Voltage stability prediction using artificial neural network for IEEE 69-bus and 30-bus / Nur Adha Hanif Mat Rahim

Mat Rahim, Nur Adha Hanif (2014) Voltage stability prediction using artificial neural network for IEEE 69-bus and 30-bus / Nur Adha Hanif Mat Rahim. Degree thesis, Universiti Teknologi MARA (UiTM).

Abstract

This project discusses the voltage stability prediction of a power system using Artificial Neural Network (ANN). Voltage instability is the one of the causes for a power system to breakdown. This incident is caused by severe low voltage condition which leads to blackouts to all system. The voltage stability prediction is essential in power system planning in order to prevent voltage collapse due to instability of voltage in power system. The voltage stability for each bus 1s determined by Voltage Stability Index (VSI). The value obtain from the VSI can determine the voltage stability for each bus in the system. There are two types of bus that will be use to determine the voltage stability prediction in power system which is 69-bus and 30-bus. A comparative study was conducted with Artificial Neural Network (ANN)-based prediction.

Metadata

Item Type: Thesis (Degree)
Creators:
Creators
Email / ID Num.
Mat Rahim, Nur Adha Hanif
UNSPECIFIED
Contributors:
Contribution
Name
Email / ID Num.
Thesis advisor
Mohd. Arshad, Pauziah
UNSPECIFIED
Subjects: Q Science > QA Mathematics > Instruments and machines > Electronic Computers. Computer Science > Neural networks (Computer science)
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Electrical Engineering
Programme: Bachelor of Electrical Engineering (Hons)
Keywords: Artificial neural network (ANN), voltage instability, voltage stability prediction
Date: 2014
URI: https://ir.uitm.edu.my/id/eprint/78122
Edit Item
Edit Item

Download

[thumbnail of 78122.pdf] Text
78122.pdf

Download (125kB)

Digital Copy

Digital (fulltext) is available at:

Physical Copy

Physical status and holdings:
Item Status:
On Shelf

ID Number

78122

Indexing

Statistic

Statistic details