Application of artificial neural network for voltage stability monitoring / Valerian Shem

This project is about monitoring the voltage stability of a system bus. Voltage stability problem has been one of the major concerns for electric utilities as a result of system heavy loading and needs to be solved. A 6-system bus is used as input variables, which consists of real power value (PL...

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Main Author: Shem, Valerian
Format: Thesis
Published: 2003
Online Access:http://ir.uitm.edu.my/id/eprint/1003/
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spelling my.uitm.ir.10032017-04-19T04:13:33Z http://ir.uitm.edu.my/id/eprint/1003/ Application of artificial neural network for voltage stability monitoring / Valerian Shem Shem, Valerian This project is about monitoring the voltage stability of a system bus. Voltage stability problem has been one of the major concerns for electric utilities as a result of system heavy loading and needs to be solved. A 6-system bus is used as input variables, which consists of real power value (PL) and reactive power (QL). This system analyzes the concerned variables and shows the stabilized value for load power (L) as the output. To solve this problem, this simulation implements the Artificial Neural Network approach using both standard back-propagation technique and hybrid technique (standard backpropagation and genetic algorithm (GA)). The latter technique requires GA to find the optimal value for each weight of the neural network. A comparative study is conducted to measure the performance of the neural network using different types of parameters. By completing this project, we should be able to have an idea on how to monitor voltage stability from any system bus and to make machine learns like human does. 2003 Thesis NonPeerReviewed Shem, Valerian (2003) Application of artificial neural network for voltage stability monitoring / Valerian Shem. Degree thesis, Universiti Teknologi MARA.
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
description This project is about monitoring the voltage stability of a system bus. Voltage stability problem has been one of the major concerns for electric utilities as a result of system heavy loading and needs to be solved. A 6-system bus is used as input variables, which consists of real power value (PL) and reactive power (QL). This system analyzes the concerned variables and shows the stabilized value for load power (L) as the output. To solve this problem, this simulation implements the Artificial Neural Network approach using both standard back-propagation technique and hybrid technique (standard backpropagation and genetic algorithm (GA)). The latter technique requires GA to find the optimal value for each weight of the neural network. A comparative study is conducted to measure the performance of the neural network using different types of parameters. By completing this project, we should be able to have an idea on how to monitor voltage stability from any system bus and to make machine learns like human does.
format Thesis
author Shem, Valerian
spellingShingle Shem, Valerian
Application of artificial neural network for voltage stability monitoring / Valerian Shem
author_facet Shem, Valerian
author_sort Shem, Valerian
title Application of artificial neural network for voltage stability monitoring / Valerian Shem
title_short Application of artificial neural network for voltage stability monitoring / Valerian Shem
title_full Application of artificial neural network for voltage stability monitoring / Valerian Shem
title_fullStr Application of artificial neural network for voltage stability monitoring / Valerian Shem
title_full_unstemmed Application of artificial neural network for voltage stability monitoring / Valerian Shem
title_sort application of artificial neural network for voltage stability monitoring / valerian shem
publishDate 2003
url http://ir.uitm.edu.my/id/eprint/1003/
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score 13.211869