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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| Format: | Thesis |
| Language: | English |
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2013
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| Online Access: | 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.pdf |
| _version_ | 1848817495114252288 |
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| author | Nur Zahirah, Mohd Ali |
| author_facet | Nur Zahirah, Mohd Ali |
| author_sort | Nur Zahirah, Mohd Ali |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T01:22:40Z |
| format | Thesis |
| id | ump-4919 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T01:22:40Z |
| publishDate | 2013 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-49192023-02-16T01:15:52Z 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 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.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 (Contributors, Thesis advisor: Zulkeflee, Khalidin). |
| 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 |
| title | 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_short | 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 |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | 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.pdf |