Optimal distribution system reconfiguration incorporating dg and variable load profile using artificial neural network / Hesham Hanie Youssef

Optimal network reconfiguration is a common method used in distribution systems to ensure minimum power losses are always attained. This is very important task for achieving cost effective operation. Due to varying load demands, conventional network reconfiguration techniques have to be repeated whe...

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Main Author: Hesham Hanie, Youssef
Format: Thesis
Published: 2020
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Online Access:http://studentsrepo.um.edu.my/13178/1/Hesham_Hanie_Youssef.jpg
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spelling my.um.stud.131782022-04-26T18:37:28Z Optimal distribution system reconfiguration incorporating dg and variable load profile using artificial neural network / Hesham Hanie Youssef Hesham Hanie, Youssef TA Engineering (General). Civil engineering (General) Optimal network reconfiguration is a common method used in distribution systems to ensure minimum power losses are always attained. This is very important task for achieving cost effective operation. Due to varying load demands, conventional network reconfiguration techniques have to be repeated whenever system loading changes to find a new configuration that has minimum power losses. This task is time consuming and ineffective approach for a real time application. Therefore, this research proposes an Artificial Neural Network (ANN) technique for optimal distribution network reconfiguration to overcome long processing time, mainly in load variation case. The proposed method involves; (1) Implement optimal network reconfiguration with variable load profile and DG generation using meta-heuristic techniques for ANN modelling (2) Designing an ANN model for optimal network reconfiguration (3) Train the proposed ANN model on the generated data using different split ratios for optimal network reconfiguration. The applied meta-heuristic techniques in this work are Evolutionary programming (EP) and Particle swarm optimization (PSO). To evaluate the performance of the proposed ANN method, simulation conducted on MATLAB were conducted on IEEE 16-bus, IEEE 33-bus and IEEE 69-bus system. The proposed network reconfiguration based on ANN significantly reduces the computational time to find the optimal solution while avoiding additional calculations. The results show that the proposed ANN technique is more than 90% faster than the conventional methods for varying load profile. 2020-11 Thesis NonPeerReviewed application/pdf http://studentsrepo.um.edu.my/13178/1/Hesham_Hanie_Youssef.jpg application/pdf http://studentsrepo.um.edu.my/13178/8/hesham.pdf Hesham Hanie, Youssef (2020) Optimal distribution system reconfiguration incorporating dg and variable load profile using artificial neural network / Hesham Hanie Youssef. Masters thesis, Universiti Malaya. http://studentsrepo.um.edu.my/13178/
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Student Repository
url_provider http://studentsrepo.um.edu.my/
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Hesham Hanie, Youssef
Optimal distribution system reconfiguration incorporating dg and variable load profile using artificial neural network / Hesham Hanie Youssef
description Optimal network reconfiguration is a common method used in distribution systems to ensure minimum power losses are always attained. This is very important task for achieving cost effective operation. Due to varying load demands, conventional network reconfiguration techniques have to be repeated whenever system loading changes to find a new configuration that has minimum power losses. This task is time consuming and ineffective approach for a real time application. Therefore, this research proposes an Artificial Neural Network (ANN) technique for optimal distribution network reconfiguration to overcome long processing time, mainly in load variation case. The proposed method involves; (1) Implement optimal network reconfiguration with variable load profile and DG generation using meta-heuristic techniques for ANN modelling (2) Designing an ANN model for optimal network reconfiguration (3) Train the proposed ANN model on the generated data using different split ratios for optimal network reconfiguration. The applied meta-heuristic techniques in this work are Evolutionary programming (EP) and Particle swarm optimization (PSO). To evaluate the performance of the proposed ANN method, simulation conducted on MATLAB were conducted on IEEE 16-bus, IEEE 33-bus and IEEE 69-bus system. The proposed network reconfiguration based on ANN significantly reduces the computational time to find the optimal solution while avoiding additional calculations. The results show that the proposed ANN technique is more than 90% faster than the conventional methods for varying load profile.
format Thesis
author Hesham Hanie, Youssef
author_facet Hesham Hanie, Youssef
author_sort Hesham Hanie, Youssef
title Optimal distribution system reconfiguration incorporating dg and variable load profile using artificial neural network / Hesham Hanie Youssef
title_short Optimal distribution system reconfiguration incorporating dg and variable load profile using artificial neural network / Hesham Hanie Youssef
title_full Optimal distribution system reconfiguration incorporating dg and variable load profile using artificial neural network / Hesham Hanie Youssef
title_fullStr Optimal distribution system reconfiguration incorporating dg and variable load profile using artificial neural network / Hesham Hanie Youssef
title_full_unstemmed Optimal distribution system reconfiguration incorporating dg and variable load profile using artificial neural network / Hesham Hanie Youssef
title_sort optimal distribution system reconfiguration incorporating dg and variable load profile using artificial neural network / hesham hanie youssef
publishDate 2020
url http://studentsrepo.um.edu.my/13178/1/Hesham_Hanie_Youssef.jpg
http://studentsrepo.um.edu.my/13178/8/hesham.pdf
http://studentsrepo.um.edu.my/13178/
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score 13.211869