
first_indexed 2023-09-18T21:59:56Z
last_indexed 2023-09-18T21:59:56Z
_version_ 1777414330396966912

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