Reactive power dispatch incooperating voltage stability improvement using artificial neural network / Rasida Norjali

This thesis presents the development of an Artificial Neural Network (ANN) based technique for reactive power dispatch that aims to improve voltage stability of a power system. In this study, a multi-layer feed forward ANN with error back propagation algorithm was used. The proposed method was teste...

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主要作者: Norjali, Rasida
格式: Thesis
語言:English
出版: 1995
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spelling my-uitm-ir.687452024-10-03T07:25:02Z Reactive power dispatch incooperating voltage stability improvement using artificial neural network / Rasida Norjali 1995 Norjali, Rasida Neural networks (Computer science) Dynamoelectric machinery and auxiliaries.Including generators, motors, transformers This thesis presents the development of an Artificial Neural Network (ANN) based technique for reactive power dispatch that aims to improve voltage stability of a power system. In this study, a multi-layer feed forward ANN with error back propagation algorithm was used. The proposed method was tested on two models, which are the 6 bus, and IEEE 14 bus interconnected systems. The testing and training data were generated by Fast Decoupled Load Flow method and the voltage stability at a load bus was measured by evaluating the voltage stability index, i.e. L-factor, developed in reference [1]. The results show that the ANN could be used to determine the value of reactive power and to predict voltage stability level for power system, since they are in close agreement with the calculating results. 1995 Thesis https://ir.uitm.edu.my/id/eprint/68745/ https://ir.uitm.edu.my/id/eprint/68745/1/68745.pdf text en public degree Universiti Teknologi MARA (UiTM) Faculty of Electrical Engineering Abdul Rahman, Titik Khawa
institution Universiti Teknologi MARA
collection UiTM Institutional Repository
language English
advisor Abdul Rahman, Titik Khawa
topic Neural networks (Computer science)
Neural networks (Computer science)
spellingShingle Neural networks (Computer science)
Neural networks (Computer science)
Norjali, Rasida
Reactive power dispatch incooperating voltage stability improvement using artificial neural network / Rasida Norjali
description This thesis presents the development of an Artificial Neural Network (ANN) based technique for reactive power dispatch that aims to improve voltage stability of a power system. In this study, a multi-layer feed forward ANN with error back propagation algorithm was used. The proposed method was tested on two models, which are the 6 bus, and IEEE 14 bus interconnected systems. The testing and training data were generated by Fast Decoupled Load Flow method and the voltage stability at a load bus was measured by evaluating the voltage stability index, i.e. L-factor, developed in reference [1]. The results show that the ANN could be used to determine the value of reactive power and to predict voltage stability level for power system, since they are in close agreement with the calculating results.
format Thesis
qualification_level Bachelor degree
author Norjali, Rasida
author_facet Norjali, Rasida
author_sort Norjali, Rasida
title Reactive power dispatch incooperating voltage stability improvement using artificial neural network / Rasida Norjali
title_short Reactive power dispatch incooperating voltage stability improvement using artificial neural network / Rasida Norjali
title_full Reactive power dispatch incooperating voltage stability improvement using artificial neural network / Rasida Norjali
title_fullStr Reactive power dispatch incooperating voltage stability improvement using artificial neural network / Rasida Norjali
title_full_unstemmed Reactive power dispatch incooperating voltage stability improvement using artificial neural network / Rasida Norjali
title_sort reactive power dispatch incooperating voltage stability improvement using artificial neural network / rasida norjali
granting_institution Universiti Teknologi MARA (UiTM)
granting_department Faculty of Electrical Engineering
publishDate 1995
url https://ir.uitm.edu.my/id/eprint/68745/1/68745.pdf
_version_ 1818587919462432768