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

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.

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Adaptive Embedded Clonal Evolutionary Programming (AECEP) for optimal Distributed Generation (DG) location and sizing in a distribution system (Table of content).pdf - Accepted Version

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Adaptive Embedded Clonal Evolutionary Programming (AECEP) for optimal Distributed Generation (DG) location and sizing in a distribution system (Abstract).pdf - Accepted Version

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Adaptive Embedded Clonal Evolutionary Programming (AECEP) for optimal Distributed Generation (DG) location and sizing in a distribution system (References).pdf - Accepted Version

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Abstract

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.

Item Type: Thesis (Masters)
Additional Information: Thesis (Master of Engineering (Electrical)) -- Universiti Malaysia Pahang – 2013
Uncontrolled Keywords: Distributed generation of electric power; Adaptive Embedded Clonal Evolutionary Programming (AECEP)
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Electrical & Electronic Engineering
Depositing User: Nik Ahmad Nasyrun Nik Abd Malik
Date Deposited: 03 Sep 2014 02:16
Last Modified: 03 Apr 2017 04:06
URI: http://umpir.ump.edu.my/id/eprint/4919
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