Voltage stability index prediction by using genetics algorithm-based machine learning (GBML) technique / Zainab Mohd Ghazali

Mohd Ghazali, Zainab (2007) Voltage stability index prediction by using genetics algorithm-based machine learning (GBML) technique / Zainab Mohd Ghazali. Degree thesis, Universiti Teknologi MARA (UiTM).

Abstract

Voltage stability is the ability of a power system to maintain acceptable voltage at all buses in the system under normal conditions and after being subjected to a disturbance. Itis important to keep the power system stable to avoid network failure or collapse. Recently years, it is reported that many major network failure occurs due to voltage instability. In case of that, voltage stability has become one of the major concerns in planning and operating of electrical power system. This problem has inspired researchers to seek for the solutions. One of effective way is by applying early prediction or on-line prediction of system's stability. This thesis has come up with new technique to predict the voltage stability condition of a power system. The proposed technique is using Genetic Algorithms-Based Machine Learning (GBML) to predict the voltage stability index. However researchers keep searching for most effective technique to predict the stability index.

Metadata

Item Type: Thesis (Degree)
Creators:
Creators
Email / ID Num.
Mohd Ghazali, Zainab
UNSPECIFIED
Contributors:
Contribution
Name
Email / ID Num.
Thesis advisor
Abdul Rahman, Titik Khawa
UNSPECIFIED
Subjects: Z Bibliography. Library Science. Information Resources > Information in specific formats or media > Electronic information resources > Computer network resources
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Electrical Engineering
Programme: Bachelor of Electrical Engineering (Hons.)
Keywords: Voltage stability, power system, network failure
Date: 2007
URI: https://ir.uitm.edu.my/id/eprint/84572
Edit Item
Edit Item

Download

[thumbnail of 84572.pdf] Text
84572.pdf

Download (5MB)

Digital Copy

Digital (fulltext) is available at:

Physical Copy

Physical status and holdings:
Item Status:
On Shelf

ID Number

84572

Indexing

Statistic

Statistic details